Journal of Trends in Computer Science and Smart Technology
A Machine Learning Framework for Evaluating MSME Incubation Dynamics
The Business Incubation is an important way to assist entrepreneurs in transferring their innovative ideas into viable businesses, offering resources, expertise, and connecting them with available resources for assistance. In India, the MSME Hackathons represent one type of competition to generate new and exciting ideas by identifying the best solutions across different technology and industry sectors. This study will develop a data-based framework for understanding innovation patterns from the MSME business incubation framework by using the officially approved ideas generated in multiple MSME Idea Hackathons. This research will also use an innovation analytics perspective as the basis for developing results, rather than using traditional descriptive approaches for understanding MSME incubator policy development; therefore, the results will identify how many institutions, states, and knowledge bases are in the overall data set and relate to the overall structure of the innovation network. A machine learning–based analytical approach is employed to examine innovation domains and structural relationships among different innovation ecosystems. Supervised ensemble modeling is used to evaluate the influence of institutional affiliation and geographical location on thematic specialization, while unsupervised clustering and dimensionality reduction techniques are applied to explore structural patterns within the dataset. The classification model achieved an accuracy of 0.672, precision of 0.599, recall of 0.672, and an F1-score of 0.624, indicating moderate predictive capability while confirming that institutional and geographical attributes provide meaningful signals for innovation domain classification. The results further indicate that the structure of institutional networks and regional environments significantly influences innovation-specific focus areas, while also revealing the coexistence of both organized and distributed forms of innovation activity reflected in hackathon participation patterns. The study contributes a scalable and interpretable analytical framework for the macro-level evaluation of incubation initiatives and provides empirical insights to support evidence-based policy formulation aimed at strengthening the MSME innovation system.
Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
Adaptive Markov Decision Process-Based Congestion Control Using Deep Deterministic Policy Gradient in Distributed MQTT Brokers
The publish-subscribe variant of the Message Queuing Telemetry Transport (MQTT) protocol is lightweight enough to permit the asynchronous sending of messages. Nonetheless, the protocol is experiencing issues related to congestion, which is caused by the IoT traffic characteristics in the distributed broker networks in the IoT environment. As a result, delay occur resulting in a degradation of QoS. However, using classical AIMD and static rate-limiting techniques for congestion control in the IoT environment is becoming difficult. The proposed work provides an adaptive Reinforcement Learning (RL)-based system to handle congestion in the IoT environment. This system can be represented using a Markov Decision Process (MDP). The DDPG algorithm will allow the RL agent to discover optimal strategies in the IoT environment, and it is beneficial in managing non-stationary IoT traffic. Based on the agent clusters’ simulation of the brokers, the RL-based system outperforms the traditional system in the IoT setting. It achieves this as the message drop rate is reduced by 38-40%, the latency is reduced by 27-32%, and the fairness of the load is increased by 18-24%.
Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement
The optimization of resource allocation in a cloud computing environment is a problem that has been challenging due to heterogeneous tasks with varying resource requirements and different optimization objectives for execution time, energy consumption, service level agreements (SLAs) and so on. In this paper, a hybrid multi-objective optimization algorithm called AH-NSGAIII-VND is proposed for solving a multi-objective optimization problem in a cloud computing environment. The proposed algorithm integrates a global search process using a variant of a multi-objective evolutionary algorithm called Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and a local search process using a variant of a local search algorithm called Variable Neighborhood Descent (VND). The problem of resource allocation in a cloud computing environment is formulated as a multi-objective optimization problem considering makespan, energy consumption, cost, service level agreements (SLAs) and resource utilization. Researchers compared the results of the PSO, GA, and GWO optimization methods to the baseline using CloudSim-based model conditions on the changing workload scale. Experimental results indicate that cloud supply governance efficiency is enhanced with the AH-NSGAIII-VND architecture. It achieves around 11.3%, 12.8%, and 28% lower costs than the baseline NSGA-III approach while increasing overall asset utilization by over 5 percentage points. Moreover, with increasing workload, the proposed model exhibits improved convergence behavior and scalability. These results confirm that global evolutionary optimization supported by adaptive local search successfully reinstates or enhances the efficiency of resource allocation.
A Federated Learning Autoencoder CNN Hybrid Approach for Privacy Preserving Network Traffic Classification
Federated learning (FL) is an essential technique for classifying network traffic in a decentralized setting while maintaining privacy. In this paper, the Federated Learning Autoencoder Convolutional Neural Network (FL-AECNN), a hybrid federated model, is proposed. For unsupervised feature learning, the FL-AECNN combines a Convolutional Neural Network (CNN) classifier with an autoencoder. This combination improves classification accuracy, generalization, and feature representation quality. Furthermore, it works in environment where the data is Non-Independent and Identically Distributed (non-IID). In the model, the CNN is used for supervised classification, while the autoencoder transforms the traffic features into latent features. This model is hybrid due to its two functionalities. For the experiment, a customized Android traffic dataset with ten classes and the ISSC VPN2016 benchmark dataset with five classes are used to test the proposed model. In the experiment, the SMOTE algorithm is employed to balance the classes, and log transformation is used to normalize all the datasets to address the skewed features. The Federated Averaging algorithm, or FedAvg, is used to aggregate the model globally, while the local model is trained independently by the ten clients and the central server. The average training accuracy of the FL-AECNN model is 90.24%, and the range of the testing accuracy of the model is between 77.01% and 82.54%. These results show that the FL-AECNN model performs better in terms of accuracy and consistency compared to the Federated Learning Convolutional Neural Network (FLCNN). These results indicate the possibility of applying federated supervised classification and federated unsupervised representation learning to develop a new method of safe traffic assessment.
Live Virtual Machine Migration Using Xen Server
Live VM migration is an indispensable part of the cloud for load balancing availability, resource management and reduced service disruption. Current migration techniques present a high memory demand, complex page patterns, varying workloads, and aggressive SLAs. We have designed an efficient live VM migration system including hybrid pre-copy and post-copy, workload prediction, and memory optimization. The transfer between pre-copy and post-copy migration can be switched in a dynamic manner depending on real time memory write type, and therefore service disruption and redundant retransmission will be reduced. To this end, we build an LSTM-based model to predict dirty page production and CPU consumption for proactive migration and adaptive phase switching according to the temporal restrictions of these two variables. It also uses clustering of the most modified pages in Lustre data access frequency to lower useless date transmission and speed up the migration. Experiments on a private cloud system under XenServer demonstrate that, as compared to other methods service disruption time is reduced by 55%, total migration time is increased by 25% and improvement of SLA satisfaction rate is recorded up to18%. This is, in a nutshell, an efficient, scalable and low-overhead approach for live migration of VMs over heterogeneous clouds by embracing predictive workload awareness through adaptive memory clustering.
Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring
A major step forward in pharmaceutical production has been the merging of Artificial Intelligence (AI) with the Internet of Things (IoTs), which successfully connects the digital and physical realms. Improving the production process and quality control through the integration of AI algorithms into IoT sensors leads to higher overall efficiency. Digital Twins (DT) are made possible by the creation and implementation of new Industry 4.0 technologies, which help to make manufacturing smarter and more agile. DTs are digital representations of real-world systems that mimic their operation and dynamics. Complete DT implementation in pharmaceutical production has not yet occurred, despite the pharmaceutical sector is currently undergoing a digital transformation to incorporate industry 4.0 and has adopted Quality-by-Design (QbD) programs. Therefore, it is crucial to assess the pharmaceutical industry's progress in implementing DT solutions. Data obtained from digital twins can also provide a full image of a product or process's lifetime, which can help with supply chain management, product quality control, and workflow optimization for manufacturing individual parts. This research proposes a Digital Twin enabled Data Pattern Analysis with Diminutive Difference Trigger using Probabilistic Regression (DT-DPA-DDT-PR) model for accurate monitoring of pharmaceutical production operations. The proposed model when contrasted with traditional methods performs better in pharmaceutical production monitoring.
Review on Blockchain-Based Offline Payment Solutions
Blockchain technology has proven to be a revolutionary base technology for the development of digital payment systems, which include properties like decentralization, transparency, and strong cryptography. However, most existing blockchain-based payment systems require always-connected scenarios, limiting their applicability in environments where no or intermittent internet connectivity is available. Hence, there is increased research in existing studies on blockchain-based offline payment systems, particularly from the viewpoints of financial inclusion and Central Bank Digital Currencies. The objective of this paper is to carry out a detailed review of existing blockchain-based offline payment systems, which includes discussions on design, security, and assumptions. The proposed work will group the prominent existing payment systems along the lines of state channel networks, hardware-based payment systems, and offline token payment systems, which will be analyzed for their pros and cons. In addition, it also raises awareness of prominent security aspects like authentication using cryptography, trusted execution environments, and delayed reconciliation, considering this study to assume certain conditions when examining the offline environment. Finally, based on the major existing work, it will outline important research opportunities in double spending, scalability, privacy, interoperability, and usability. The purpose of this study is, through synthesis, to provide a structured view of the offline blockchain payment platform and help focus R&D efforts on effective, secure, and scalable offline digital payment systems.
A Comprehensive Review of Architectural, Algorithmic, and Technological Innovations for Energy-Efficient 6G Communication Networks
Due to the evolution of 6th generation (6G) communication networks, it has been witnessing an increasing need for ultra-high data rates, massive connection, low latency, and intelligence. However, some tasks need to be performed for fulfilling this demand through preserving the energy consumption. In this article, it is providing an extensive assessment of technological, computational, and architectural developments for optimizing energy efficiency during developing 6G communication networks. In context of architectural advancement, it is assessing intelligent reflecting surfaces, Green cell-free massive MIMO, and hierarchical edge-cloud computing architectures. They help decrease energy consumption during processing, device, and transmission, according to those architectures. The capabilities of AI-driven network management, energy-conscious resource allocation, and reinforcement learning optimization for optimizing energy efficiency in an effective manner through computing capabilities of AI have been described. The role of technological innovations like energy harvesting, integrated sensing and communication, ultra-low-power transceivers, and terahertz transmission in lowering operational network costs has been discussed too. In this review article, it is discussing THz wave communication and Green cell-free massive MIMO technology particularly.
Evaluating Random Forest and Decision Tree Algorithms for Resolving Lexical Ambiguity in the Gujarati Language
Word sense disambiguation is the task of determining the exact meaning of a word based on its context. This task is crucial in natural language processing. The lack of labeled datasets and the complex structure of the language, which includes idiomatic usage and subtle semantic changes, contribute to the poor outcomes of earlier attempts to solve word sense disambiguation in Gujarati. As a result, various models have shown low accuracy. To address this issue, we have created a new dataset that is manually sense-annotated for unclear Gujarati words. The corpus contains 50 ambiguous words, and each word has been assigned to the appropriate context. This makes it a valuable starting point for evaluating supervised learning models. With this newly compiled corpus, we carry out a systematic study of two supervised machine learning algorithms-Decision Tree and Random Forest-using 3-fold and 5-fold cross-validation. Our results show that Random Forest obtains the highest accuracy, highlighting which supervised methods are best suited for this particular task. The main contributions of this work include the development of a much-needed annotated corpus and sufficient evidence to prove that supervised learning can be quite effective in improving WSD for Gujarati when proper data is integrated.
Multi-Scale Semantic Fusion Network with Adaptive Attention for Remote Sensing Image Captioning
The aim of remote sensing image captioning (RSIC) is to obtain insightful and detailed textual description of satellite images and aerial images. However, traditional methods are not able to achieve this aim effectively due to a lack of contextual awareness caused by variations in scale, viewpoint and scene complexity. In this paper, we propose a method, the Multiscale Region-Aware Captioning Network (MSR-CapNet), which helps to achieve the aim of RSIC by generating relevant and semantically correct textual descriptions for scenes in satellite images (and aerial images). We train and test our method for the purpose of RSIC on the RSICD and UCM caption datasets. In our MSR-CapNet method, we have integrated Feature Pyramid Encoding (used for local and global visual characteristics representation), Adaptive Attention (which helps in dynamic prioritization of relevant regions) and Topic-Sensitive Embeddings (to generate semantically consistent captions). To show the effectiveness of the proposed method (MSR-CapNet), we compared it with existing techniques (recent transformer and graph-based baselines) using BLEU-4, METEOR, and CIDEr measures, where it shows consistent improvement over existing techniques.
Explainable Hybrid Artificial Intelligence Boosting–Shapley Framework for Cardiac Disease Diagnosis
Heart disease is the leading cause of illness and death worldwide. Thus, there is an increasing demand for developing appropriate diagnostic techniques. This paper presents an explainable artificial intelligence hybrid model using a combination of SHapley Additive ExPlanations and Extreme Gradient Boosting to perform interpretable feature explanation and heart disease prediction. In this context, a dataset including 4,240 patient records with 15 clinical parameters was employed to validate the proposed model. XGBoost hyperparameters were optimized for the best setting using a grid-based search combined with 10-fold cross-validation. The conducted experiments showed that the proposed approach outperforms traditional classifiers dealing with the same data. Thus, the F1-score, AUC, recall, accuracy, and precision of the proposed methodology are 99.3%, 98.8%, and 0.97, respectively. SHAP analysis provided both local and global feature attributions, revealing that cardiovascular risk is strongly linked to age, systolic blood pressure, smoking status, and cholesterol level. This hybrid design enhances the reliability of predictions and works well in physician decision-support systems for the early detection and treatment of cardiovascular diseases. It also supports clarity in clinical information and renders it more trustworthy.
The Role of Digital Intangible Culture Heritage Resources in Promoting Students' Innovative Ability in Higher Education Courses
The study is aimed at incorporating Web-AI (IESWS-LGBM) systems into the higher education curricula, specifically focusing on the development of creative thinking and innovation based on the use of digital ICH assets. The research was conducted on 52 undergraduates in the experiential learning program at the School of Ethnology and Sociology at Guangxi Minzu University. Additionally, 240 undergraduates from various branches of science, engineering, and arts were taught at a university in Southeast Asia. Specialized innovation courses focused on an AI application that provided audio for the digital assets of ICH. Although the strategy is unique to one institution, it is likely to be more beneficial and assist other colleges attempting to improve student creativity through the application of cultural elements in addition to the primary focus of the strategy. The research employed a mixed-methods design including pre-and post-tests, behavioral observation, questionnaires, interviews, and evaluations of projects. Quantitative data indicate a 26.4% increase in cognitive knowledge of ICH. Behavioral data shows that 92% of students reported increased levels of cultural motivation with 86% of the students being actively engaged. One of the most important aspects of the data is that 76% of the students in rubric-based exams evaluated the creativity criterion. The proposed model IESWS-LGBM has also demonstrated an increased accuracy (91.5%) in evaluating students’ creativity compared to previous models. These results suggest that ICH with the help of technology is quite effective at promoting critical thinking, originality, and multicultural learning among students in institutions of higher education.
Design and FPGA Based Realization of an SMC-ESO Enhanced Nonlinear Fractional Order PID Controller for BLDC Motor Speed Control
Extended State Observe Nonlinear Fractional-order PID (NLFOPID) algorithm and Sliding Mode Control (SMC) are utilized for controlling the speed of a BLDC motor in this research study. The slow response time of the traditional PID controller makes it inefficient. FOPID controller is optimized to control the speed of the BLDC motor utilizing the SMC-ESO alongside it to overcome the shortcomings of traditional controllers. BLDC motors are suitable for many applications because they use permanent magnets to provide high torque over a broad range of speeds. The FOPID controller offers improved precision in control, SMC provides immunity to disturbance, and ESO provides real-time estimation of the state. The controller under consideration achieves ±1 RPM constant speed accuracy, under 5 % overshoot, and approximately 0.3 s settling time with 2 150 look-up tables, 1 800 flip-flops, 10 DSP slices and 2 BRAMs in the FPGA, demonstrate better accuracy and efficiency compared to standard PID and FOPID controllers. For a real-time system, it is implemented on a Field Programmable Gate Array (FPGA) due to its high processing capacity.
Proportional Integral Derivative Controller
Energy-Efficient Neuromorphic Architectures Enabled by Resistive Memory
Their excellent performance and compatibility with CMOS technology, while the OXRAM (Oxide-based CMOS Resistive Random Access Memory) technique used in CMOS transistors presents challenges in terms of device unpredictability and scalability, also offers potential advantages such as higher endurance, lower power consumption, and quicker reading and writing operation speeds when compared to traditional Flash memory. The inability of conventional SRAMs to store data after powering off limits their use in battery-operated mobile devices and other applications where non-volatility related to zero leakage currents is required. In the article, a new OXRAM-based Non-Volatile SRAM (NVSRAM) device is presented. It is suggested to compare the performance of SRAM with NVSRAM at the memory and cell levels. Learning is crucial for the brain's ability to adapt to changing conditions. A synaptic connection table in an external memory at a local routing node is used to learn a rule in the address domain for neuromorphic architecture. A number of parameters are compared, including design complexity, leakage current values (SRAM cells are 3.4µA, 7.4nA) and (NVSRAM-based OXRAM are 2.7 µA, 5.9nA) at 180nm and 90nm, and energy saving or power usage values (SRAM cells cell are 5.5 µA, 10.5nA) and (NVSRAM based OXRAM are 4.9 µA, 9.8nA) at 180nm and 90nm. The circuits that are being described can be realized using far-above ground voltage CMOS Cadence tools at 180 nm and 90 nm.
Static Random CMOS Access Memories (SRAMs)
UPS-Tailored Next-Generation Battery Technologies: Variability, Sustainability, Comparative Analysis and a Technology Roadmap
The global uninterruptible power supply forecast market is estimated at nearly USD 4.04 billion in 2024 owing to the growth in demand at data centers and other crucial sectors. Despite the great deal of information available in battery research, most of the literature on uninterruptible power supply does not consider the variability (temperature, depth of discharge, duty cycle) and mode of operation (grid-interactive vs. Standalone) performance during other operational modes. This review overcomes these deficiencies by providing three contributions: (1) variability in uninterruptible power supply specific quantitative performance metrics; (2) a growth map predicting the adoption of various battery chemistries in UPS on a short, medium, and long-term basis; (3) a sustainability assessment through the lens of primary materials, recyclability, and costs from the life cycle. Representative outcomes include: low-cost standby solutions from VRLA batteries, but limited cycle life (500–1,500 cycles); continuous UPS applications are dominated by LFP and LTO chemistries, which have a high tolerance to deep cycles; Li–S batteries have a high energy density (350–600 Wh/kg), but poor cycle life (<1,000); flow batteries have limited energy power density, but excel in longevity (>10,000 cycles) and are still too bulky for compact uninterruptible power supply systems. These conclusions provide a reasonable assessment of the cross technology trade-offs and the underlying research gaps to derive a pathway towards next generation reliable UPS batteries.
AI-Driven Malware Detection and Prevention using Hybrid Machine Learning and Blockchain for Secure Cyber Threat Intelligence
The ever-evolving cyber threat landscape has enabled sophisticated and intelligent malware detection techniques. False positive rates and inadequate attack pattern adaptation are common limitations of conventional intrusion detection algorithms. In an effort to improve security, this work offers a combined malware detection method based on artificial intelligence. Additionally, it integrates deep learning and machine learning with blockchain technology. By using Random Forest and Long Short-Term Memory for feature selection and anomaly detection, the suggested system can identify cyber threats with greater accuracy. A blockchain ledger also facilitates the recording of attack indicators, enhancing threat intelligence. The proposed method outperformed the standalone ML/DL results with 99.7% accuracy, 99.5% precision, 99.4% recall, and a 99.5% F1-score on the chosen dataset. Blockchain technology further enhances incremental trust in security by ensuring confidence in cybersecurity agencies by eliminating data manipulation.
Effectiveness of Farmers’ Professional Cooperatives in Helping Rural Revitalization Using Big Data Analysis
Farmers' Professional Cooperatives have been increasingly recognized as organizational forms facilitating economic development, agricultural modernization, and rural social capital construction as part of China's national rural revitalization strategy. Based on a big data study method, the study estimates the performance trajectory of FPCs and their impacts on social capital construction, income increase, poverty alleviation, agricultural restructuring, and employment in urban-rural areas. A combined approach of the Back Propagation Neural Network and the Mayfly Optimization Algorithm was put forward in an attempt to promote feature selection and accuracy of prediction for the intricate and multi-layered socioeconomic case. Big data and the proposed predictive model will be compared by the proposed study using the data of 820 members in 14 cooperatives. The outcome reveals that the suggested model achieved a 93.4 % success rate, a 26.4 % increase in earnings, an 18.7 % rise in technology uptake, and a 31.2 % rise in training attendance and engagement. Among cooperative members, descriptive statistics demonstrated a significant improvement in household income, market access, training engagement, and the application of technology. To facilitate generalization, the model was trained on 70% of the dataset, tested on 15% of the dataset, and validated on the other 15% through the use of cross-validation methods. The proposed model was found to be more accurate than traditional models with a 93.4% correctness rate and an RMSE value of 2.13. Other significant factors determining performance in cooperatives, including farm size, years of experience, and education, were also discovered by the model. FPCs strategically facilitate resource concentration, implementation of policies, and rural institutional integration along with enhancing farmers' economic welfare, as revealed by the findings.
An Optimized Inter Planetary File System Framework Integrating Federated Learning and Blockchain to Bridge Interoperability and Latency Gaps in Electronic Health Record Systems
In the current era, Electronic Health Record (EHR) systems are widely adopted to store and manage patients' medical information in digital form, as they allow doctors and healthcare professionals to view a patient's complete medical information in an instant. The use of EHR makes healthcare faster, more accurate, and safer, and is therefore an important part of the future of digital healthcare. However, it faces many obstacles in terms of seamless integration (interoperability) and low-latency data acquisition, which directly impacts real-time medical decision-making and the quality of patient care. Integrating Blockchain Technology for EHR management with the InterPlanetary File System (IPFS) and federated learning can improve system performance by reducing the high latency of data retrieval, despite challenges like non-Independent and Identically Distributed (IID) data, client drift, and intermittent connectivity across hospital nodes. To address these challenges, we introduced Adaptive Contextual IPFS Retrieval (ACIR) and asynchronous aggregation. We tested our framework in a simulated environment representing 1,000 hospitals, and the results were promising. Data could be retrieved 65% faster, model training finished 38% sooner, and the system's overall performance improved by 42%. Most importantly, we achieved these improvements while maintaining full compliance with HIPAA and GDPR data privacy standards.
Adaptive Contextual IPFS Retrieval (ACIR)
Health Insurance Portability and Accountability Act (HIPAA)
General Data Protection Regulation (GDPR)
Enhancing Insecticide Classification Accuracy with Modified ChemNet Architecture
Proper detection of insecticides is also vital since it is the determinant of agricultural safety, efficiency, environmental protection and environmental compliance. In this paper, a modified ChemNet architecture is introduced, which is modified to the classification of insecticide in fruits. Compared to the traditional ChemNet, which leaves out chemical priors and residual learning, the proposed model has certain improvements: Squeeze-and-Excitation (SE) blocks for adaptive channel recalibration, residual blocks coupled with SE units to improve feature extraction, Self-Attention mechanism to learn long-range dependencies, and the swish activation function to support gradient flow and non-linear representation. Also, the network applies progressive dropout, early stopping, and class-weighted loss to address overfitting and unbalanced samples. The validation and training of the proposed model were trained using the Kaggle Banana Insecticide Dataset comprising a total of 6,103 images. The data is grouped into six distinct categories: monohigh (high level of banana treated with mono-type insecticide), monolow (low level of mono-type insecticide), novahigh (high level of nova-type insecticide), novalow (low level of nova-type insecticide), natural (banana samples that were never treated) and rotten (samples of banana that were biologically spoiled). This composition ensures that the dataset encompasses a wide range of levels of insecticide contamination and natural spoilage, enabling a strong foundation for training and evaluation. This will ensure the quality of the model in the classification of the data. The experimental findings show that the modified ChemNet achieves an overall classification accuracy of 81.02% and demonstrates good generalization across the chemically heterogeneous classes. These outcomes indicate the effectiveness of the proposed modifications to transform ChemNet for image-based insecticide detection in agricultural images and suggest its potential application as a tool for food safety monitoring that can be expanded.
A Comprehensive Review of Blockchain-Enabled Open-Source Tools for Managing Electronic Health Records (EHR) in Hospitals
Currently, blockchain technology is becoming the potential innovation within a healthcare sector. This systematic review studies the literature regarding blockchain and blockchain adoption in healthcare with an emphasis on hospital Electronic Health Record (EHR) management. This study presents two objectives - identifying gaps in the current research pipeline and suggesting future research recommendations. The first task as part of the study is to analyze the opportunities and challenges associated with applying blockchain technology in the healthcare sector. To accomplish these objectives and assess the position of blockchain technology in the field, a systematic literature review was performed using credible scientific databases and conference papers. Some of the terms from the study glossary that were used as literature search terms included "Blockchain," "implementation," Of the relevant articles originally selected, 32 articles were deemed relevant of which 26 articles contained the elements necessary to conduct factor analysis. The authors of the selected articles presented the analysis of a framework for EHR management in 22 of these articles, that included classification, implementation forms and research gaps. This review shows that there are potential advantages to using blockchain technology in healthcare notable benefits include better data security, improved interoperability, and enhanced patient privacy. A transaction in Blockchain (Ethereum) cost 8 US cents. The evaluation of performance included a designed assessment and metrics that includes, 18.29 s execution time, throughput with 100 to 500 users for a period of 10 to 35, and a latency of 14ms. Among the very important contributions of this review was identifying existing research gaps and providing an exploratory analysis to inform future research efforts. By identifying these gaps, researchers can continue to investigate innovative blockchain applications in healthcare and apply its implementation across a range of clinical scenarios. As part of using blockchain technology to implement EHR management; this systematic review adds to existing scholarship on implementing blockchain technology in healthcare.
An Effective Multi-Patch Deep Learning Method for Mitosis Detection in Breast Histopathology
Within the realm of computational pathology, the detection of mitotic cells poses a formidable challenge. Many existing approaches rely on hand-crafted features, which often result in poor generalization, as their performance degrades across different tissue types, staining processes, and the various scanners used for digitizing whole slide images. The Multi-Patch Mitosis-Detect (MPMD) framework is suggested in the proposed work to detect mitosis from histology images. To identify mitotic reference regions, the proposed MPMD framework uses the detection module for segmentation by utilizing the Recurrent Residual Convolutional Unit (RRCU). The classification model then employs Inception Recurrence Residual Convolutional Neural Networks (IRR-CNN) to validate the mitotic regions. Furthermore, a novel confidence analysis and the MPMD technique are combined to improve the performance of the detection in the testing phase. The novelty of the proposed multi-patch approach is that: (a) Mean Squared Error (MSE) loss is used instead of Dice Coefficient (DC) loss for both training and testing; (b) Global Average Pooling is used in place of fully connected layers in the classification model to reduce the number of network parameters. Experimental findings demonstrate the performance improvement of the proposed approach compared to existing state-of-the-art methodologies.
Unsupervised Learning with Spiking Neural Networks for Image Classification Tasks
Artificial neural networks based on sigmoidal neurons have achieved undisputed performance in various tasks, including image recognition and classification. The search for more biologically plausible artificial neuron models led to the creation of pulsed models. The new way of encoding information is through discrete pulses, and the timing of these pulses is important for the result. This work proposes the creation of a neural network with pulsed neurons for the task of image classification. The network uses cell models more similar to those found in animal brains, communicating through spikes and relying on a stochastic component for pulse generation. It also applies STDP as an unsupervised learning rule, very similar to human learning. Experiments were run using various parameter sets to study the network's dynamics in the image classification task. The results obtained were analyzed, and their performance indicates a promising method capable of good performance on three known image databases (Caltech 101, ETH-80, and MNIST). The database 1 achieved a classification accuracy of 87%, database 2 achieved 77% on ETH-80, and database 3 achieved 86% on MNIST respectively.
An In-Depth Comparative Study of Adaptive k-Anonymity Methods for Streaming Data Privacy
The real-time data is growing extensively due to the immense use of numerous web applications, IoT devices, social media, and network-based applications. This online streaming data, characterized by its volume and velocity, is expressed as big data. While it is accessible for business analytics and research purposes, it can often sacrifice individual privacy. There are different traditional approaches, such as k-anonymity, l-diversity, and t-closeness, that exist to safeguard individual privacy by making each data record indistinguishable from at least k other records. The dynamic nature of real-time stream data makes these methods difficult to apply. However, various research shows that modifications to these methods can effectively protect individual privacy in streaming data. This paper presents a comprehensive review of k-anonymity-based techniques that adapt sliding window models, clustering approaches, and other variations to efficiently protect data privacy while maintaining k-anonymity without compromising data utility. The review discusses the challenges faced in protecting stream data privacy and concludes with research directions to enhance these methods for adaptive and scalable privacy-preserving mechanisms for streaming data.
Neuroplastic Fuzzy GCN-LSTM: A Hybrid Graph-based Deep Learning Model for Accurate Battery SoC Prediction
Accurate prediction of the State of Charge (SoC) in batteries is critical for the efficient and safe operation of Electric Vehicles (EVs). In this work, a novel hybrid neural architecture called Neuroplastic Fuzzy GCN-LSTM is proposed for accurate SoC prediction. It involves fuzzy logic, dynamic graph modelling and graph convolutional networks (GCNs) to effectively handle the complex patterns of battery data. Initially, time-series sensor inputs of voltage, current, and temperature are normalised and transformed into fuzzy linguistic representations to address uncertainty and improve interpretability. Then, a dynamic graph is constructed to capture inter-feature dependencies via a Gaussian similarity kernel. These graphs are processed through spectral GCN layers to extract spatial correlations. Finally, the neuroplastic LSTM (NP-LSTM) is applied for SoC prediction. Unlike conventional LSTMs, the NP-LSTM adaptively modulates its memory cell updates using error-based synaptic plasticity, allowing the model to emphasise learning from past prediction errors. The performance of the Neuroplastic Fuzzy GCN-LSTM model is verified using NASA’s Prognostics Center of Excellence data sets in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) compared to models. The Neuroplastic Fuzzy GCN-LSTM model achieved the best performance among all competing models, with the lowest MSE (0.009378), MAE (0.086234), and RMSE (0.096839).
Early Diagnosis of Alzheimer’s Disease Using Hybrid Deep Learning Model based on Integration of EfficientNet and LSTM
Early detection of Alzheimer’s disease (AD) is crucial for prompt clinical intervention and enhanced patient outcomes. Here, we introduce a hybrid deep learning design that leverages EfficientNet-B6 spatial feature extraction capabilities and integrates the Long Short-Term Memory (LSTM) neural network to capture the sequential patterns in MRI scans for Alzheimer’s classification. The model is developed to categorize brain MRI scans into four diagnostic stages: non-demented, very mild demented, mild demented, and moderate demented, utilizing the publicly accessible augmented Alzheimer MRI datasets. A preprocessing step is applied, comprising scaling, normalization, and data augmentation, to ensure standardized and high-quality inputs to the network. The experimental findings for accuracy are presented, while we outline the conceptual basis of the hybrid model and its potential to address shortcomings of current methods, like inadequate spatial-temporal integration, scalability, and interpretability. The proposed architecture aims to enhance diagnostic results by leveraging EfficientNet-B6 multiscale spatial feature learning and LSTM temporal modeling. The proposed hybrid architecture evaluated on 80:20 training and validation dataset split, achieving a classification accuracy of 98.90% on a benchmark dataset, which shows the potential of the proposed hybrid architecture. This study lays the foundation for a comprehensive, interpretable, and scalable deep learning model for early AD detection from MRI scans.
Deep Learning based Gait Analysis for Early Detection and Progression Monitoring of Alzheimer's Disease
Neurological diseases present a considerable impact on individuals by affecting their quality of life leading to disability and mortality. Gait represents the pattern of human walking, which serves as a chief indicator of health status, functional impairment, and treatment prognosis. Gait analysis (GA) plays an essential part in the assessment of neurological disorders, with patterns helping as reliable factors of potential disorders in the future. Alzheimer's disease (AD) adjacent profound concerns across universal healthcare networks demanding timely monitoring and suitable intervention. In this analysis, we present an innovative approach to model the time-based dependence in AD progression by integrating gait inspection with cognitive performance metrics and functional neuroimaging using recurrent neural networks (RNNs). By encompassing LSTM, the longitudinal nature of AD data allows movement patterns to be utilized as a supplemental marker to capture subtle changes in cognitive function as well as mobility over time. By inspect consecutive data gathered from individuals at risk or diagnosed with AD. Our approach aims to forecast future cognitive decline, with biological markers indicative of disease progression helping in early diagnosis. With accuracy, recall as 0.98, precision, F1-Score and AUC-ROC as 0.99 our integrated framework makes use of an indigenous dataset to offer a holistic understanding of the multifaceted dynamics in AD progression, paving the way for personalized care and treatment strategies tailored to suit individual cognitive and motor impairments.
AI Based Novel Model for Prediction of Cyber Security Attacks Using KNN Algorithm & XDR
Cyberspace is treated as a fourth dimension of modern-day warfare apart from land, air and sea. Solutions are developed to provide cybersecurity to computer systems, but every time the attacker tries new methodologies and overcomes the security systems. Such a set of tools and solutions also consists of Log Analysis solutions. It is a proven fact that, Log Analysis helps to predict and prevent cybersecurity attacks. However, very few research attempts have been made regarding the application of Artificial Intelligence to Log Analysis (especially Extended Detection and Response (XDR) Log Analysis). Therefore, in this paper we propose and implement a K-Nearest Neighbors (KNN) algorithm based preventive and predictive system. The K-Nearest Neighbors algorithm is a non-parametric supervised learning algorithm. Extended Detection and Response (XDR) is one of the modern solutions that has the capability to collect and process data from various sub-systems connected in a given network and is an information goldmine from the cybersecurity audit perspective. In this paper, we propose to use the KNN algorithm over the XDR. Therefore, the proposed novel model includes steps such as; Input the data, checking for “missing values” and “duplicate entries”, identifying available “classes” and optimizing them to two or three Major Classes, then performing “label encoding” and creating the “correlation value-based matrix”. Further, we find out “Positive” and “Negative correlation values” and discard the rest values, then select the features which has highest correlation values. Later, we apply the scikit-learn class standard scaler method to scale the features to centre the data around a mean of "0" and a standard deviation of "1." Finally, apply the KNN classifier with Optuna to identify the K-nearest neighbor. This will generate the final output, which will define, whether the given log entry is of “Suspicious Class” or “Not Suspicious Class”. The Suspicious Class XDR log entries will be dealt with separately, as they might indicate a potential risk or incident of compromise (IOC). The proposed novel experiment has been tested on the standard icrosoft based GUIDE dataset and a locally generated in-lab dataset. The Microsoft GUIDE XDR data contains 13 million pieces of evidence across 33 entity types, 1.6 million alerts, and 1 million well annotated incidents collected from 6,100 organizations. In both cases, our experimentation has successfully achieved a result of 93.85% accuracy in predicting cybersecurity attacks.
Cross-Lingual Attention-based Mechanism for Speech Emotion Recognition
Speech emotion recognition is one of the most emerging areas for emotion detection that may fall within the scope of affective computing. In this particular case, emotional speech files of spoken words delivered during verbal communication are of interest. The emotions of speech are investigated through sound and emotion in speech and are modeled through machine learning. Through machine learning, we performed a series of experiments on datasets like RAVDESS, TESS, SAVEE, and EMO-DB, which lean toward the objective that a Recurrent Neural Network (RNN) and (CLAF-SER): The Cross-Lingual Attention-Based Adversarial Framework for SER would be able to detect and classify such emotions as sadness, anger, happiness, neutrality, and fear. Features such as MFCC, LPCC, pitch, energy, and chroma were extracted before implementing the RNN. Through this model, TESS achieved the highest accuracy among the other datasets. However, CLAF-SER gives the best performance when all datasets are combined.
CLAF-SER (Cross-Lingual Attention-based Adversarial Framework for SER)
SAVEE (Surrey Audio-Visual Expressed Emotion Database)
RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song)
EMO-DB (Berlin Database of Emotional Speech)
MFCC (Mel-Frequency Cepstral Coefficients)
LPCC (Linear Prediction-based Cepstral Coefficients)
FLIDS: Fuzzy Logic-based Framework for Interpretable Image Manipulation Detection
This work introduces FLIDS (Fuzzy Logic-based Image Distortion Scoring), an interpretable and efficient system for image tampering detection based on hand-crafted features and fuzzy logic. FLIDS combines JPEG artifact analysis, edge consistency, co-occurrence entropy, and CFA disparities into a fuzzy rule-based system for assigning a tampering confidence score. In contrast to black-box deep learning systems, FLIDS prioritizes transparency and generalizability. Tests on CIFAR-10, MNIST, ImageNet Subset, and Deepfake datasets indicate FLIDS attains competitive accuracy compared to ResNet-18, Autoencoder, and hand-designed JPEG detectors in the majority of instances. FLIDS achieves 93.5% and 91.8% accuracy on CIFAR-10 and ImageNet Subset, respectively, as well as a balanced 90.2% on deepfake datasets. These findings point to FLIDS as a promising, interpretable solution to intricate deep learning systems in image forgery detection.
Automating Histologic Assessment of Prostate Cancer with a ResNet50-Based Hybrid Vision Model
Precise Gleason grading of prostate biopsy specimens is vital for determining the appropriate clinical management of prostate cancer. However, traditionally, subjective manual evaluation by pathologists is susceptible to inter-observer variability, contributing to variable diagnoses and a likelihood of less-than-optimal treatment decisions. Therefore, we present a hybrid deep-learning architecture, wherein a modified ResNet50 convolutional backbone has been amalgamated with a Vision Transformer (ViT) module with the aim of automated and standardized Gleason classification. The ResNet50 portion consists of 50 layers with bottleneck residual blocks inserted for texture and glandular pattern localization in contrast-enhanced histopathological images. The spatially rich feature maps are then forwarded to the ViT module that extracts long-range dependencies and contextual relationships across image patches through a combination of multi-head self-attention mechanisms and transformer encoders. In this manner, a combination of local feature extraction and global attention facilitates the model's learning of subtle morphological variations that are crucial for the differentiation of six different Gleason patterns on a large scale. The model was trained and validated on a balanced multiclass dataset of prostate biopsy images, achieving a classification accuracy of 99%, which is better than several existing deep-learning baselines. This hybrid architecture aims to enhance diagnostic consistency while providing a realistic, interpretable framework for implementation in clinical workflows geared toward high-throughput prostate cancer screening, especially in resource-limited healthcare settings.