VAWKUM Transactions on Computer Sciences
The VAWKUM Transactions on Computer Sciences (VTCS) is a biannual, peer-reviewed journal established in 2013 to promote high-quality research across all domains of computer science and its emerging applications. The journal aims to advance scientific knowledge by publishing innovative, original, and impactful research that supports technological progress and societal benefit. Its objectives include ensuring research excellence through rigorous peer review, fostering innovation, encouraging global knowledge dissemination, and strengthening collaboration between academia and industry. VTCS welcomes original research articles and expert reviews covering Artificial Intelligence, Machine Learning, Data Science, Bioinformatics, Software Engineering, Algorithms, Distributed and Cloud Computing, Cyber-Physical Systems, Cybersecurity, IoT, Green ICT, and other interdisciplinary technologies. The journal adheres to COPE and CORE ethical standards to maintain transparency and integrity in scholarly publishing. ISSN: 2308-8168 (Online), 2411-6335 (Print) Editor-in-Chief: Prof. Sher Afzal Khan, Abdul Wali Khan University Mardan <span class="csspropertycolor" style="box-sizing: inherit; color: red; font-family: Consolas, Menlo, 'courier new', monospace; font-size: 15px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">Important Links REGISTER SUBMISSION EDITORIAL TEAM LATEX TEMPLATE POLICY DOCUMENT HEC Category Y Worldcat Pastic.gov.pk JournalTOCS Academic Journals Database Sciences Po Sudoc Journal Guide ISSN + ROAD Ulrich’s Periodicals Directory/ProQuest Oalib Open Access Library <a href="http://sfx.princeton.edu:9003/sfx_pul/az?param_textSearchType_save=startsWith&param_perform_save=searchTitle&param_letter_group_script_save=&param_chinese_checkbox_save=0&param_starts_with_browse_save=0&param_sid_save=b2d32284a5f202b8c792b440b6535c2f&param_lang_save=eng&param_type_save=textSearch&param_chinese_checkbox_type_save=Pinyin&param_current_view_save=table&param_letter_group_save=&param_langcode_save=en&param_jumpToPage_save=&param_services2filter_save=getFullTxt&param_pattern_save=vawkum&param_type_value=textSearch&param_jumpToPage_value=&param_pattern_value=VAWKUM+Transaction+on+Computer+Sciences&param_textSearchType_value=startsWith&param_starts_with_browse_value=0&not_first_load_indicator=1&param_ui_control_scripts_value=&param_chinese_checkbox_value=0">Princeton University USA Library World of Periodicals BASE: Bielefeld Academic Search Engine COnnecting REpositories (CORE) Dimensions Research Bible
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Hybrid Deep Learning Models for Criminal Emotion Detection and Risk Prediction in Women's Safety
Crime against women is a persistent global issue, affecting women across all socio-economic backgrounds in various forms. Traditional crime prediction methods are typically reactive, being implemented only after a crime has occurred. This study proposes a hybrid deep-learning framework for detecting criminal emotions and estimating the risk of crimes against women. The model integrates several deep learning architectures, including CNN, Bi-LSTM, Bi-GRU, LSTM, and MLP. Additionally, two hybrid models, Bi-GRU + LSTM and CNN + Bi-LSTM, are introduced to enhance prediction performance. A dataset comprising 12465 real-world crime instances, including cases of domestic violence, sexual harassment, and acid attacks, was used for training and evaluation. The CNN + Bi-LSTM hybrid model achieved the highest accuracy at 98.12%, followed by the Bi-GRU + LSTM model with 97.65%. The individual model achieved performances were Bi-GRU (96.7%), CNN (94%), LSTM (96.3%), and Bi-LSTM (96.9%). The results demonstrate that the proposed model effectively captures both spatial features and temporal dependencies in crime-related data, offering promising capabilities for detecting emotional cues and assessing risk. Despite challenges such as data imbalance and privacy concerns, this study underscores the potential of AI-enabled solutions to enhance women’s safety and empower them with practical tools for crime prevention.
Copyright (c) 2026 VAWKUM Transactions on Computer Sciences
A Digital Twin-Inspired Hybrid Variational Quantum Reinforcement Learning Framework for Heart Disease Risk Prediction
Despite many advancements in the medicine domain heart disease mortality rates continue climb that is signailing an urgent need for more advanced risk models. In this work we have tackled this challenge by introducing a hybrid framework that combines digital twin modeling with variational quantum reinforcement learning(VQRL). By developing patient-specific digital twins from raw patient clinical data, we are able to simulate unique health states within a specilized reinforcement learning environment. To optimize the diganostic decision-making we leveraged a variational quantum network. This setup utilizes a hybrid quantum-classical optimization loop, making it well suited for the constraints of modern near-term quantum hardware. When put to the test on the "Cleveland Heart Disease dataset" the proposed framework delivered impressive results: achieving a 94.2% accuracy, and an MCC of 0.82. Beyond just high scores in precision 90.4%, and recall 92.2%. Our framework demonstrated remarkable stability and an ability to generalize during the training phase. Ultimately, this fusion of digital twins and quantum computing offers a fresh, scalable path forward for clinical decision making.
AI Based Makeup Recommendation System: A Suitable AI Solution for Women
This paper describes the development of an AI-driven makeup recommendation application and how it was developed with the use of OpenCV and dlib to process the data on the back side and machine learning algorithms to execute a recommendation, and Flitter in order to operate the front-facing camera. The application gives unique recommendations to users depending on their face structure and their interests, to transform the way individuals shop makeup. The tool is more accessible to the art of cosmetics and utilizes AI to enable users to use it and enhance their natural beauty with confidence. It also uses sophisticated algorithms to detect facial characteristics like color of skin, shape of eyes, and color of lips to recommend appropriate cosmetic products and methods. This creative method is not only very simplifying to the make up process it also promotes creativity and self expression.
IoT-Enabled Machine Learning Framework for Precision Agriculture: Achieving Near-Perfect Crop Yield Prediction in Pakistan's Diverse Agro-Climatic Zones
This study introduces an integrated IoT-based framework for machine learning of agricultural crop yield prediction without rivaling in the accuracy levels. After an intensive survey of 16 machine learning algorithms on multiyear data from the principle agricultural zones of Pakistan, we show that ensemble models are capable of achieving close to perfect prediction accuracy. The Random Forest method had excellent performance with R² = 0.99, RMSE = 538.37 kg/ha and MAPE = 0.2944% which are a new benchmark in the field of agricultural yield prediction. Analysis of 400 agricultural records across seven districts showed evident outperformance of the tree-based ensemble methods against conventional ones. The proposed end-to-end framework integrates IoT sensor networks and cutting-edge ML models to monitor in real-time, and with high accuracy, the yields of major crops viz. wheat, cotton, sugarcane and rice. Here, we present an important leapfrogging in precision agriculture technology development that provides disruptive potential to the agricultural planning, resource allocation and food security improvement of developing countries by fusing IoT and machine learning.
Real-Time Detection of Credit Card Fraud in Imbalanced Datasets Using Machine Learning and Ensemble Approaches
The increase in digital payments has become more prominent and challenging in this short period of time and poses a great threat to both banks and customers. The traditional rule-based fraud detection systems cannot handle the evolving attack patterns, large volumes of data, and significantly biased transaction datasets. This work proposed a comprehensive machine learning-based fraud detection system that integrates advanced data preparation, eﬃcient resampling approaches, and a wide variety of supervised learning algorithms to overcome these challenges. It compares seven machine learning models using Random Under-Sampling and Synthetic Minority Over-Sampling Technique. All the models went through the same preprocessing pipeline, which was used to enhance the reliability of the model through feature scaling and anomaly control. This paper used the Latency-Aware Accuracy Index, a metric that considers latency and outperforms the traditional metrics. It provides empirical evidence that boosting-based models such as LightGBM, CatBoost and XGBoost using SMOTE outperform classical classifiers with respect to prediction performance and inference time eﬃciency. It illustrates that accuracy alone is not suﬃcient for practical implementation; latency plays an equally important role in minimizing financial loss by ensuring a smooth customer experience. The results indicate that ensemble learning models much exceed the performance of standard linear classifiers in balanced datasets. XGBoost demonstrated superior performance with SMOTE oversampling, getting an accuracy of 0.9898, an F1-score of 0.9898, and a LAAI of 0.9889. This shows that it is strong across different balancing approaches. On the other hand, Logistic Regression and Naïve Bayes always got accuracy values around 0.50, which shows that they could not find the complex non-linear patterns that are typical of fraud detection.
Buddy Finder: A Privacy-Preserving Clustering Approach for Like-Minded Social Connections
Applications that connect users with like-minded individuals while safeguarding their privacy are in high demand due to the quick growth of digital platforms for social interaction. Buddy Finder, a method for locating potential friends based on shared interests and physical proximity, is presented in this work. In contrast to conventional social networking or dating websites, the system prioritizes user privacy by displaying only a small amount of information during the first stages of interaction. Users are grouped based on their shared interests using clustering techniques, with K Means serving as the main algorithm. Participants in these groups may start conversations and gradually decide what more details to disclose. For use with a variety of data sets, the clustering code is effective, scalable, and adaptable. Additionally, the system is intended to facilitate meetings inside the local community as well as meetings that take place locally. In addition to outlining the overall architecture design, algorithmic design, and implementation difficulties, this study provides recommendations for future advancements in personalization and privacy recommendation systems.
Copyright (c) 2025 VAWKUM Transactions on Computer Sciences
The introduction of Artificial Intelligence in Medical Education: A Narrative Review of Implementation Practice, Evaluation, and Methodological Barriers
Artificial Intelligence (AI) is changing the face of medical education at an unprecedented rate, providing new opportunities for personalized learning, automated assessment, intelligent tutoring, clinical simulation, and data-driven decision support. Although this adoption is gaining momentum, the use of AI in medical curricula is still disjointed, and there is considerable diversity in the practices and approaches of implementation, assessment, and institutional preparedness. This is a narrative review of the existing data on the applications of AI tools in medical training, their educational utility, practical application, and effects on student learning. The review also presents methodological challenges, such as the inconsistent evaluation schemes, insufficient empirical validation, ethical issues, problems with the curriculum, readiness by faculty readiness, and unclear structural constraints, which are obstacles to scalable and standardized integration. This review offers a clear basis to support the evolution of AI-enhanced medical education by the educator, policymaker, and institutions by mapping the existing practices, defining gaps, and identifying recommendations to be implemented and researched in the future.
A Next Generation Real Time Frame work for Drone Video Decoding Leveraging IoT-Enabled Communication Network
The new data processing systems required reliable. Fast and low latency data services for smart cities operations and unmanned vehicle systems for quick and fast decisions. At present the low latency and speed data for video decoding for real time are required for intelligent decisions. In this research work we present video decoding model based on for decoding data in real time. This proposed model is based on hybrid Edge-Fog-Cloud orchestration layer that perform decoding task in real time according to the network congestion and this technique ensure data integrity, traceable task distribution and protect the data from tempering by using IoT backbone secured by blockchain technology. To reduce the risk of end-to-end latency and packet loss in worst conditions a novel Temporal-Spatial Predictive Decoding (TSPD) method is used. The AI model deep reinforcement learning is used for fast decisions. After analyzing it can be concluded that a 47.8% improvement in decoding throughput, a 62% reduction in jitter and 38% improvement QoE. This shows satisfactory performance from proposed model. By optimizing energy-latency and combining decentralized system with IoT-driven communication for autonomous aerial system can be used in future 6G network.
Investigation and Analysis of the Role of Leaf’s Vein Features for Automated Classification of Plants
Several researchers have proposed various methods to classify plants. Veins pattern of leaf contains imperative information for plants identification regardless its complex modality. This research describes a methodology for classifying different types of plants using various features i.e. leaf shape, texture, color and newly proposed vein features. The proposed algorithm comprises of three distinct stages that are preprocessing, feature extraction and classification. The characteristics of plant can be determined from leaf structures. The classification approach used in this research based on plant leaves as leave belongs to different plants has different characteristics. The used dataset for the proposed approach is a flavia database that contains 32 kinds of plant leaves. It is observed from experimental results that the classification accuracy of 97.25% was achieved using a Random Forest classifier when a combination of shape, HSV color moment, texture and venation features are used.
From Machine Learning to Transformers: A Comparative Study on Emotion Classification of Tweets
In this era of social media, it is essential that policymakers, businessmen, and researchers focus on understanding the sentiments of the people through their expressions on social media. An analytical comparison of several well-known machine learning (ML) algorithms along with Transformer-based models has been exploited in this study to determine the emotions and sentiments of people through their tweets. ML algorithms included in the study consider classical models—Random Forest, SVM, ID3, Naive Bayes, KNN, MLP—and transformer models, namely BERT-Base and BERT-Large. These algorithms have been applied to a carefully curated dataset of tweets that have been labeled for their emotion, such as joy, fear, anger, and sadness. Evaluation metrics, including precision, accuracy, kappa score, F1 score, and recall, have been used to compare and analyze the performance of each algorithm. The findings of this paper reveal that both Random Forest and SVM showed the best accuracy, at 79.4% and 79.1% while Random Forest slightly outperformed SVM in accuracy. In Transformers, BERT-Base outperforms not only BERT-Large but also the performance of all ML algorithms with an accuracy of 86.3%. The proposed study offers important insights for future applications and studies in the area of emotion classification. It also highlights the importance of choosing a suitable algorithm for tasks related to text categorization.
Interpretable Physics-Informed Neural Network for Reliable Humidity Forecasting in Contrasting Climates of Sindh.
Reliable forecasting of humidity is fundamental to effective climate monitoring, agricultural scheduling, and public health planning. Traditional machine learning frameworks, however, struggle to mirror the largely nonlinear climatological processes governing moisture distribution and rarely satisfy governing physical laws. This work introduces a physics-informed neural network (PINN) explicitly tailored to forecast daily mean humidity, embedding thermodynamic principles and smaller-scale climatological knowledge to bolster generalization and interpretability. The analysis is based on a comprehensive dataset of meteorological measurements from three climatically contrasting districts in Sindh, encompassing temperature, wind velocity, cumulative rainfall, and derived interaction terms. The PINN topology augments the mean squared error loss with a physically grounded rainfall–humidity relationship, thereby constraining the output to physically realizable humidity profiles. To decode the model’s inner operation, we employed SHAP and LIME, providing affected stakeholders with a transparent rationale for predictions. Comparative benchmarks reveal that the physics-informed architecture exceeds the forecast skill of conventional regression, ensemble methods, and standard feed-forward neural networks, securing R² metrics greater than 0.99 and uniformly low mean squared error across all three locales. Sensitivity assessments based on SHAP and LIME indicate that seasonal modulation and the interaction between wind and humidity are the predominant drivers of the forecasts, whereas the contribution of rainfall-derived predictors remains negligible. The combination of PINN with contemporary interpretability methodologies yields a forecasting paradigm for meteorology that is both transparent and governed by established physical laws.
Efficient Java Bytecode Analysis and Sequence Identification for Embedded Systems
Conventional tools and techniques of bytecode profiling require a lot of resources to generate bytecode sequences. Embedded devices have limited resources, and using conventional profilers to analyze bytecode execution traces and generate basic blocks is time-consuming. However, it often negates the benefits of creating Super instructions in some scenarios. Furthermore, these techniques provide complete basic blocks to be used as Super instructions. This paper proposes a simple and high-performance technique based on partial control flow analysis to identify basic blocks by locating control transfer instructions and their targets. Subsequently, the proposed approach extracts repeating patterns within the basic blocks to determine the most frequently repeated sequences to maximize the benefit from the super instructions. The SPECjvm 2008 benchmark suite and a custom bubble sort example were used in this study for the prototype implementation of the proposed framework. The prototype implementation of the technique identified the sequences of length 2 to 50 from the basic blocks, and then their frequency, ranging from 2 to 50, was extracted. It was observed that most basic blocks consist of 2 to 10 instructions in the actual code. The repeating sequences within basic blocks give better results compared to full basic blocks used as a super instruction. This approach enhances the execution performance and reduces the storage requirements of Java bytecode in embedded devices.
Enhanced Malicious Website Detection Using Machine and Deep Learning Techniques
Malicious websites currently pose an escalating security threat that leads to malware contaminations and leaking of sensitive data. The customary detection methods generate numerous incorrect alarms while struggling to identify optimized security risks, so better detection techniques become essential. A dependable detection system for malicious websites is proposed, which utilizes AdaBoost and XGBoost algorithms along with Convolutional Neural Networks (CNN) by integrating machine learning techniques. The four-phased framework as suggested includes (1) Data Acquisition and Initial Analysis, which makes use of a dataset available on Kaggle to realize patterns; (2) Data Cleaning, Normalization, Segmentation, and Model Training processes, which clean, normalize, and segment the data in preparation for optimal model training; (3) Detection and Classification Evaluation, which checks against performance metrics such as precision, recall, F1-score, and accuracy; and (4) Comparative Study and Outcome Comparison checks against relevant literature and reports on CNN performing better compared to conventional techniques. The CNN model demonstrated successful accuracy performance at 96.58% during malicious URL identification processes with reduced occurrence of wrong positive detections. The improvement of this research lies in the introduction of the real-time contextual augmentation within the detecting architecture to enable the adaptive learned malicious URL detector with reduced instances of false alarm and greater flexibility towards new cyber vulnerability. Practical applications for malicious website detection can utilize advanced algorithms backed by the research findings, which provide guidance for future research. This research contributes by providing a robust comparative framework that establishes CNN as the most effective classifier for malicious website detection, reduces false alarms, and strengthens practical cybersecurity applications while offering guidance for future research. The research contributes by providing a robust comparative framework that establishes CNN as the most effective classifier for malicious website detection, reduces false alarms, and strengthens practical cybersecurity applications while offering guidance for future research.
LIME-based Explainable AI classifier to detect COVID-19 pandemic through X-ray images
The term “black box” is used in the field of artificial intelligence to describe the lack of transparency between the model and its users. This work proposes the Explainable AI classifier, a major model for detecting COVID-19 using X-ray pictures, a topic that is at the cutting edge of research in the area of medical image analysis. The purpose of this research is to look at the process by which a model diagnoses a disease based on X-ray pictures. Create a COVID-19 radiography dataset, which includes evaluated lung images of 3616 COVID-19-positive cases and 10192 normal cases that can be accessed by the public. Which features of the image influence the results are identified with the help of the LIME method. The superpixels are categorized by the model and then shown with the help of the LimeImageExplainer function. It's very apparent how the model arrives at its results. With 97% accuracy, 94% precision, and 99.9% recall, the proposed approach exceeds the state-of-the-art model.
Operability and Policy Implementation of Blockchain Technology in Distance Learning: A Survey
Nowadays, due to the advancement of technology, most educational institutions and organizations have focused their attention on e-learning education systems, especially since the COVID-19 pandemic. This is accelerating the use of technology in the education system. However, the organizations also face many challenges in implementing this new mode of education. The existing e- learning systems have some limitations, such as user interactivity, system interoperability, and information Security. Currently, there is no secure and privacy control model implemented that protects the personal learning data, such as learning logs in e-learning systems, and the authenticity of academic credentials. Another challenge in the current e-learning system is verifying the authenticity of academic credentials. Some of the other challenges that affect the distance learning system are fraud detection, decentralized classrooms, and ensuring transparency of scholarships. There are many proposed solutions to tackle these problems, such as client-server architecture, cloud, and big data implementation. However, Blockchain technology can provide a solution to these challenges more effectively. This study is based on the use of blockchain in an e-learning system and aims to analyze the operability and policy implementation of blockchain in the education system. To address the above problems, a conceptual model for e-learning systems is proposed by adopting blockchain technology in the education system to create a trusted online accreditation system globally, which reduces the development cost, standardizes infrastructure, and achieves a higher degree of interoperability.
English to Urdu Neural Machine Translation using Transformer with Averaged Word Embeddings
In this era of computers, the World Wide Web (WWW) content is vital for everyone. Most of the useful content on the web is in English. Machine translation from the web content into the national language of Pakistan, i.e. Urdu, has several applications such as Urdu text generation, language resources creation, language research and availability of knowledge to illiterate individuals who cannot comprehend English but know Urdu. We propose a novel English-to-Urdu machine translation model in this research work, based on the transformer model that exploits the average of three word embeddings. These three word embeddings are Urdu word2vec (skipgram-based), part-of-speech-ngram (POS-Ngram) embeddings, and POS-POS embeddings, both of which encode the rich morphological and morphosyntactic features of Urdu language inside the word embeddings. Experiments are performed using a manually compiled English-Urdu parallel corpus from OPUS corpora and Github. The proposed transformer-based approach is compared to fine-tuned Llama-3-8B, T5-small, Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM). The evaluation metrics used are BLEU and ROUGE-L scores. The results suggest that the proposed model outperforms T5-small, LSTM and Bi-LSTM by ≈2.15, 7.44 and 5.53 points respectively, in BLEU score and by ≈1.7, 2.5 and 4.12 points, respectively, in ROUGE-L score. The proposed model shows comparable performance to the fine-tuned Llama-3-8B.
Hybrid EfficientNet Models with Attention Mechanisms for Enhanced Bone Fracture Detection and Classification Using X-ray Images
Bone fractures are a major diagnostic problem that needs rapid and prompt diagnosis. This paper presents an improved Efficient Net-B03 model and a self-attention mechanism to capture the local and global features for detection and classification. Data were obtained on the Kaggle bone fracture dataset. The model improves focus on fine details of fractures produced with computational efficiency. The preprocessing methods were adopted to enhance the quality of X-ray images, and the data augmentation was applied, and transfer learning also enhanced performance. The Efficient Net-B03 and the Attention Mechanism were trained separately and subsequently combined to come up with the proposed Hybrid model. The proposed hybrid model and Efficient Net-B03 both reached an accuracy of 0.99. The classification reports show that the dataset was categorized into two classes: Class 0 (non-fracture) had a precision of 0.99, and a recall. With a 0.88, Class 1 (fracture) had a 0.95 precision, 1.00 is recall and F1-scores is 0.93 for Class 0 and 0.97 for Class 1, with support values of 372 and 925 instances, respectively. Overall, the proposed hybrid model achieved an accuracy range from 0.96 to 0.98 with cross-validation, and up to 0.99 accuracy without cross-validation. The proposed hybrid model will help in the radiology clinical setting.
Enhanced Vertebral Segmentation and Cobb angle Calculation using Advanced Instance Segmentation Techniques
Scoliosis, a spinal deformity affecting children and adolescents, is quantified using the Cobb angle. Traditional manual measurement by clinicians is time-consuming and subject to variability. This study introduces an automated method using the YOLACT++ instance segmentation model to detect vertebrae and calculate the Cobb angle on the SpineWeb dataset. By identifying the most tilted vertebrae using principal component analysis, our approach achieves a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.11%, outperforming previous segmentation-based methods. This demonstrates improved accuracy and reliability, with potential for clinical decision support. The source code and other details are available at github link. https://github.com/inzamam-ulhaq-collab/Cobb-angle-measurement-code.git
Security Vulnerabilities of Eclipse Application Programming Interfaces: An Empirical Analysis
The Eclipse framework distinguishes between stable public APIs and less stable internal APIs; however, there is no formal assurance that these interfaces have been comprehensively evaluated for security vulnerabilities. Furthermore, previous studies revealed that many developers are unaware of most vulnerabilities. Applications that use security vulnerable APIs risk failing and are prone to malicious attacks if vulnerabilities are not fixed. Security vulnerability discovery is a difficult process and takes at least 3 years to discover and fix them. As a result, developers who depend on affected APIs are frequently compelled either to address these vulnerabilities independently or to discontinue their use. This study aims to identify interfaces within the Eclipse framework that are free from known security vulnerabilities and to recommend them for safer adoption by application developers. To achieve this objective, an empirical investigation was conducted using the SonarQube static analysis tool across 28 major Eclipse releases. The analysis focused on determining the presence and distribution of vulnerability-free interfaces. The study resulted in a dataset comprising approximately 222K public APIs and 292K internal APIs that were found to be free of detected security issues. The findings reveal that, on average, about 91.4% of public APIs and 85.3% of internal APIs across the analyzed releases do not exhibit security vulnerabilities. Furthermore, the average time required to remediate identified vulnerabilities was estimated at 1,425 days. These results offer valuable guidance for both API providers and users by identifying more reliable interfaces and providing an indication of the effort required to resolve security issues. Additionally, the dataset of vulnerability-free interfaces has been made publicly accessible via GitHub to support the development of more secure software systems.
HAR-AttenNet: Multi-Head Transformer for Precise Human Activity Recognition Using Wearable Devices
Human Activity Recognition has become inevitable in wearable technology in healthcare and fitness tracking applications. Precise recognition of daily routine human activities using wearable sensors remains a challenging task due to sensor modality, placement, and environmental factors. Indeed, Machine Learning and Deep Learning models have become robust in human activity recognition, yet these face numerous challenges in precisely recognizing the daily human activities. One major challenge is the variability in sensors readings where the same activity may lead to different sensor readings, intra-class variability, as different people perform the same activity differently or use different devices. To address this, we propose a multi-head transformer model with a multi-head attention mechanism that explicitly handles the intra-class variability. We demonstrate that the multi-head Transformer model exhibits enhanced robustness and performs better even in the face of such variability, although variability is a major problem in the popular PAMAP2 dataset, since it directly impacts the performance of deep learning models.
An Efficient Fault Diagnostic Methodology with LWDA and DAGSVM for Industrial Processes
ince the advent of industry 4.0, many prevalent techniques and processes in the industry have been redesigned, leading to significant advancements in fault diagnosis methods. These methods can generally be categorized into three main categories: model-based, knowledge-based, and data-driven. Among these, data-driven methods are the most preferred due to the abundance of available data. The integration of machine learning into data-driven methods has become an active area of research. Many techniques with well-documented limitations and capabilities have been proposed in this context. However, when working with larger datasets, two critical challenges arise. First, datasets often contain features with high dimensionality, leading to feature redundancy. Second, larger datasets increase complexity, causing machine learning algorithms to perform poorly. To address these challenges, we present an efficient fault diagnostic methodology using LWDA and DAGSVM. The proposed methodology (i) preserves spatial relationships while reducing both dimensionality and data complexity, and (ii) achieves better classification accuracy for non-linear and overlapping data through optimized kernel approaches. We evaluate this methodology on a benchmark dataset, where experimental results demonstrate an accuracy of approximately 97.49%, outperforming existing approaches.
Predictive Job Category Classification Using Deep Learning and Transformer Approaches
In the dynamic employment environment, job trials where the type of job is predicted based on the job description is very essential in minimizing the challenges in the job market. This paper discusses how machine learning and deep learning can be used to enhance job type prediction by solving ambiguity in job descriptions. With the help of supervised learning, we process both structured and unstructured data to facilitate and improve the accuracy of categorization and suggest more qualified candidates to be hired. Comparative analysis of machine learning algorithms, such as Logistic Regression, Naive Bayes, Support Vector Machines, Random Forest, and Gradient Boosting, and deep learning models, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN), demonstrate that major progress has been made in precision, recall, and F1 scores of job matching. Random Forest has the best accuracy of 0.85, whereas GRU having a three-layer architecture has competitive results with an accuracy of 0.60, whereas BERT Large has the same accuracy as Random Forest of 0.85. These improvements are demonstrated on a heterogeneous dataset, enhancing the relevance of recommendations. Challenges such as ambiguous job descriptions and the rapidly changing labor market were identified as potential barriers. The study highlights the potential of machine learning to automate recruitment processes, reduce traditional CV screening costs, and improve job seeker experiences. This work pioneers adaptive job recommendation models with the potential to revolutionize global recruitment.
An ACID-BASE Analysis of NoSQL Database Structuring Models for Efficient Data Management
Due to the massive growth of data in the modern era, NoSQL databases have achieved significant popularity for their ability to scale and adapt, making them a favored option for managing extensive data in distributed systems or cloud databases. The primary goal of this research is to explore the ACID (Atomicity, Consistency, Isolation, Durability), BASE (Basically available, soft state, Eventual Consistency), and CAP (Consistency, Availability, and Partition tolerance) database structuring models to conduct a comparative analysis of ACID and BASE of twelve mostly used NoSQL databases. We also categorize each database with Brewer’s CAP theorem. This research also compares the functional and non-functional features of databases adhering to the discussed models and explores the variety of data stores from each of the four NoSQL categories (Document, Key-value, Column, and Graph). The research summarizes the suggestions, benefits, and challenges of using NoSQL databases based on their applicability to cloud-based environments.
Artificial Intelligence in Sustainable Smart Agriculture: Concepts, Applications, and Challenges
Artificial Intelligence (AI) has emerged as a transformative force in modern agriculture, revolutionizing traditional farming practices into smart agriculture ecosystems. This paper presents the ideas and uses of AI in smart agriculture, therefore highlighting its great influence on improving farming efficiency, sustainability, and production.Based on a number of layers that facilitate data collection, data analysis, and decision-making, in the farming processes, we propose in this paper an AI-based Internet of Things (IoT) platform of smart agriculture. There are also other AI-based technologies such as Machine Learning (ML), computer vision, and IoT integration, explored in this paper, that can give farmers the ability to access real-time data, predictive analytics, and autonomous decision-making power. We also discuss the ways AI would address some significant agricultural challenges, such as optimisation of resources, climate resilience, insect control and monitoring of crops. The paper explains the promising future of smart farming based on AI in ensuring sustainable farming and food security in the world.
Urdu-Punjabi Code Switched Sentiment Analysis Empowered by a Deep Learning Framework Integrating XLM-R, and GPT
Sentiment analysis is a procedure that uses computational methods, textual analysis, and natural language processing to derive significant insights from textual sources. Sentiment analysis detects and quantifies the attitudes, opinions, and emotional states that individuals convey through textual information. The majority of existing sentiment analysis work is centered on the English language, leaving low-resource languages largely underexplored. Performing sentiment analysis on low-resource languages is challenging due to the unavailability of extensive datasets and associated resources. To overcome the challenge of unavailability of datasets we proposed Large Urdu-Punjabi code switched Corpus for Sentiment Analysis (LUPCSA-25) comprises over 10,00,000 user reviews in Urdu and Punjabi (Shahmukhi). Urdu and Punjabi domain specialists enrolled in PhD provided additional annotations to the dataset. In this research, we examine how head-pruning strategies can enhance both the predictive accuracy and computational efficiency of transformer architectures—specifically XLM-R and GPT-2—for sentiment classification of Urdu–Punjabi code-switched text. After preprocessing the textual data, BERT embeddings are produced and subsequently passed to the proposed classification model for determining sentiment. The performance of the proposed classifier is assessed by comparing it with baseline classifiers. The results demonstrate that the proposed classifiers with head pruning technique surpass current state-of-the art models with a precision rate of 96.4%.
AGRITECH: A Smart System for Sustainable Farming
Conventional agriculture, which requires human labor and does not use any kind of mechanism, is proved to be very less efficient and cannot meet the growing food requirements of the world. The application of IoT as a means of implementing change toward precision agriculture is presented below. The following paper describes the design of a smart agricultural system using IoT devices, Raspberry Pi, and a set of sensors: soil moisture, humidity, gas, flame, and motion sensors to improve farming. High technologies like drone and image processing are used to check the health of the plant and increase the production during the farming process.The smart system substantially enhances the productivity and utilization of resources by making smart choices. A mobile application can expand the system’s capabilities, data protection, low energy consumption and high reliability. This specific use of IoT makes farming more efficient to enable farmers to grow more crops and make more profits as a positive step towards sustainable farming. From the research, the authors have been able to show how IoT can be implemented in agriculture to facilitate better yield, resource utilization, and its sustainable utilization.The implementation of IoT-based smart agriculture systems significantly enhanced farming efficiency, resource utilization, and crop yield. Results indicate improved decision-making, reduced manual labor, and increased productivity through automated monitoring and mobile-based control.
Optimization of Feature Selection using Firework Algorithm for Machine Learning Algorithm
In machine learning and deep learning, the optimal feature selection plays an important role in enhancing performance, decreasing computational cost, and improving the interpretability of the algorithm. The performance of the machine learning algorithm is impacted by the noisy, redundant, and meaningless features found in the majority of classification problem datasets. Feature selection is the process of selecting a feature subset and a search method for finding the optimal subset of features from many features using a fitness function to improve the accuracy and execution time. This research focuses on the application of the fireworks algorithm as a useful tool for optimizing feature selection. By navigating the feature space, the recommended technique finds the optimum subset of features that maximize a model's performance. By analyzing the fitness function, which combines the complexity of the model with the predictive ability of the chosen features, the program repeatedly improves the feature subset. The paper makes use of widely known datasets on breast cancer that include a limited number of characteristics. The classification performance of selected feature subsets is evaluated using classification techniques: Support vector machine, logistic regression, and bagging classifier. The proposed algorithms are better than the particle swarm optimization algorithm, ant colony optimization algorithm, principal component analysis, and so on. The results indicate that a certain feature subset may be chosen with higher accuracy through the use of the recommended techniques, as opposed to using all characteristics. An optimal subset of feature selection techniques enhances the accuracy and decreases the number of features. Enhancements in classification accuracy are supported by a substantial decrease in the number of features with a higher weight on the fast reduction of the fitness function.
Transformative Role of LLMs in Digital Forensic Investigation: Exploring Tools, Challenges, and Emerging Opportunities
In the evolving realm of digital forensics, the admissible nature of trustworthy digital evidence in a court of law necessitates the application of scientifically validated digital forensic investigative techniques to substantiate a suspected security event. The incorporation of LLMs represents a transformative technology, set to enhance the efficiency and accuracy of digital forensics investigations. A thorough literature analysis is conducted, including current digital forensic models, tools, large language models (LLMs), deep learning methodologies, and the application of LLMs in investigative processes. The review delineates the issues in current digital forensic methodologies and examines the barriers and potential of integrating LLMs. This study emphasizes the need of integrating LLMs into digital forensics, providing insights into their advantages, disadvantages, and wider implications for addressing contemporary cyber threats.
Leveraging Social Media Streams for Disaster Prediction: A Machine and Deep Learning Approach
Natural disasters have an impact on the lives of people and animals worldwide. Twitter, which is a social networking tool that enables individuals to share news, opinions, and even their experiences, has emerged as a valuable source of real-time information. Since real-time data is easily accessed, many service providers make an effort to analyze it on a daily basis for purposes of detecting emergencies and lowering risk factors. As for people, a person cannot sit and analyze thousands of records and, at the same time, recognize threats in real time. To address this challenge, machine learning (ML) and deep learning (DL) techniques have become essential tools for automating disaster detection and classification. The article examines the disaster prediction capabilities of Twitter text data and evaluates the power of diverse supervised machine learning models (logistic regression, naive Bayes, support vector machines (SVM), k-nearest neighbors (KNN) and XGBoost). Also, a deep learning model based on Long Short-Term Memory (LSTM) is tested to see how it performs on sequential data in tweets. The main goal of the work is investigating the impact of the usage of different data modes, including text, visual data (including pictures and satellite data), audio data (including sounds of emergency calls or acoustic patterns), and blog or news posts, on effective disaster prediction and classification. Although the nature of Twitter text data (real-time, short, and publicly available) has been prioritized in this research, the property of multimodal data sources to improve situational awareness is also recognized as their increasing role. Through the analysis of these varied streams of information with both a traditional machine learning approach and an advanced deep learning approach, the comprehensive study will attempt to create a more holistic and responsive disaster detection framework that could serve as an aid to decision-makers and emergency responders in their attempt to mitigate losses and improve the response strategy.
A Novel Blockchain Proof of Validation Scheme Based on Explainable AI for Healthcare Workload
These days, the usage of blockchain with machine learning to optimise data validation in terms of transparency, validity, and immutability has been increasing daily. Therefore, many complex applications, such as healthcare and related disease processes, have recently required the implementation of many remote resources in a transparent form. The blockchain provides real-time security validation based on proof of work validation schemes. To understand the dynamic situation of blockchain, mainly machine learning implemented for the decision and improve the efficiency of the security. However, there are many limitations when using blockchain technology with machine learning. Therefore, to cope with this issue, a novel blockchain proof of validation scheme based on explainable AI for healthcare applications is needed to process the decision of blockchain with machine learning in a more explainable way. We present the blockchain proof of work validation explainable AI (PoWV-XAI) to control the delay, energy, cost and security dynamic issues compared to existing blockchains with machine learning algorithms. The proposed PoWV-XAI algorithm suggested different metaheuristic schemes and supported the explainability of healthcare workload execution on other nodes, such as local and server. Simulation results show that the proposed PoWV-XAI is more explainable, and all decisions, such as processing delay, validation, security, energy, and cost, are explainable compared to existing blockchain methods.
Advancement in Smart Vision Systems: A Computer Vision-Based Assistive System for Visually Impaired Individuals
Visually impaired individuals experience significant mobility challenges due to the limited situational awareness provided by traditional aids like white canes. To address this, an AI-powered smart vision kit that enhances environmental perception through real-time object detection and audio feedback has been proposed. Our system combines embedded edge computing with optimized neural networks to deliver a portable, low-cost assistive solution. The hardware prototype incorporates a Raspberry Pi 4 and camera module with a TensorFlow Lite pipeline, utilizing a quantized MobileNetV1 SSD model trained on the COCO dataset for efficient inference. The framework processes live video streams via OpenCV, detecting objects within a 5-meter range at 12 FPS (tested on 480p input). Detections are converted to spatialized audio alerts using text-to-speech (TTS), prioritizing critical obstacles.
Automated Fetal Femur Segmentation and Length Measurement in Ultrasound Images: A Key Tool for Accurate Gestational Age Assessment
Accurate gestational age (GA) estimation in the second and third trimesters is crucial for effective prenatal care. It is typically determined by measuring fetal femur length (FL) in ultrasound (US) images. However, manual FL measurements are time-consuming and require expertise, leading to the need for automation. This study aims to develop an automated multi-step approach to improve FL measurement accuracy while addressing common US challenges such as speckle noise, shadows, and low signal-to-noise ratio (SNR). The proposed method includes image acquisition, preprocessing for contrast enhancement, speckle noise reduction using a bilateral filter, k-means clustering for initial femur segmentation, and morphological analysis to isolate the femur for precise FL measurement. The approach achieved a Dice similarity coefficient of 93.18±9.54% and a mean measurement difference of 0.062 cm compared to manual assessments, with 95% limits of agreement from -1.06 cm to 1.19 cm, confirming its accuracy and clinical reliability. These results demonstrate that the proposed method enhances FL measurement accuracy, reduces manual workload, and contributes to more reliable GA estimation, making it a valuable tool for prenatal care.
Enhancing Multivariate Data Classification Using Graph Convolutional Networks: A Comparative Evaluation with PCA and t-SNE
Graph Neural Networks (GNNs) have become a critical tool for learning on structured data, particularly for their ability to capture feature dependencies within multivariate datasets. This study investigates the application of Graph Convolutional Networks (GCNs) to improve classification performance on such data. We constructed a fully connected graph using cosine similarity to compare our GCN approach against standard dimensionality reduction techniques like PCA and t-SNE. In our tests, the GCN model clearly surpassed these baselines in accuracy, precision, recall, and F1-score.But the method has its own trade-offs: dense graphs are computationally costly, there are chances of overfitting on smaller datasets and the resulting embeddings may be uninterpretable. We cannot make such a claim of generality at this point because this review has only limited itself to one multivariate data set. In future endeavour we would wish to compare GCNs to more powerful classifiers, such as Random Forests, Deep Neural Networks, with greater regard to efficiency in training and resistance to noise. To summarize, this paper confirms that GNNs are an ideal option when it comes to multivariate classification, although scalability and interpretability have to be resolved in order to make it applicable in the real-life context.
Advancements in EEG-Based Machine Learning Techniques for Early Autism Spectrum Disorder Diagnosis: A Review
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction, communication, and repetitive behaviors. Electroencephalography (EEG) has gained prominence as a reliable tool for ASD diagnosis, capturing critical behavioral patterns. Researchers have applied various Machine Learning (ML) techniques to enhance ASD detection, achieving notable accuracy. Studies using feature selection with ML classifiers have reported up to 100% accuracy in children as young as 6–12 months. Other approaches integrating EEG with behavioral features such as eye gaze, facial gestures, and body movements have attained classification accuracies as high as 87.5%. Additionally, resting-state EEG studies have explored microstate differences between ASD and neurotypical individuals. Several ML models, including Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods like Random Forest and Naïve Bayes, have demonstrated classification precision between 90% and 99%. However, challenges such as data heterogeneity and limited sample sizes hinder clinical implementation. This review highlights the most notable EEG-based ML studies for ASD diagnosis and emphasizes the need for further research to refine these techniques for broader clinical adoption.
Advancing NLP for Shahmukhi Punjabi: Word Embedding and Text Classification with a Novel Dataset
The Punjabi language occupies a large pool in today’s era; millions speak it. Only in Pakistan, 80 million people speak the Punjabi language in the province of Punjab. However, with this Big Market Cap, there has yet to be any proper research available. This research focuses on the Punjabi Language, especially the Shahmukhi Punjabi language, famous in Pakistan and Asia. However, it needs to be given more attention in the existing research. There needs to be an adequately supervised established dataset available with large data. Till now, there has yet to be any proper research on Word Embedding and Classification. This paper introduced the crafted dataset for the Shahmukhi Punjabi language dataset. It is also based on advanced NLP techniques like Word2Vec and SDfasttext for Word Embedding to capture the semantic relation within the language. In addition, we investigated the applications of six distinct classification models to analyze four different categories: News, Ghazal, Dohra, and Poetry. The notable success of the Naive Bayes classifier with other classification models lays the groundwork for future research and applications in natural language processing for the Punjabi language. The study encourages further exploration and the development of tailored solutions to meet the linguistic diversity in digital environments and apply deep learning models.
A Review on Cardiovascular Diseases Risk Prediction Approches Based on Machine Learning and Evolutionary Algorithms
Across the world, 17 million people die from heart disease each year. Heart-related diseases were the main cause for about 19\% percent of deaths in Pakistan in 2016, the same has since now risen to 29%. As per most recent WHO statistics regarding prevalence of heart attacks in Pakistan, approximately more than two hundred thousand (200000) persons died in Pakistan in 2020 due to coronary heart disease, making up 16.49 percent of all fatalities. With a death rate of 193.56 per 100,000 inhabitants, Pakistan is ranked at number 30 in the world. Rising death rate due to heart disease can be minimized through detection at early stage. Different data mining approaches have made early detection of cardiac disease possible. Certain datasets are being used to retrieve useful information. Several machine learning techniques / models have been proved to be the most effective, accurate and profitable to detect cardiovascular disease at an early stage. However, the approach of machine learning and Genetic Algorithm (GA) with feature selection may aid in lowering the computational complexity of GA and increasing the effectiveness of its search for ideal solutions. Hence, there is dire need to apply such hybrid approach to get much more effective and accurate results. The goal of this survey paper is to review different papers related to CVD prediction at early stage by applying hybrid approach of ML Techniques with Genetic Algorithm. Moreover, the results obtained by the authors in reviewed papers are also examined. In the end, this survey will showcase the importance of the hybrid approach in improving accuracy of ML results.
Next-Gen Railway Crossings with IoT Solutions for Enhanced Safety and Control
Unmanned level crossings particularly at the railway crossing are very dangerous, and cause accidents, which are normally caused by human oversights. This study provides an IoT-based railroad crossing system, which can improve safety using a combination of the NodeMCU, vibration sensors, and Firebase to control the gates in real time. In contrast to the manual or semi-automated systems used in the past, our solution exploits the use of wireless data transmission to monitor a train that is heading towards or leaving as well as automate the operation of the gates. The system will comprise three interconnected nodes namely two vibration sensor nodes that will be located at strategic points along the track and a gate node that has the leadership of a servo motor, a buzzer and LED indicators. Sensor data of the presence of a train is sent to the gate node when it has been recognized by the firebase, which closes the gate automatically and sends alert notifications. When the train takes off, the second sensor is utilized to open the system again. Our scalable, low-cost and real-time solution is expected to dramatically decrease the number of accidents in on coming trains, as well as offer a very efficient and IoT-based alternative to the traditional gate control systems.
Integrated HealthAI: Revolutionizing Healthcare through Advanced Diagnostic Systems
The incidence of skin cancer has increased dramatically and the need for early detection, management of the disease, and effective public health campaigns are therefore paramount. Here, we introduce an AI-powered health application, encompassing novel features for skin cancer detection, heart rate estimation, and a health-related chatbot, providing new opportunities for accessible healthcare solutions. With the chosen Skin Cancer Classification data set and the YOLOv8 model, the system successfully classifies skin cancers including actinic keratosis, basal cell carcinoma, dermatofibroma, and vascular lesions. It allows users to upload images of skin conditions and get instant data clinical insights, while for heart rate monitoring, it uses video analysis of the user's uploaded facial videos. The chatbot also gives individual health recommendations so that users can better make health-related decisions. The performance metrics (accuracy, precision, recall, and F1 score) present the effectiveness of the application for dermatology and health monitoring. Such tool is promising of better access to healthcare, encouraging screening and preventive care leading to better public health.
Sentiment Analysis of Balochi Text Using Deep Learning
Balochi is a low-resource language with limited available data for computational modelling. This study aims to perform sentiment analysis on Balochi text using machine learning techniques. To address the scarcity of linguistic data, we contribute a large, newly constructed dataset of Balochi text. Our proposed model incorporates feature extraction and data augmentation within deep learning algorithms to classify sentiments as positive, negative, or neutral. We evaluate both traditional machine learning methods—such as Random Forest and Support Vector Machine (SVM)—and advanced deep learning models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).The experimental evidence proves that LSTM and GRU are more effective than traditional methods, and the accuracy rates of sentiment classification with their help are 83.57% and 81.23%, respectively. It has been experimentally verified that, when it comes to the Balochi sentiment analysis, deep learning methods can be more effective than the traditional ones.
Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Nature-Inspired Optimization Algorithm and Machine Learning Techniques
PDAC is one of the most harmful cancer causes due to late diagnosis, its rapid progression, and an 11% survival rate of 5 years. Current methods for diagnosis are very costly, uncomfortable, and unreliable, However, better and more accurate solutions are needed. This study proposes a diagnostic model using urinary biomarkers and machine learning techniques for early detection. Key urinary biomarkers, including LYVE-1, REG1B, TFF1, and plasma CA19-9 are used with patient data. Particle Swarm Optimization is used here for feature selection and hyperparameter tuning, optimizes the machine learning classifiers like Support Vector Machine, Logistic Regression, and Random Forest. Accuracy, precision, recall, and F1-score are used as evaluation metrics; however, random forest achieves the highest accuracy of 89.83%. This study shows how PDAC detection changes after combining molecular diagnostics with machine learning. Future research could explore the study of hybrid swarm intelligence algorithms and increase the data set to make further enhancements to diagnostic capabilities. This model shows a great step toward a quick and accurate diagnosis of PDAC and improves patient outcomes and survival rates.
Harnessing Machine Learning for Accurate Smog Level Prediction: A Study of Air Quality in India
Accurate prediction of smog concentrations is needed to mitigate the harm of AP on public health and the environment. This research proposes a new method to combine machine learning (ML) models with live data from Central Pollution Control Board (CPCB) to fill in the smog prediction accuracy gaps. The data consist of hourly AQI readings from different towns in India which were preprocessed to adjust for missing values and normalize data before ML models. The algorithms were tested with 8 ML algorithms, and hyper-parameter settings were tuned using the GridSearchCV method. The results show that XG Boost Regressor (XGBR) and Extra Tree Regressor (ETR) models significantly surpass other ML algorithms and traditional techniques with better accuracy on predicting smog. These results are useful for policymakers and environmental agencies to implement sustainable air quality management.
How Effective are AI Tools for Diverse Learners? A Case Study on Python and Data Science Education
Artificial Intelligence (AI) based tools (such as ChatGPT) has revolutionized the learning methodologies for complex subjects by introducing the innovative methods in education. This study focuses on how AI tools can be helpful for the undergraduate students at Shah Abdul Latif University in learning the Python programming and Data science concepts. The study was conducted on beginners and intermediate groups of students through an organized survey by comparing their experiences gained through interactive tasks. The survey results reflect that intermediate users get more benefits from AI tools due to their familiarity with the technologies, whereas beginners face challenges in comprehension and ease of use. The study concludes recommending some practical suggestions to enable AI tools more effective, comprehensive and user-friendly.
5G and AI: Addressing Security Challenges in Next-Generation Wireless Networks Through Machine Learning and Cryptographic Solutions
Modern wireless communication technologies are progressing very fast making it possible to deploy fifth-generation (5G) networks that have high speed with low delay and great connectivity. However, these innovative technologies also bring significant security risks as these are based on distributed environments, network slicing, and software-defined networking. Considering these threats, this research aims to examine the application of artificial intelligence and cryptographic approaches towards mitigating these risks. The use of AI in security has been highlighted to one of the best security features of current computer and network systems, especially in the case of machine learning. The Convolutional Neural Network (CNN) based detection models like GANs and Auto encoders show good detection rates but have issues of high computational load and energy consumption. Reinforcement learning models provide clients with adaptive solutions for security to change their approach as threats change. Moreover, five advanced solutions include post-quantum cryptography, homomorphic encryption, and blockchain-based authentication to enhance the security of the 5G network from unauthorized parties and data loss. These approaches are then assessed for their performance and effectiveness through experiment in enhancing aspects such as network security, performance, and efficiency in terms of energy use. However, adversarial AI attacks, block chain scalability, and the computational overhead associated with quantum-resistant encryption are still hurdles towards large-scale adoption. This paper revealed that there is the necessity to further refine the AI methods used, set standardization across the regulatory bodies, and employ highly secure cryptographic techniques for better protection of the 5G network. AI security frameworks, together with cryptographic improvements for the future generation wireless networks, can significantly improve security while maintaining efficiency and scalability, which will promote more secure future networks.
Recent Advances in Machine Learning Models for Antiviral Peptide Prediction
Viral diseases are widespread, and their impact is expressed in millions of cases of infection and mortality around the world. Chronic viral diseases include COVID-19, HIV, and hepatitis. To prevent and treat these viral infections, novel agents and antiviral peptides (AVPs) have been developed. Thus, identifying AVPs is crucial because these pieces of information are invaluable throughout the entire pharmaceutical industry and other sciences. This has been done experimentally and computationally, but the need for better, more accurate, and efficient predictors persists. This research also reviews current AVP predictors, including the datasets employed, feature representation methods, classification algorithms, and assessment criteria. In our paper, we discuss the weaknesses of the existing techniques, overview the most efficient strategies, and evaluate the benefits and drawbacks of the classifiers. Furthermore, several directions for future work are discussed in detail, including enhanced feature representation techniques, feature selection, and classification approaches. The following advancements aim to improve the effectiveness of algorithms for predicting AVPs, enabling the development of new antiviral drugs more successfully.
AI vs. Human Programmers: Complexity and Performance in Code Generation
Large language models, like ChatGPT, have shown the ability to do a variety of tasks in different fields, and this has increased efficiency greatly. However, their increasing use is causing concern about the potential job displacement, particularly in the technical fields. While there have been many studies on the performance of large language models in technical fields, there is a notable absence in assessing their performances in programming. This study fills this gap by comparing ChatGPT (GPT-4) and human experts in the coding discipline to determine if ChatGPT has advanced to a point where it can replace human programmers. To accomplish this goal, this study has produced 300 Python programs with ChatGPT (GPT-4) and compared them with functionally equivalent programs written by three experienced human programmers. The evaluation included both quantitative and qualitative evaluations using measures such as Halstead Complexity, Cyclomatic Complexity, and expert judgment by two human evaluators. The results showed statistically significant differences between the ChatGPT-generated and human-written code. Programs that were generated by ChatGPT were shown to be verbose, complex, and resource demanding, which is reflected in higher program volume, difficulty, and cyclomatic complexity scores. In qualitative terms, ChatGPT's code was easier to read, but lagged behind in some key areas, such as the quality of documentation, structuring of functions, and compliance with coding standards. On the other hand, human-written programs performed well in terms of maintainability, error handling, and dealing with edge cases. Although ChatGPT was found to be incredibly efficient at creating working code, the output needed a lot of review and refinement to be considered standard. The study concluded while ChatGPT is a useful tool for generating code, it has not yet reached the level needed to replace human expertise in programming.
Empowering Sentiment Analysis with Deep Learning Model: Evaluating Social Media's Benefits and Drawbacks
Online social networks (OSNs) have revolutionized communication by facilitating unprecedented information sharing and global connections. Despite these benefits, OSNs also present significant challenges, including the spread of misinformation, increased distraction, and adverse mental health effects. This study examines a dataset of 3,904 user reviews collected from online sources and personal networks, revealing a polarized sentiment distribution with 56% positive, 43.1% negative and 0.9% neutral views on the impact of social platforms. To capture the nuanced sentiments expressed, Long Short-Term Memory (LSTM) enhanced with preprocessing techniques such as tokenization, lemmatization, and word embeddings with Word2Vec was employed. The LSTM model achieved an accuracy of 86.43% in sentiment classification, significantly outperforming traditional baseline methods. These findings provide valuable information for platform developers, policymakers, and researchers aiming to understand and mitigate the social and psychological effects of digital platforms. Future research will focus on expanding the dataset and addressing class imbalance to further refine and enhance sentiment analysis models.
Optimizing Adaptive Hypermedia Educational Systems: A Comparative Study of Frameworks
The majority of online educational systems deliver the same content to all learners, disregarding their individual needs, goals, and educational backgrounds. This approach often makes the learning process challenging and unengaging. Adaptive hypermedia addresses this issue by enhancing these systems through personalization and customization. Adaptive hypermedia-based educational systems (AHES) create a learner profile, known as a learner model, and tailor the content based on the learner's preferences and prior educational history. However, developing AHES is a complex and time-consuming task, as it requires the integration of adaptive features alongside communication tools, digital libraries, and more. To simplify this process, various authoring tools and frameworks, such as AHA! Moodle, Open edX, InterBook, and COFALE, are available. This study provides an analysis and comparison of these frameworks, facilitating the selection of an appropriate framework for developing high-quality AHES.
DermInsight: A diagnostic System for Human Skin Diseases utilizing Deep Learning
Skin diseases present significant challenges in healthcare due to their diverse diagnosis and treatment. They ranked among the top causes of disability because most people ignore their early symptoms due to costly and time-consuming diagnostic methods, which worsen the skin condition with time. This research-based project is a contribution to the timely and cost-effective identification of seven prevailing skin diseases. We propose a novel deep learning-based system capable of classifying distinct skin diseases including melanoma, melanocytic nevi, actinic keratosis, benign keratosis, basal cell carcinoma, dermatofibroma, and vascular lesions. By leveraging a comprehensive dataset HAM10000, our system achieved an impressive accuracy rate of 98.14% in accurately identifying and categorizing these skin diseases. We employ transfer learning and fine-tuned three advance deep learning models MobileNetV1, MobileNetV2, and Xception, and evaluate their performance in the classification of seven human skin diseases. Remarkably, MobileNetV1 emerged as the top-performing model, surpassing the capabilities of the other models and existing state-of-the-art methods. In addition, the proposed model is deployed in an android app named “DermInsight” for the use of dermatologists. The dermatologist uploads the image of the skin lesions of a patient and the app will predict the disease within 1 to 2 seconds.
A Convolutional Neural Network (CNN) Based Framework for Enhanced Diagnosis and Classification of COVID-19 Pneumonia
COVID-19 pneumonia is a persistent worldwide health problem that usually affects the most vulnerable groups in society: the newborn and aged populations. Most of the current endeavors toward handling diagnosis and classification of pneumonia have used numerous techniques for machine learning and deep learning, with a particular focus on COVID-19 pneumonia. However, most of these techniques have raised concerns with regard to diagnostic precision as a result of the limited application of advanced algorithms, datasets whose validation is mostly inadequate and predictive capability. To address these limitations, our research introduces a deep learning-based approach by Convolutional Neural Networks (CNNs), which enhances the performance in classifying COVID-19 pneumonia. Salient features of the proposed method include a four-step process: first, data acquisition from a comprehensive chest X-ray dataset on GitHub; second, processing and analyzing the data through visual means like histograms and scatter plots; third, using CNNs supplemented with techniques for data augmentation in supervised learning; lastly, performance evaluation to benchmark against existing models. The present study uses features from X-ray images with the help of CNN's advanced pattern recognition capabilities in pursuit of achieving better generalization and precision in classification. The model achieved an accuracy of 85.70\% and precision of 88.6%, which surpasses the existing techniques and thereby built the promise of improving the accuracy of the diagnostic process, hence, leading to improved care for the patients, and more so forms the foundation on which future AI-powered healthcare diagnostics are expected to take off.
Distance and energy aware DEAODV routing protocol in disastrous situation
In the disastrous or emergency situation a reliable and robust ad hoc mobile network communication is important. The natural disaster when occurs in the urban populated areas which damage Information technology infrastructure and other valuable assets within a movement. In such emergency situation when the disaster victims stuck in debris, lifesaving of people are important. The purpose of this study is to enhance lifetime and improve network performance when the lives of disaster victims are matters. Distance and Energy aware AODV (DEAODV) reactive routing protocol enhance the network performance and ensure the reliable data transmission during crisis situation. The DEAODV reactive routing protocol considered node Energy and shortest distance as a routing metrics during route decision process. DEAODV routing protocol is compared with traditional AODV routing protocol to evaluate the performance of network. The proposed routing protocol is more efficient in emergency situation than AODV in terms of Packet Delivery Ratio, End to End Delay and Consumed Energy.
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