UCP Journal of Engineering & Information Technology
UCP Journal of Engineering and Information Technology is HEC recognized (Y-Category) research journal UCP Journal of Engineering and Information Technology (UCP-JEIT) is a multidisciplinary, peer-reviewed, open-access journal jointly published by the Faculty of Engineering and the Faculty of Information Technology & Computer Sciences. The journal is devoted to publishing research in Engineering, Information Technology, and Computer Sciences. ISSN: 3005-8015 (Online), 3005-8007 (Print)
A Systematic Evaluation of Machine Learning Techniques for Dyslexia Detection Using EEG Signals
Dyslexia is a learning disability that develops in childhood, leading to difficulty in reading, writing, and spelling, while having average or above average intelligence. The range of impact can be significant if this condition is not diagnosed early. Various methods have been tested to diagnose this condition, including psychometric assessments, facial and eye movement tracking, magnetic resonance imaging, and electroencephalograms. Electroencephalography (EEG) measures the electrical activity of the brain and contributes to understanding cognitive processes. It is particularly useful in recognizing patterns of neural activity that correlate with dyslexia, providing a cost effective and objective means of diagnosis. This paper attempts to review available diagnostic methods and highlight their shortcomings and gaps in research. It also evaluates machine learning and deep learning models for the classification of dyslexia using EEG data collected from a video-based educational paradigm. The models include Support Vector Machine, Random Forest, Logistic Regression, KNN, Decision Trees, AdaBoost, Naïve Bayes, CNN, LSTM, BiLSTM, and GRU. The best models in capturing EEG data patterns were CNN and LSTM, while the best classical baselines were SVM and AdaBoost. The proposed method showed a weighted F1 score of 0.99, proving reliability.
Deep Learning, Machine Learning, Dyslexia, EEG based classification
Copyright (c) 2026 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
An AI-Enhanced Lightweight Data Integrity Framework for Secure IoT Communication
When billions of everyday objects connect to open networks, two security problems tend to arise in parallel: attackers who alter sensor readings mid-transit, and attackers who masquerade as legitimate devices to inject fabricated data. This paper presents the AI-Enhanced Data Integrity Service (AI-DIS), a security framework tailored for the kinds of low-power IoT microcontrollers that standard protocols simply cannot accommodate. Building on the classic Data Integrity Service model—with its Source-Side Entity (SE) and Destination-Side Entity (DE)—AI-DIS introduces three coordinated layers of protection: a TinyML anomaly pre-filter that inspects outgoing sensor data at the SE before any signing takes place; a contextual behavioural validator at the DE that reasons about plausibility once the cryptographic check has passed; and a Federated Averaging (FedAvg) update loop that continuously refines the shared threat model without pulling raw sensor data away from the device. Each packet is signed using HMAC-SHA256 over the device MAC address, the smart-environment identifier, a strictly increasing sequence counter, and a timestamp—a design choice that rules out replay attacks at the protocol level rather than relying on detection heuristics. Testing on the N-BaIoT and IoT-FADS benchmark datasets with 5-fold cross-validation shows the LSTM-based pre-filter reaching F1 = 0.961 ± 0.007, a false-positive rate of only 0.9%, and an inference time of 12.1 ± 0.8 ms on an ARM Cortex-M4 running at 80 MHz. End-to-end, AI-DIS consumes 25.4 µJ per packet. A smart-home deployment study and a comparison spanning ten related works—two of them from 2026—show that AI-DIS is the only solution that satisfies all five required properties at once: data integrity, source authentication, lightweight operation, IoT-readiness, and AI-driven threat detection.
Design and Implementation of Integrated Security Solutions Using Internet of Things and Artificial Intelligence
In recent years, the need for advanced security systems has increased due to rapid urban development and the growing use of smart technologies. Traditional security approaches often depend on manual monitoring, which limits their effectiveness in handling complex and evolving threats. This study presents the design and development of an integrated security system that combines Internet of Things (IoT) devices with Artificial Intelligence (AI) techniques. The proposed solution uses connected sensors, surveillance devices, and intelligent algorithms to monitor environments and detect threats in real time. Firstly, the data is gathered through these edge devices, this data can vary in form. Some of the data gets processed near the source of its production. This pre-processed data becomes more efficient for data transmission. Any immediate threat can also be detected during this pre-processing. Once the data is transmitted it is analyzed by more advanced AI algorithms. Pattern recognition is also performed during this. The system is designed to improve situational awareness, reduce the reliance on human intervention, and enable a faster response to security incidents. It achieved significantly higher detection accuracy (up to 98.7%) and improved system performance compared to conventional security approaches.
Smart Surveillance, Wireless Sensor Networks
AI Adaptability and Project Success: Roles of Decision-Making and Transformational Leadership
Artificial intelligence (AI) is the catalyst of digital transformation in project-based industries, though the means by which AI contributes to success in projects are not well understood. The article examines the association between AI adaptability and project success by exploring the mediating mechanism of IT-enabled decision-making and the moderating effects of transformational leadership. Based on Dynamic Capability Theory and Transformational Leadership Theory, a moderated mediation model is developed and empirically tested using quantitative survey data from AI-enabled project professionals. Direct, indirect, and conditional effects among the variables of interest are investigated using Structural Equation Modeling (SEM). SEM was used to analyze survey data from 300+ project professionals. Results indicated that AI adaptability significantly improved project success (β = xx, p < 0.05), while AI-supported decision-making partially mediated this relationship. Transformational leadership further strengthened the indirect effect of AI adaptability on project success. The findings suggest that AI adaptability positively affects project success, and AI-facilitated decision-making partially mediates this relationship. In addition, transformational leadership has a significant moderating effect on the association between AI adaptability and decision-making, such that this moderation is strengthened at higher levels of transformational leadership in mediating the indirect relationship with project success. These outcomes help support the understanding that, in the context of a project, the true value of the implementation of AI is found in the preparation of the organization, the quality of decisions, and the type of leadership support. The research builds on project management and information systems by offering a new integrative framework to understand the effect of technological capability and managerial leadership on project performance. Practically, the outcomes provide companies with opportunities to leverage their AI projects to enhance the success of their future projects.
Artificial Intelligence Adaptability; Project Success; AI-Supported Decision-Making; Transformational Leadership; Digital Transformation; Project Management; SnapCheck.io.
Behavioral and Deception-Driven Cyber Defense Management in SOCs Using Digital Decoys, MITRE ATT&CK, and SOAR
Organizations are increasingly enhancing their cyber defense capabilities in response to cybercrime's growing threat and risk. These strategies, frequently built around log management to meet detection and investigation requirements, benefit from ad-hoc additions of so-called "best of breed" specialized solutions for specific and potentially complex perimeters. This tends to address their flaws or even introduce new ones. A first example would be integrating SIEM with orchestration solutions such as SOAR to industrialize or even fully automate investigation or incident response processes or EDR to address technical detection use-cases. Particularly at the system level and to facilitate endpoint response. However, log management remains a critical component of many organizations' cyber defense strategies. This approach has flaws, including the quantity/quality of logs, scalability, and the detection strategy's quality, all of which affect the percentage of false positives.Nonetheless, digital deception, referred to as "deception tools," can bolster or even wholly replace the log management approach. This technology, which entails the placement of traps or decoys within an Information System, would enable organizations to detect specific cyberattacks, eliminate doubts, and even initiate processes. Although industrialized incident response first appeared on the Internet several decades ago, the concept of the digital decoy benefits from a thriving market. The subject of this study is the benefits and limitations of various market solutions for enhancing the detection and response capabilities of today's businesses.
Optimizing Geometric Constraints: A Systematic Review on the Impact of Linear Curvature Regularization in Variational Joint Segmentation-Registration Models for Medical Images
Biomedical imaging has played a vital role in disease detection, tracking, and treatment. These methods are essential for patient care and health benefits. A synthesis of the literature is helpful to find out limitations such as difficulty handling images with complex background structures and intensity inhomogeneity. The primary aim of this review is to tackle these inadequacies. Then, to identify gaps for future studies. A main goal is to review a joint segmentation–registration model that highlights computational and methodological challenges. Also, to identify obstacles to the clinical translation of the new joint segmentation and registration (NJSR) model. A systematic literature review was carried out to specify relevant studies in medical image analysis. Searches were conducted in IEEE Xplore and Google Scholar. Key search terms incorporated "medical image analysis" and "joint segmentation and registration." The review incorporated models across anatomical structures (e.g., brain magnetic resonance imaging (MRI), hand X-ray), and comparative and narrative reviews of main foundational studies. It excluded the non-joint studies. The literature studies show that separate approaches as compared to joint segmentation and registration frameworks, do not show much significant elevation in performance. The comparative and narrative analyses focused on sum of squared differences (SSD) metrics and the deformation vector field. All in all, the previous studies show that joint frameworks can cope with the reliability, accuracy, and robustness of disease detection and tracking process in biomedical image analysis. All in all, NJSR has demonstrated better performance in convergence speed and registration smoothness compared to Guyder Vese joint segmentation and registration (GV-JSR) model, while NJSR also having the limitations in segmentation accuracy for complex real-world data (Experiment 2 and 4) that is in certain complex experiments the F measure is observed to be the lower in value (that is lower than 0.5). This slight sacrifice in the segmentation accuracy (F-measure) is an acceptable trade-off within the context of pure registration task focused on biomechanical plausibility (as shown by lower E score). In regions where the main objective is to derive a physically meaningful motion field for non-rigid compensation—rather than accurate segmentation borders—this enforcement shifts to achieving a smoother, more reliable deformation field. However, it is acknowledged that for clinical segmentation tasks (e.g., tumor volume definition), this level of precision would be insufficient and require further refinement of the data fidelity term. Moreover, the models having NJSR as a base paper having multi modal frameworks are also come into comparison with each other.
Optimizing Breast Cancer Screening Outcomes in Dense Breasts: Diagnostic Performance, Cost-Effectiveness and Implementation of Supplemental Modalities
Dense breast tissue is a major challenge to screening of breast cancer since it covers about 40-50% of the breast and hides the lesions in traditional mammography. This weakness requires the efficient use of additional imaging modalities for high-risk patients. The following paper is a comparative analysis of the major supplemental imaging techniques, such as ultrasound, Magnetic Resonance Imaging (MRI), Contrast-Enhanced Spectral Mammography (CESM), Digital Breast Tomosynthesis (DBT), and AI-assisted mammography. A literature review was conducted in databases like PubMed, ScienceDirect, and Google Scholar, and peer-reviewed articles published since 2016, and those that concentrated on breast cancer screening in high-density breasts were assessed. The review is a confirmation of the role that mammography plays at its core, but indicates the improved detection of cancer with the help of supplemental ultrasound and MRI. Importantly, CESM has a similar diagnostic potential as MRI and the practical advantages of lower costs and shorter scan time. DBT enhances clear images by reducing overlap between tissues, and it effectively reduces the rate of recollection among patients. Moreover, AI-assisted mammography is one of the essential developments that will raise the detection of cancer in dense breasts and may even prevent the use of auxiliary procedures. The results strongly reflect the need of implementation of tailored and risk-based screening methods where such advanced add-on imaging technologies are combined with Artificial Intelligence, which is likely to elevate screening accuracy, clinical outcomes, affordability, and ultimately make a significant contribution to lowering the rates of mortality of breast cancer.
End-to-End Motorcycle Violation Detection with Region-Specific Automatic License Plate Recognition
We present a region-aware, end-to-end motorcycle violation detection pipeline tailored to traffic conditions in Punjab, Pakistan, which integrates three YOLOv11-based components into a unified framework: motorcycle violation detection (MCVD) for helmet compliance and multi-rider analysis, license plate detection (LPD), and license plate character detection (LPCD). The system integrates lightweight object detection, BoT-SORT-based tracking, and character-level recognition, supported by a synthetic-toreal adaptation strategy that combines large-scale synthetic data with limited real samples. Two specific datasets are published, a 40,000-sample synthetic Punjab license plate dataset (PS-LPCD) and a 650-sample real-world dataset (PR-LPCD), which are publicly released in order to encourage research development and adaptation to the region. Class consolidation enhanced MCVD performance (weighted average F1 score: 0.77) and the LPD model performed at mAP50 = 0.99. Two-stage fine-tuning on synthetic and real samples allowed LPCD to reach a character accuracy of ≈ 98% and a full-plate recognition rate of ≈ 90.7%, both surpassing EasyOCR and PaddleOCR, while also achieving lower per-plate latency. With a single motorcycle per frame, the sequential pipeline maintains a throughput of ≈ 9.5 FPS; the throughput reduces in scenes where there are many motorcycles. These findings indicate that synthetic pretraining, together with a small real fine-tuning, can be used to obtain a powerful, scalable, and region aware automatic license plate recognition (ALPR) system, which provides a reproducible method for detecting traffic violations across a variety of license-plate formats.
AI-Enhanced Battery Degradation Prediction for Solar Home Systems in Sub-Saharan Deployment Conditions
The durability and reliability of batteries in solar home systems (SHS) are critical to the long-term success of off-grid electrification efforts in Sub-Saharan Africa. However, harsh environmental conditions, variable load profiles, and limited maintenance capacity contribute to accelerated battery degradation and unexpected failures. This study presents a data-driven framework for accurate prediction of battery state of health (SOH) using advanced machine learning models under deployment-relevant conditions. Three architectures—Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and Transformer—were trained and evaluated using a high-resolution synthetic dataset simulating 1,000 battery cycles. The dataset incorporated temperature variability, depth of discharge (DOD), charge rate fluctuations, and measurement noise to reflect real-world SHS operating environments. Model performance was assessed using MAE, RMSE, and R2R^2R2 metrics. The Transformer model consistently outperformed others, achieving the highest accuracy and lowest error variance, with residuals tightly centered around zero. SHAP analysis revealed temperature as the dominant contributor to degradation, followed by DOD and charge rate. The deployment feasibility of each model was also validated through inference benchmarking on a Raspberry Pi 4, confirming sub-300 ms runtime and minimal memory consumption suitable for low-power edge computing. These findings establish the Transformer model as a viable candidate for real-time, embedded battery diagnostics in SHS applications. The integration of such AI-based prediction systems offers a scalable solution to enhance battery longevity, reduce maintenance costs, and ensure uninterrupted energy access in underserved regions. The approach also supports adaptive energy management, warranty validation, and sustainable SHS design.
RUPT: An Extension to Traditional Compilers in C++ to Support Programming in Native Language
The medium of instruction has a significant impact on effective communication and comprehension. The majority of literature is available in English because it is a universal language; however, if someone is provided with the information in their native language, they will comprehend the concept much more effectively. In computer science, source code of programming languages is written in the English language, whereas endemic language has its own impact. To address this gap, this study has rendered a framework, “Roman Urdu Programming Translator” (RUPT), that will be used to translate a program coded in Roman Urdu or Hindi into a proportionate C++ program. Actually, this will be an additional layer to the original C++ compiler, i.e., a programmer will write a program using special keywords of the Roman Urdu language, use RUPT to translate it into an equivalent C++ program, and then use the traditional C++ compiler to compile and run it on the computer. The special set of Roman Urdu keywords includes, e.g., “keyboardSay (ks)” instead of “cin”, “screenKiTaraf (skt)” instead of “cout”, “klea” instead of “for” etc. In this work, additional Urdu and Hindi Roman keywords are added to the original set of C++ language and will be replaced by the C++ equivalent keywords by our translator RUPT to convert it into a pure C++ program. RUPT is only composed of the lexical analysis phase. State of the art has increased the understanding and learning rate of novel users towards the field of computer science.
Copyright (c) 2025 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
Design of A Complete Algorithm With Mathematical Modeling For Sound Localization And Tracking Using Artificial Intelligence
This research focuses on designing a novel acoustic source localization and tracking method that applies mathematical modeling in conjunction with machine learning (ML) and deep learning (DL) techniques to overcome issues arising from dynamic noise and reverberations characteristic of real-world environments. Conventional sound localization and tracking (SLT) techniques like Time Difference of Arrival (TDOA) and Beamforming have inherent drawbacks because they are passive techniques operating on basic geometric time delays, rendering them ineffective under dense, multi-source acoustic conditions. To resolve this, the proposed artificial intelligence (AI) enhanced system incorporates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers to significantly boost localization precision and trajectory tracking efficacy. The system extracts dense spatial and temporal patterns from raw audio data, enabling real-time operation suitable for robotic, surveillance, and autonomous vehicle platforms. The experimental results reveal that this AI-based approach dramatically outperforms traditional frameworks across key metrics—including localization error, tracking mean squared error (MSE), average tracking drift, and system latency—at the expense of a manageable increase in computational complexity. Future work will investigate hardware-level optimizations to compress execution timelines and incorporate cross-modal sensor streams to reinforce system robustness in adverse climates.
Multi-Class Brain Tumor Detection Using Transfer Learning and Interpretable Deep Models
Accurate brain tumor detection remains critical yet challenging due to diagnostic complexity and variability in MRI interpretation. This study proposes a deep learning approach for automated multi-class brain tumor classification using transfer learning (TL), three pre-trained CNN models ResNet50, InceptionV3, and VGG16 were adapted and evaluated on a curated MRI dataset of 7,000+ images. Preprocessing, feature extraction, fine-tuning, and integration of Explainable AI (Grad-CAM, LIME, SHAP) ensured robust and interpretable results. ResNet50 achieved the highest performance with 98% accuracy, 0.92 F1-score, and 0.96 AUC, outperforming the other models across all metrics, with strong convergence and minimal misclassification. ResNet50’s architecture enabled deeper feature learning and improved generalization. Explainable AI visualizations confirmed model focus on tumor-relevant MRI regions, enhancing clinical interpretability. The findings position ResNet50 as an effective and explainable solution for MRI-based brain tumor classification, suitable for future real-world deployment and further expansion to mobile and multi-center applications.
Smart Resource Allocation for Mobile Edge Network in IoT Using Game Theory
Emerging from years of research and development, the modern era of computing recognizes Internet of Things (IoT) as most empowering technology to connect the digital and real world. IoT has introduced new advancements that are transforming the world, however, it still faces constraints that limit its effectiveness in various application areas, including computing power, resource allocation, reliability, and time consumption. Achieving acceptable latency for task operations on IoT devices necessitates the appropriate allocation of Mobile Edge Caching device computing resources which should be based on task size, delivery, and service latency. It is impossible to handle the billions of data requests originating from a growing number of base stations. This research proposes a general mechanism for allocating computing resources and caching to facilitate efficient scheduling in cellular networks. A game theory approach used to model miniaturization problems has been employed in this work. A wireless network system has been analyzed where each node in the system is a participant with its strategies and contributions to achieve the desired performance. The simulation results show that the proposed technique has great potential to improve resource allocation. Each IoT device increases the number of requests handled by the MEC server in the non-cooperative subgame. The proposed system efficiently allocates IoT resources and excels in performance and reduce latency.
Microservice Antipatterns: Causes, Detection, and Refactoring Challenges
Microservice antipatterns negatively impact quality, necessitating a thorough understanding of their causes, effects, and solutions. This study provides a comprehensive review of antipatterns after analyzing 50 studies through a multivocal literature review. Key findings show that unprepared adoption and team culture are major causes, affecting maintainability, performance, and testing. Detection techniques are categorized into five groups, with most tools using search-based approaches. Four refactoring strategies were identified, along with their limitations. The study also highlights research gaps and challenges, guiding future work in improving detection and refactoring methods to mitigate antipattern effects.
Maximum Efficiency Point Tracking Control for Dynamic Wireless Power Transfer
Wireless power transfer has evolved as a dominant research field. Roadway-powered vehicles have poor and fluctuating efficiency due to varying coupling coefficients because of their dynamic nature. Efficiency improvement efforts become useless in the event of communication failure between the transmitter and receiver sides. This paper presents a novel algorithm based on a silent handshake between the two sides to achieve the most efficient operation with varying flux linkage. The timed sequence of operations enables the continuous load mapping by impedance matching converter to maximize the efficiency followed by load regulation done by the supply converter based on accurate mathematical modelling. Simulation and experimental results are presented to ascertain the performance of the algorithm and the mathematical models.
HoloNet: Enhancing Predictive Modeling of Space Phenomena Using Holomorphic Neural Networks in CubeSats
The provision of precise space weather forecasts necessities the development of enhanced methodologies for simulating space events. This paper then presents the HoloNet, a holomorphic neural network for improving the predictive modeling of space events based on CubeSat-acquired data. Herein, HoloNet leverages key aspects of CubeSats to address the challenges of managing multi-source data, such as geomagnetic indices, satellite position, solar wind parameters, and sunspot activity. The uniqueness of the approach remains in applying holomorphic neural network for this purpose, which provide a higher dimensionality of features under consideration and thus have the potential to enhance the predictive capability of the model regarding solar-terrestrial interactions. The present’s analysis involved merging data from four different sources, undergoing data preprocessing, feature extraction, and deep learning. The obtained result demonstrates a high degree of generalization, a reduction in prediction error, and a crucial features analysis that identifies the primary consumers of changes in solar activity. This work will demonstrate how HoloNet a significantly after the use of CubeSat technologies for space weather monitoring by enhancing data exploitation and predictive capabilities. In conclusion, this work lays a solid foundation for the use of holomorphic neural networks in CubeSats for precise space Phenomena.
Control Strategies for Smart Charging of Electric Vehicles from a Grid Perspective: A Review
Electric vehicles have emerged as an alternative way to reduce fossil fuel consumption, which is the cause of increasing environmental, economical and geopolitical problems. This paper reviews the strategies for charging electric vehicles smartly from the viewpoint of the grid. These strategies are classified into three categories. The strategies at the component level discuss the necessary aspects of batteries, their charging methods, and chargers for smart charging purposes. The strategies on the system level are discussed under the heads of unidirectional and bidirectional power flow strategies. Unidirectional power flow strategies manage the power flow from the grid to electric vehicles for their charging. The bidirectional power flow strategies, apart from charging the electric vehicles, also use their battery storage for grid support. Also, the strategies that can be deployed at the operational level are discussed. These strategies, on the one hand, tend to alleviate the stressful impacts of increasing the load of charging the electric vehicles on the grid, and on the other hand, use the energy storage capability of the electric vehicles for grid support.
Advanced Fault Detection, Classification, and Analysis Framework for HV Transmission Lines using RT Synchronized Monitoring and Control Systems
The emergence of new technologies such as IoT, along with the merger of renewable energies, AI, smart grids, and non linear loads is enhancing the complexity of modern power systems and detecting fault as well as its correction much harder. Traditional methods suffer from inadequate speed, accuracy, less coverage, and latency that renders them highly ineffective in varying conditions. Reliable power transmission is vital for modern infrastructure, as faults on transmission lines can disrupt supply, damage equipment, and create safety risks. This paper presents a Fault Detection and Analysis System (FDAS) designed to enhance power system reliability and efficiency by enabling early fault detection, classification, and precise fault location. The FDAS system unites sensor inputs from voltage and current transformers alongside superior analysis methods for continuous fault surveillance. Proper detection of faults enables rapid identification of faulty sections thus minimizing the duration of outages and protecting equipment from damage. Fault classification defines fault categories which allows technicians to apply suitable solutions and accurate fault location enhances repair operations to decrease operational interruptions and maintenance expenses. Moreover, the FDAS system outperforms impedance-based and wave-based traditional methods through its combination of real-time acquisition and analytical algorithms and wireless monitoring which accelerates fault detection while enabling accurate corrective actions. Identifying specific fault types through fault classification provides suitable corrective solutions while exact location determination helps minimize repair time and spending related to maintenance costs.
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Securing Financial Transactions: Leveraging Random Forest for Credit Card Fraud Detection
It is a crucial responsibility in the financial sector to safeguard clients and businesses against financial losses by verification of credit cards. It is demonstrated that machine learning algorithms like random forest methods can detect fraud. Multiple decision trees are used in the Random Forest to create predictions. It is very suitable for fraud detection in credit card fraud due to its prowess with high-dimensional and huge datasets. With the help of training on labelled data and performance analysis, the Random Forest algorithm can accurately detect fraudulent transactions and help decrease financial risks. Due to its features of interpretability and durability against overfitting, it is well well-suited tool for firms wanting to improve their fraud detection systems due to. With the use of the strengths of the Random Forest algorithm, accurate analysis and categorization of complex patterns and minute abnormalities present in credit card transactions has become feasible. This strategy helps in the efficient and on-time identification of potential dangers which enables proactive measures to guard against fraudulent activity and increases the accuracy of detection. With this advantage of Random forests, institutions of finance can get an in-depth analysis of the causes that impact fraud incidents and enable them to enhance and improve their security systems. Despite of shifting dynamics of transactions and fraud strategies, the model can tackle overfitting and guarantees reliable performance. Therefore, with the addition of the Random Forest algorithm into card fraud detection frameworks, enterprises acquire the ability to strengthen their defence system and enhance the security and trust of both their clients and the overall financial ecosystem.
A Design-Oriented Classification of Microservice Smells
Context: Introduction of bad smells can generate negative consequences on the quality of microservices. It is essential to gather state-of-the-art knowledge on these smells and understand the challenges they present. This will benefit researchers and practitioners in mitigating the consequences of smells in microservice-based systems. Objective: The main goal of this study is to present a comprehensive catalogue of microservice smells. Method: To document the advancements and best practices in the field of microservice smells., we performed a multivocal literature review study incorporating both academic and grey literature sources. We systematically analyzed 34 studies published from the beginning of 2014 until the end of 2023 by following standard guidelines. Results: 38 bad smells in microservices are identified and cataloged in 10 different types. Conclusion: Research gaps and open challenges are highlighted in this study. This will give directions to other researchers and practitioners towards addressing challenges posed by smells in microservices.
Health Predictions Redefined: The Impact of AI on Future Disease Diagnosis
Healthcare professionals often apply a one-size-fits-all approach in patient care, potentially leading to misdiagnosis, suboptimal treatments, and higher healthcare costs. Machine-learning models have garnered attention for their ability to improve diagnostic accuracy, with numerous studies focusing on machine learning applications for individual disease predictions, such as Type II diabetes, heart disease, kidney disease, and hypertension. However, limited research has tackled the combined prediction of Type I diabetes (standard cases), Type II diabetes (gestational diabetes), and cardiovascular disease, presenting a significant research gap. To address this gap, we introduce a set of benchmark corpora based on authentic patient records, targeting specific disease categories. The first contribution is a heart disease corpus containing 606 instances. The second and third contributions consist of two separate corpora, each with 849 instances: one focused on standard diabetes cases and the other on gestational diabetes cases. We evaluated these corpora with ten machine-learning algorithms and five deep-learning algorithms, rigorously comparing their performance across common metrics, including accuracy, precision, recall, and $F_{1}$-score. Our results revealed high performance across all models, with top $F_{1}$-scores of 0.785 using Random Forest, 0.790 with Gradient Boosting, and 0.994 using BiLSTM for the combined disease prediction. These findings suggest that the proposed datasets and models provide a robust foundation for accurate and scalable high-risk disease prediction, contributing a valuable, multidimensional approach to personalized patient care
Heart disease prediction Diabetes prediction
Auto-Classification of FIA-Cybercrime Wing Complaints Using Bidirectional Encoder Representations from Transformers Model
The Cybercrime Wing (CCW) of the Federal Investigation Agency, which was formerly known as the National Response Center for Cybercrime (NR3C), is governed by rules that were created in 2016 as part of the Prevention of Electronic Crimes Act (PECA) to combat cybercrime. Criminal activities executed using computers and the internet are referred to as cybercrimes. In order to carry out illegal activities, cybercriminals make use of any information system as their primary means of communication with the devices that belong to their victims. This research mainly focusses on the cybercrime complaints with an automated classification system. To achieve the automatic modelling for classification of different types of cybercrimes, this study used the Bidirectional Encoder Representations from Transformers (BERT). Its obstacles mainly include the possibility of human errors as it manually classifies cybercrime complaints, also that there might be delays during handling in comparison with an automated system. The dataset includes complaints submitted in English during a two years window, and it was encoded, tokenized and cleaned thoroughly. The purpose was to simplify the training process for the model. The study used a lightly fine-tuned, pretrained (BERT)-base-uncased model. The findings confirm that the model can be used for classifying complaints and exhibits an excellent classification accuracy, precision and F1-scores between different cybercrime offences indicating its supremacy among advanced Natural language processing (NLP) techniques to strengthen cybersecurity measures.
DDoS Attack Detection System Using Machine Learning Techniques
Distributed Denial of Service (DDoS) attacks are the major issues that introduce disruption of accessibility and reliability of the network services. The purpose of this paper is to demonstrate an overall recognized machine learning based system that can efficiently identify and classify DDoS attacks using a rich dataset allowing us to work with various installation network traffic attributes, we have developed an automatically classification pipeline the Random Forest Classifier which is known for its high performance in handling large datasets and heterogeneous data. These learnt models were then combined with Decision Tree, Gradient Boosting, and Logistic Regression models to provide a better way analyze the product. An important step involved in this framework is the data preprocessing pipeline that involves one-hot encoding of categorical features to numerical features and scaling for the numerical features, leading to model input optimization. Efficiency of our models is assessed through metrics just as accuracy, precision, recall, and F1-score and it is further validated using cross-validation techniques. The top models are being evaluated by powerful tools, which include feature importance visualization, confusion matrices, precision-recall curves, and calibration curves, for a deeper understanding of their predictive ability as well as their decision-making processes, within those models. A feedback loop mechanism for the iterative betterment and adaptation of the model is accounted which learns from new patterns actively. This approach demonstrates good evaluation and robust in identifying DDoS attacks that are threat of cybersecurity defenses using machine learning.
Heart Attack Prediction Using Federated Learning on Distributed Medical Data
Despite advances in cardiology, heart disease continues to be a major global challenge. The development of tools for early detection and accurate prediction of the probability of triggering a heart attack as a critical event in heart disease is essential. Traditional machine learning models for heart attack prediction are a violation of medical data privacy and security as they involve a centralized dataset. Another model, federated learning, is the optimal way to keep decasualized privacy data from being available in multiple medical institutions. In this work, we conduct a study to determine how effective FL is in predicting heart attack using Logistic Regression and Support Vector Machine models with large-scale simulated distributed medical data. The first model yielded an accuracy of 88.52%, indicating that to some extent, heart attack prediction is a use case for FL. We also conduct further research on other models, and the SVM model demonstrated an accuracy of 86.89%, which is considered a fully dependent variable to be predicted as favorable. The current research also examines additional models, including K-Nearest Neighbors and Decision Tree. The latter showed lower performance, exercising an accuracy of 68.89%, while it has higher value in interpretability. It deserves to be aware that the research focus is the communication overhead within the FL framework. In my opinion, it is significant to proceed with the further investigations on the enhancements of optimum communication approaches balancing the model accuracy, training time, and communication cost. Moreover, privacy preservation within the FL deserves to be highlighted. It is worth mentioning that current research is the initial attempt, whereas privacy-preserving techniques customized for LR and SVM within the FL remain an unknown field to be analyzed. Overall, through this research, we have showed the significant potential of the FL approach for heart attack prediction with the use of distributed medical data. This future was proposed by considering the observance of privacy limitations on the accessed datasets. The FL could remain as a significant solution in the development of appropriate machine learning models, enhancing the efficiency of communication, and providing privacy considerations with an opportunity to minimize the risks of compromise.
Predictive Healthcare Analytics Introduction
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SecureNet:A Convergence of ML , Blockchain and Federated Learning for IoT Protection
The Internet of Things (IoT) has become a foundational element of the digital infrastructure, extending its connectivity across various sectors and embedding intelligence in everyday devices. This article introduces SecureNet, a pioneering approach that integrates Machine Learning (ML), Blockchain, and Federated Learning (FL) to enhance IoT security. To navigate this challenging train, an innovative framework that synergizes Machine Learning (ML), Blockchain technology, and Federated Learning (FL) to fortify IoT security. SecureNet is architected to deliver a robust defense mechanism for IoT ecosystems, providing resilience against increasingly sophisticated cyber threats, and ensuring the preservation of data integrity, privacy, and unwavering system reliability. This study explores the application of advanced ML techniques NSL-KDD dataset, implementing two highly effective classifiers: Random Forest and Logistic Regression. The Random Forest classifier exhibited an exceptional accuracy of 99.85%, while the Logistic Regression model demonstrated a near-perfect accuracy of 99.03%. These compelling results highlight the efficacy of ML in identifying and mitigating activities within network traffic. SecureNet leverages ML’s profound analytical capabilities for intelligent threat discernment, Blockchain’s immutable ledgers for unassailable data verification, and FL’s privacy-centric approach to distribute model training. These outcomes underscore the potential of ML models to enhance IoT security by accurately identifying malicious patterns and anomalies within network traffic.
Harmony Hub: Bridging Communities Through Digital Collaboration and Authentic Service Recognition
"Harmony Hub" is a groundbreaking digital platform that has the ability to transform the volunteering sector. This platform brings into play transparency, trust and partnership so as to meet the complex requirements of universities, NGOs, and colleges. It guarantees that every user has a unique profile thereby able to keep comprehensive records and verify data about his/her volunteer experiences. For instance, it assists university students confirm their enrollment status into such schools together with accumulating hours served in formal volunteer programs that can be certified. This helps Non-Governmental Organizations (NGOs) by increasing their visibility hence making sure reliable existing student attendance tracking systems exist. Academic institutions possess an unparalleled capacity to manage student engagement and select partnerships simultaneously. The issuance of blockchain-secured certifications and the implementation of rigorous verification processes are both facilitated by the administrative framework, which is vital to this ecosystem. This abstract captures the essence of Harmony Hub and its potential impact on advancing a community service paradigm that is open, cooperative, and verified. What truly sets Harmony Hub apart is its novel approach to fostering collaboration between NGOs, university students, and users. This integrated platform ensures that all parties not only benefit individually but also contribute to societal betterment, a feat no other platform has previously accomplished.
Boosting the Classification of Complex Large Synoptic Survey Telescope (LSST) Data
Analysis of light curves emanating from various celestial bodies is of paramount importance in order to enable ourselves with quantify the variability in sky and discover time-varying objects. Large Synoptic Survey Telescope (LSST)gather voluminous time-series data. However, classifying these events from large-scale surveys is a challenging task that requires efficient and robust machine learning methods. In this paper, we present a novel approach for astronomical time series classification using gradient boost, a powerful ensemble technique that combines weak learners into a strong classifier. We apply our method to two datasets from the Catalina and Zwicky TransientFacility surveys, which contain light curves of various types of transients and variables. We compare our results with state-of-the-art methods that use different features and models. We show that our method achieves superior performance in terms of accuracy with comparable computational complexity. We also discuss the advantages and limitations of our method and suggest possible directions for future work.
Micromobility is actively being adopted in many cities around the globe. The various personal travel options included in micromobility can contribute to lower congestion, reduced greenhouse gas emissions, and improvement in the general well-being of its users. However, travel options falling under micromobility are not very common in Pakistan. This paper explores the several challenges in way of providing a supportive micromobility ecosystem in Pakistan and discusses the steps to be taken for its adoption. It examines the current infrastructure, cultural norms, and government policies present in the country and identifies gaps and areas of improvements for successful integration of micromobility. The introduction of micromobility in Pakistan presents a significant opportunity to promote sustainable and equitable transportation in the country. With the right interventions and policy solutions, Pakistan can create a transportation system that is accessible, safe, and efficient for all its citizens, and pave the way for a more sustainable future.
Floating Satellite with Ultrasonic Radar Payload for Debris Detection
An educational platform designed for assessing and testing control algorithms for pico and nanosatellites in a near-frictionless environment is presented. The system provides access to test different space application algorithms. The application provides here is the debris detection mission for space surveillance. Different payloads are added on FloatSat, integrated with the Real-Time operating system (RODOS) and STM32F407G as the main controller to detect the debris around the satellite in a specific range.
Harvest Link - Blockchain in Agriculture Supply Chain
The Agricultural supply chain (ASC) is the backbone of economies around the world, playing a vital role in driving numerous industries and serving as a major stakeholder. The involvement of diverse stakeholders, including farmers, distributors, retailers, and consumers, contributes to the inherent complexity and challenges in managing the supply chain effectively. However, the lack of transparency within the supply chain not only presents challenges but also leads to inefficiencies, wastage, and susceptibility to fraudulent practices. Recognizing these deficiencies, this paper aims to comprehensively address and rectify the shortcomings in the agricultural supply chain. The significance of these issues cannot be overstated, as they have a profound impact on food security, fair trade, and the livelihoods of countless stakeholders. In response to the growing demand for transparency in the sourcing of food products, this paper endeavors to shed light on these critical aspects, fostering improved trust, enhanced accountability, and the assurance of safety and quality in food products for consumers. To effectively tackle the identified deficiencies, the paper proposes the utilization of Blockchain technology as the primary mechanism to establish an immutable ledger for every transaction and product movement, ensuring data integrity and trust without the need for intermediaries. Each stage of the supply chain, from cultivation to distribution, will be recorded in a tamper-proof manner, allowing consumers and stakeholders to effortlessly trace the history of agricultural products. Additionally, the integration of AI is proposed to analyze blockchain data, providing real-time insights and predictions to further enhance transparency. While acknowledging potential barriers such as technical challenges, education, and government policies, this fusion of blockchain and AI stands as a promising technology to create a robust, transparent, and accountable agricultural supply chain. Efforts will be made to address these challenges and foster widespread adoption among farmers and stakeholders, realizing the envisioned positive impact on the agricultural supply chain.