Journal of Trends in Computer Science and Smart Technology
Deployment-Oriented Evaluation of Healthcare Reimbursement Cost Prediction
The use of healthcare cost prediction models to assist with resource allocation and risk management is on the rise, but their use is limited by the requirement for reliable, easily interpretable explanations. Although there are multiple methods of providing these explanations, their operational use in healthcare settings remains inadequately evaluated. This research analyses four of the available explainable models (i.e., Permutation Feature Importance, Tree Importance, Local Interpretable Model Explanations, and Shapley-based explanations) using a Random Forest healthcare cost prediction model developed on a real-world dataset (comprising 2,302 aggregated patient segments and 54 features) with a coefficient of determination of 0.9957. The analysis used three criteria that are critical to the deployment of these models: steadiness of explanation under data perturbation, correspondence of feature importance across methods, and computational latency. Results show that each of the global explainable models has relatively high steadiness and relatively low latency, making them applicable for real time and regulatory use; however, the local explainable models provide more intuitive instance-level explanations at the expense of lower steadiness and greater computational demands. A tiered deployment framework for method selection is proposed based on these results as guidance for selecting methods based on clinical, regulatory, and operational needs. This research provides insight into the practical use of explainable healthcare cost prediction systems within real-world environments.
Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
Adaptive Gated Multimodal Fusion for Robust and Generalized Human Activity Recognition
In this paper, an adaptive gated multimodal fusion framework for generalized and robust multimodal Human Activity Recognition (HAR) systems with heterogeneous sensing modalities like skeletal pose estimation and inertial measurement units (IMUs) is proposed. The proposed framework employs deterministic data harmonization through anterior harmonization and a reliability-based gated multimodal fusion mechanism to improve the robustness and generalization capability of multimodal HAR systems. The proposed gated multimodal fusion mechanism has been mathematically derived to approximate the inverse-variance weighting mechanism to obtain stability in the presence of modality-dependent noise and avoid posterior domain adaptation techniques. To improve temporal alignment between multimodal data streams, frequency domain analysis has been used to justify resampling at a unified 30 Hz rate to meet the Nyquist criterion. The proposed framework has been evaluated using the NTU RGB+D 120, UTD-MHAD, and PAMAP2 datasets with statistically significant results over static baselines (p<0.05, d=2.1), and low computational costs to meet edge-constrained IoT sensing requirements.
Hybrid Graph Mamba Framework for Histopathological Image Classification
Digital pathology has already been shown to be useful in disease diagnosis; however, the computational power required to use high-resolution digital pathology is substantial, making it unrealistic in many clinical laboratories due to limitations in hardware capabilities. Accordingly, this paper presents a new architecture, MFLA-Graph-Mamba-NCA, which addresses these limitations. The main point of MFLA-Graph-Mamba-NCA is to develop a lightweight model for tissue classification while maintaining speed and optimal accuracy. Four complementary learning paradigms are incorporated into the architecture: a morphological frontend, a frequency-domain branch based on Functional Kernel Adaptive Networks (FunKAN), feature stabilization over time using Liquid Neural Ordinary Differential Equations, and global sequence modelling using a Graph-based Mamba module. The model was tested on a harmonized benchmark of 125,000 images, based on the NCT-CRC-HE-100K and LC25000 datasets, and divided into 12 different biological classes to expressly avoid cross-dataset lab leakage. It has been shown to provide 99.89%accuracy and a Macro: F1 score of 99.78%. These techniques were condensed to 1.46 million parameters, forming a hybridized model that can be deployed to standard clinical workstations.
Functional Kernel Adaptive Networks (KAN)
Enhancing Rail Surface Defect Detection Using a Hybrid YOLOv11–Vision Transformer Framework
Railway track surface defects cause severe challenges to the safety of humans, trains, and transported goods. Modern YOLO-based detectors efficiently detect rail surface defects in real time. However, they mainly focus on local features and also confuse defects that look similar. On the other hand, Vision Transformers are good at modeling global context using self-attention, but they do not localize objects properly when used alone. To overcome these issues, this study introduces a hybrid YOLOv11 and Vision Transformer (YOLOv11a and ViT) framework for enhanced rail surface defect detection. The approach integrates a lightweight Vision Transformer module into the YOLOv11 backbone so that the model learns both detailed local features and captures global dependencies. Experiments are conducted on a merged public railway surface defect computer vision dataset comprising 8,177 labeled images of five defect categories. The results show that the new method achieves an mAP@0.5 of 0.951 with a precision of 0.899 and recall of 0.921, outperforming the baseline YOLO model. The most significant advancements have been made in identifying elongated defects such as cracks and light bands.The framework also maintains real-time performance, making it practical for use in railway safety inspections.
Guardian-Based Anonymous Password Management with Privacy Preservation Using Threshold Cryptography
Most password managers are designed as a compromise between providing a secure way to handle difficult login credentials and creating a system that keeps user information private. Most password managers store users’ sensitive information in a central location, leaving users vulnerable to hacking attacks. The proposed work creates a better way to manage passwords and provide more protection to users based on their private information through a new concept called Guardian-Anonymous Password Management (GAPM). The idea is to create a unique architecture that stores passwords in decentralized locations using guardian anonymity, creating a hybrid architecture of secret sharing with post-quantum encrypted wraps. Accordingly, GAPM separates the act of recovering user passwords from a person, using a set of anonymous guardians who securely recover users’ passwords without putting any of them at risk of being hacked or located through social engineering techniques like phishing. This is achieved by using a Shamir-style secret sharing scheme combined with verifiably reassured commitments, where none of the guardians know each other, and they are required to reach a certain threshold of agreement to combine their shares into an easily accessible password recovery key. The GAPM system supports multiple guardian sets, allowing participants to be added or removed, and there is no need to reissue all the shares each time users make a change. The user can also change the recovery threshold in real-time. Finally, the shares are further secured through the use of a post-quantum Key Encapsulation Mechanism (KEM) to ensure that, no matter what kind of attack (classical or quantum), the password recovery process will remain strong and secure.
Dual-Metric Detection of Synthesis-Masked Hardware Trojans in AES-256: Correlating Power Signatures with Switching Activity
Hardware Trojans can be accidentally hidden due to synthesis optimizations performed by electronic design automation tools in cryptosystems, leaving important detection gaps in the process. In this study, the impact of synthesis on the power signatures of AES-256 cores has been analyzed based on power/net switching correlations. Four hardware Trojans have been synthesized targeting the 7nm Versal technology of Xilinx and have been simulated over the course of 10,000 clock cycles across 714 different key/plaintext combinations. The results showed that three hardware Trojans exhibited unexpected negative power deviations, although they incorporated malicious logic in their structures. Two separate categories of synthesis interactions can be observed from switching distribution histograms over 646 nets and KL divergence statistics (0.03–0.42): synthesis-masked dormant Trojans with lower toggles and leftward-shifting distributions, and synthesis-neutralized active Trojans with 40% more active nets and lower power. This two-fold correlation method allows for detecting synthesis-affected Trojans that cannot be identified through conventional power-overhead analysis methods, achieving reproducible structural detection supported by dual-metric cross-validation (Z-scores -0.62 to -0.77), despite individual signatures falling below the conventional 3σ detection threshold.
Machine Learning Approach for Adaptive Data Protection in Document Database Systems
With the growing dependence of cloud applications and enterprise organizations on NoSQL databases, it is necessary to ensure data protection while maintaining efficiency and performance. Traditional static encryption systems deliver strong regulatory protection but are unable to react to changing contexts in dynamic and zerotrust environments, which limits their ability to address abnormal behaviour, malicious insiders, and advanced attackers. This work proposes AdaptiCrypt-ML, a lightweight proxy based on machine learning, which aims to implement domain-level adaptive encryption in NoSQL database security systems. The framework utilizes the LightGBM model to classify 14 contextual, behavioural, and data-sensitivity features to determine immediately the most appropriate encryption level across four security categories. When data is entered, the encryption level is dynamically determined according to the risk level, whereas a risk-based decryption policy controls the extent to which data is revealed when retrieved. Empirical results, derived from a statistically validated synthetic dataset of 50,000 examples, demonstrate strong predictive performance, with an overall accuracy of 99.1%, an F1-macro score of 0.963, and a low generalization gap of 0.0018. The average inference time ranged between 0.5 and 0.8 milliseconds, and the total response time stabilized at 3.25 milliseconds (P95 = 4.10 milliseconds), with an average of 3,120 queries per second. A 5% noise robustness test validated 96% performance stabilization. These findings emphasize the possibility of integrating context-aware adaptive encryption into NoSQL frameworks without sacrificing real-time requirements.
Federated Attention‑Capsule CNN with Bio‑Optimization for Coronary Artery Disease Screening
Coronary artery disease (CAD) remains the leading cause of death around the globe and hence requires reliable, non-invasive, and secure diagnostic methods. In order to diagnose CAD in its early stages, we present a novel architecture of a Federated Attention-Capsule Convolutional Neural Network (FAC-CNN) integrated with Bio-Inspired Optimization Algorithms (BIOA) in dealing with multiple modalities of physiological data such as Electrocardiography (ECG), Photoplethysmogram (PPG), and Blood Pressure (BP). The model is evaluated using unimodal (only ECG signals) and multimodal physiological datasets to validate the proposed algorithm for different data distributions. To improve accuracy, the architecture of FAC-CNN uses capsule networks to recognize the hierarchical structure within the data fed into it. Through attention mechanisms, the network is capable of selecting the most important features related to cardiac diseases. To ensure data security, the federated learning (FL) method is applied as a solution to the problem of local model training using patients' private data stored in edge computing devices. A novel hybrid optimization method combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is used to update learning rates, the total number of capsule layers, and routing iterations. Three databases, including the PTB diagnostic ECG database, the MIT-BIH Arrhythmia database, and the MIMIC-III Waveform database, which comprises authentic ICU recordings of ECG, PPG, and blood pressure data from more than 40,000 patients were employed to evaluate the model. It can be seen from the results that FAC-CNN outperformed the other models used in the experiment, including CNNs and LSTMs by 4-7%, obtaining a mean accuracy of 97.4%, an F1-score of 96.8%, and an AUC score of 0.985. Moreover, the scalability of the proposed algorithm for applications in remote healthcare settings improved, and the more effective training method contributed to time-savings for the training procedure, decreasing it by 18.6%.
A Slot-Aware Semantic Signaling Framework for Multi-intent Classification
Multi-Intent Detection (MID) and Slot Filling (SF) are fundamental tasks in Natural Language Understanding (NLU) for goal-oriented dialogue systems. Despite advances in various joint models, which improve performance by learning interactions between intents and slots, existing models might not adequately capture intricate and complex relationships between intents in multi-intent utterances, especially if slot-level semantic information is not adequately utilized during intent detection (ID). To this end, this paper proposes a novel framework, namely Slot-Aware Semantic Signaling for Multi-Intent Classification (S3MIC), which is a joint framework that leverages slot-level semantic information to improve intent prediction in multi-intent dialogue systems. We conduct experiments with the proposed framework on benchmark datasets, namely MixATIS and MixSNIPS, and report consistent improvements over existing benchmark models in terms of Slot F1, Intent Accuracy, and Semantic Frame Accuracy (SeFr Acc). Specifically, our proposed framework achieves 52.5% SeFr Acc on MixATIS and 86.28% SeFr Acc on MixSNIPS, thereby validating the efficacy of slot-aware semantic signaling in improving joint MID and SF performance in goal-oriented dialogue systems.
Emotion and Cognition Based Mental Health Analysis from Social Media
Social media websites are popular, and it is now possible to collect a significant amount of behavioral data in real time. This has made it possible to conduct research on mental health. This study proposes a machine learning framework to identify and predict mental health issues, such as emotion and cognition, related to social media with respect to depression, anxiety, and bipolar disorder. In this study, three social media datasets were used, each with more than 1.6 million posts: Sentiment140, Twitter Depression Dataset, and Facebook Sentiment. The data was tokenized and lemmatized, and stopwords were removed. We used BERT, a transformer model, to view the data using emotional traits. This was done using the sentimental polarity score and emotions such as grief, anger, joy, fear, and insignificance. We used Latent Dirichlet Allocation (LDA) and Linguistic Inquiry and Word Count (LIWC) to infer cognitive distortions and psychological indices. We used Recursive Feature Elimination (RFE) for feature selection and a Random Forest classifier to predict mental health across multiple classes. The proposed method is more effective than deep learning and machine learning techniques, achieving an accuracy of 95.0%, precision of 94.0%, recall of 93.7%, F1-score of 94.8%, and AUC-ROC of 98.9%. The sentiment polarity score improved the prediction by 28%, the emotional scores improved the prediction by 35%, the cognitive themes improved the prediction by 22%, and the LIWC features improved the prediction by 15%. The results indicate that the use of cognitive linguistics and emotional representation facilitates the identification of early symptoms of severe depression and bipolar disorder.
Graph Neural Network and Reinforcement Learning Framework for Test Case Prioritization, Selection, and Reduction
The process of regression testing is generally done under critical time and resource constraints. In the existing approaches, Test Case Prioritization (TCP), Test Case Selection (TCS), and Test Suite Reduction (TSR) have been treated as different optimization problems. This is based on simple heuristics that do not use valuable information that can be extracted from the program under test. Moreover, they do not allow for making internally consistent budget-conscious decisions. This paper introduces a unified approach that combines various variants of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for the solutions of TCP, TCS, and TSR. In our proposal, the representation of regression artifacts such as test cases, code entities, and faults is used as nodes in the typed graph, while the edges are used to show the relations between these artifacts. The relation-aware GNN is used for generating test case embeddings, reflecting the distance between the test cases and the changed areas as well as the areas known to have issues in the past. The actor-critic RL agent uses these test case embeddings and the budget to decide whether to run, skip, or discard test cases. The performance of our proposal outstrips coverage-based heuristics, history-based ranking, a GA-based search, an enhanced QPSO, and two learning-based ablations, as validated by our experiments on four Java projects from Defects4J. In terms of various programs and budgets, using GNN–RL has led to an improvement in the Average Percentage of Faults Detected (APFD) and Cost-cognizant Average Percentage of Faults Detected (APFDc) by approximately 6–9 percentage points on lower budgets. In addition, it is possible to obtain a 42% suite reduction and a 48% cost reduction while still retaining 98% of all fault-revealing tests in the final suite. The results show that it is possible to obtain promising solutions for adaptive regression testing using budget-aware reinforcement learning and graph-based representation learning.
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.
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.