Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences (ISSN: 2096-3246) is a bi-monthly peer-reviewed international Journal. Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences was originally formed in 1969 and the journal came under scopus by 2017 to now. The journal is published by editorial department of Journal of Sichuan University. We publish every scope of engineering, Mathematics, physics.
FEATURE ENGINEERING AND EXPLAINABLE AI FOR PLANT DISEASE DETECTION: A COMPREHENSIVE SURVEY
Plant diseases represent a significant threat to global food security, causing yield losses estimated between 20% and 40% annually. Accurate, timely, and scalable disease detection is therefore critical for modern agriculture. This survey provides a comprehensive review of feature engineering approaches and deep learning techniques applied to plant disease detection, with special focus on Gradient-weighted Class Activation Mapping (GradCAM) and Explainable Artificial Intelligence (XAI) methods that bridge the gap between black-box model predictions and human interpretability. We examine traditional handcrafted feature engineering methods including color histograms, texture descriptors (GLCM, LBP, Gabor), and shape features, followed by deep feature extraction using Convolutional Neural Networks (CNNs) and transfer learning architectures such as ResNet, EfficientNet, VGG, DenseNet, and MobileNet. The survey covers key datasets including PlantVillage and PlantDoc, discusses data augmentation strategies, and critically evaluates explainability tools including GradCAM, GradCAM++, LIME, and SHAP. Emerging trends such as MLOps integration, lightweight models for edge deployment, and transformer-based architectures are also discussed. The paper highlights challenges including limited real-world data, class imbalance, and the need for domain-trustworthy interpretability, and outlines future research directions toward robust, explainable, and deployable plant disease detection systems.
Vijayalakshmi S. Abbigeri, Geetha D. Devanagavi
REVIEW AND OPEN CHALLENGES: BLOCKCHAIN-BASED MULTIMEDIA CONTENT PROTECTION
Abstract: This study presents a comprehensive analysis of multimedia content protection applications utilizing blockchain technology. A taxonomy is established to categorize these applications based on the technical dimensions of blockchain technology, content protection methods, specifically encryption, digital rights management, digital watermarking, and fingerprinting (or transaction tracing), as well as performance metrics. The literature review indicates that there is yet no comprehensive and systematic taxonomy for blockchain-based copyright protection solutions. Furthermore, the quantity of effectively built blockchain-based content protection systems is exceedingly limited. This indicates a deficiency in research. We propose a taxonomy that amalgamates technical elements and application expertise to direct researchers in the development of blockchain-based multimedia copyright protection systems. Additionally, the report addresses several technological obstacles and delineates prospective research avenues.
PESNET: A CNN FRAMEWORK TO PREDICT THE GENDER OF HUMANS USING BARE FOOTPRINTS
In forensic science, one of the common applications of personal identification occurs in cases involving unidentified human remains. This work proposes PesNet, a gender prediction framework from bare footprints using a novel convolutional neural network (CNN). In this study, a dataset of 1,000 human barefoot impressions, evenly split between male and female sample impressions, collected from the different administrative division of Haryana, India, was exploited to visualize and analyze the shape and sizes by utilizing deep learning approaches. PesNet architecture in this article uses higher order preprocessing methods such as Gaussian filtering and grayscale conversion to improve image quality and minimizes environmental noise. PesNet, deployed with a pre-trained model, prevailed over conventional methods and established models to achieve a stunning gender classification accuracy of 96% are the findings of our study. This research not only highlights the potential of deep learning in forensic applications but also addresses challenges such as class imbalance and the need for reliable identification techniques in criminal investigations.
Akash Bans, Jaskaran Singh, HarshitSharma,Prashant SinghRana
EXPERIMENTAL AND ANALYTICAL INVESTIGATION OF HYBRID NANOPARTICLE-ENHANCED EMULSIFIED FUEL FOR PERFORMANCE OPTIMIZATION IN INTERNAL COMBUSTION ENGINES
Abstract—This research presents an experimental and analytical examination of a hybrid nanoparticle-enhanced water-emulsified biodiesel fuel for diesel engine utilization. The suggested fuel is made up of B15 biodiesel (85% diesel and 15% waste cooking oil biodiesel), distilled water, titanium dioxide (TiO₂) nanoparticles (20–30 nm), and carbon quantum dots (CQDs) made from orange peel biomass. The hybrid nano-additives were added to speed up combustion, catalytic oxidation, and secondary atomization through micro-explosion events. We tested a single-cylinder, four-stroke, water-cooled, direct-injection diesel engine (3.5 kW, 17.5:1 compression ratio) that ran at a steady speed of 1500 rpm with loads that changed from 25% to 100%, corresponding to experimental points at 25%, 50%, 75%, and 100%. We looked at performance metrics like brake thermal efficiency (BTE) and indicated thermal efficiency (ITE), as well as emissions like NOₓ, HC, CO, and SO₂. The results showed that the maximum BTE was 38.82% at 80% load with 100 mg/L TiO₂, 100 mg/L CQDs, and 5% water content. This is a big improvement over the baseline diesel operation. Adding 10% water cut NOₓ emissions by about 17% because of thermal quenching. Using the best concentrations of nanoparticles cut HC emissions by 78% and CO emissions by 61%. A physics-based artificial neural network (ANN) model was very good at making predictions, with R² values over 0.86 for all output parameters. The hybrid TiO₂–CQD emulsified fuel system had 4–7% higher BTE and 12–18% lower HC emissions than regular single-nanoparticle fuel blends. This shows that the suggested synergistic combustion enhancement approach works.
HYBRID GREY WOLF–ANT BEE COLONY OPTIMIZED PI CONTROLLER FOR ENHANCED POWER QUALITY IN HYBRID WIND–PV–EV SYSTEMS TO MITIGATED TOTAL HARMONIC DISTORTION (THD)
An electric vehicle (EV) control mechanism that might be implemented using a hybrid Grey Wolf Ant Bee Colony (GWO-ABC) is a grid-connected wind-photovoltaic (PV) system. As the controller of the DFIG-based wind subsystem adjusts the PI gains of the grid-side converter (GSC) and the rotor-side converter (RSC), they collaborate with the PV array through the shared DC link. The tuning criterion aims to minimize a multi-objective cost that incorporates settling time, total harmonic distortion at the point of common connection (THD), and the integral of time-weighted absolute voltage inaccuracy, among other things (ITAE). Scenarios like as renewable ramps, stochastic EV load steps, and single-phase voltage sag are used to test a MATLAB/Simulink model. Across these cases, the proposed GWO–ABC–PI achieves tighter DC-bus regulation with reduced overshoot, maintains near-unity power factor, and lowers grid-current distortion such that PCC voltage THD remains within IEEE 519-2022 limits. Comparative studies versus standalone GWO PI, ABC-PI, and a PI–ANFIS baseline show faster convergence of tuned gains and improved disturbance rejection without sacrificing the transparency and low computational footprint of PI control. The results indicate that hybrid metaheuristic tuning offers a practical pathway to robust power-quality enhancement in hybrid renewable–EV systems subject to non-linear loads and grid imbalance.
Mrs. Kiranmayee Jampala ,Dr. KVNS Pavan Kumar
CHAOTIC BOBCAT OPTIMIZATION ALGORITHM (CBOA) BASED ENERGY EFFICIENT CLUSTERING ROUTING PROTOCOL FOR UNDERWATER WIRELESS SENSOR NETWORKS
The Underwater Wireless Sensor Network (UWSN) was created since there is a rising need for real-time data collection, remote monitoring, and autonomous operation in underwater environments.So, the main things to think about in a UWSN are how long the network will last and how much energy it would need. The routing protocol for a UWSN is in-charge of finding the optimal method to transport data packets to the surface destination in a way that is both fast and effective. Clustering and routing methods can help UWSNs use less energy. A new protocol for UWSNs introduces CBOA-MHDT - Chaotic Bobcat Optimization Algorithm-Multi-Hop Data Transmission. The CBOA-MHDT technique chooses cluster heads (CHs) and groups them into clusters based on things like how much energy is left, how far apart the clusters are, and how far apart the clusters are from each other. The CBOA method uses the Bobcat Optimization method (BOA) and chaos to make optimization work better. The hunting techniques of bobcats inspired BOAs, and the addition of chaos is meant to make the optimization process better at finding and using resources. After that, each cluster gets a CH to gather data packets from sensor nodes that are distributed around at random. Things like residual energy, distance, and node degree, were considered by the suggested method, which finds a fitness function.MHDT does not need sensor nodes and the sink to talk to each other directly. It passes the data across several intermediate nodes instead. Simulations have been executed in Matrix Laboratory R2021b (MATLABR2021b) to assess and contrast the proposed algorithm's efficacy against established clustering techniques, utilizing metrics such as PLR - Packet Loss Ratio, network lifetime, throughput, Residual Energy, and PDR - Packet Delivery Ratio.
MECHANICAL AND TRIBOLOGICAL PERFORMANCE OF AL7050 AMALGAMATED WITH BN-TIB2 MMC
The influence of fabrication on the structure and strength of reinforcement (TiB2 & BN) and Al alloy composite has been studied. Al 7050 is chosen as matrix and TiB2 & BN are selected as reinforcement. This Research work deals with the fabrication, tribological and mechanical behavior of Al based TiB2 & BN matrix composite. The key properties that make Al a dominant material over additional characteristics are minimal weight, high strength and ease of machinability. TiB2 & BN which are highly thermal stability and high strength, low density and easily available at moderate cost is included as a reinforcement. In this case, Stir casting, which entails combining the necessary amounts of additives into stirred molten Al, completes the fabrication. Each fabrication carries the TiB2 & BN reinforcement content varied from 0wt. % - 5wt. %. Following solidification, the samples were prepared as per ASTM standards. They are further tested and computed to find the mechanical and tribological properties like wear properties, tensile, compression, hardness, corrosion and SEM Analysis. The tensile strength of the composite material increased by 67% at 5wt%, compressive strength increased by 13% at 0.5wt% and Hardness increased by 28% at 3wt% reinforcement. The wear resistance of the composite material increased by 50% compared to Al7050 without reinforcement. The reinforcement is uniformly distributed in the metal matrix and thus the propagation of cracks at the grain boundaries is slowed down or halted which improves the overall strength of the MMC. The corrosion rate for all wt% of the composite material is uniform and is at a rate of 0.001 mm/day or 0.33mm/yr.
Praveen U B, Dr. Manjunatha B, Dr. Puneeth Kumar N, Mr. Manjunatha A N
ANN FOR PREDICTION OF CORROSION AND WEAR PROPERTIES OF A356 /AL2O3/RHA PARTICULATES REINFORCED HYBRID COMPOSITES
The characteristics of wear and corrosion of Al2O3 and RHA particulates reinforced with Aluminium A356 were first experimentally studied,after that ANN were actualized so as to demonstrate the Specific wear rate and Corrosion resistance. Following ASTM-compliant machining, the test sample underwent a solution heat treatment and artificial ageing. The wear characteristics have been examined, and double-aged people's stretching conditions have significantly improved. The additional exposed with ANNs can be utilized in enhancing the procedure parameters of aluminum alloys. The ppredicted values from the Test samples and experimental values are in close agreement and this shows the usefulness of applying ANNs in predicting results.
Hanumanthe Gowda, Puneeth Kumar N, Harish S, Neerukattu Srikanth, Vinod Kumar
ATTENTION-GUIDED EFFICIENTNET ARCHITECTURE FOR PRECISE CRIMINAL IDENTIFICATION IN SURVEILLANCE IMAGES
Abstract: Criminal identification from surveillance imagery has become a critical research area in intelligent forensic surveillance systems due to the increasing deployment of CCTV cameras in public and private environments. However, surveillance-based face recognition remains highly challenging because of low image resolution, illumination variation, motion blur, pose changes, facial occlusion, and background clutter. To address these limitations, this paper proposes an Attention-Guided EfficientNet (AG-EfficientNet) framework for precise criminal identification in surveillance images. The proposed framework integrates EfficientNet-B0 with Convolutional Block Attention Modules (CBAM) to enhance discriminative facial feature learning under degraded surveillance conditions. In addition, a multi-scale surveillance feature fusion strategy is introduced to preserve both local texture information and high-level semantic identity representations. A hybrid Softmax-Triplet optimization mechanism is further employed to improve inter-class separability and intra-class compactness for robust criminal identity discrimination. The proposed framework was experimentally evaluated using the Labeled Faces in the Wild (LFW) and SCFace datasets. Experimental results demonstrate that the proposed AG-EfficientNet framework achieved superior surveillance recognition performance with an identification accuracy of 98.2%, Precision of 97.9%, Recall of 97.6%, F1-Score of 97.7%, and ROC-AUC of 0.99, outperforming conventional deep learning architectures including AlexNet, VGG16, ResNet50, MobileNetV2, and standard EfficientNet-B0. Furthermore, Grad-CAM visualization and ablation analysis confirm the effectiveness of the proposed attention-guided feature learning strategy. The computational complexity analysis also indicates that the proposed framework maintains relatively low inference overhead, making it suitable for real-time forensic surveillance applications.
AN IMPROVED ATTENTION-GUIDED MULTI-SCALE U-NET ARCHITECTURE FOR PULMONARY NODULE SEGMENTATION
Segmentation of pulmonary nodules is one of the key processes of computer-aided diagnosis of lung cancer, as it has a direct impact on early diagnosis, assessment of the disease, and clinical decision-making. Computer-assisted segmentation of computed tomography images is still a formidable task because of the marked heterogeneity of nodule size, abnormal morphology, low contrast, and complicated anatomic attachments. The traditional U-Net-based deep learning models show good performance, albeit with drawbacks in that they lack the ability to capture finer boundary information, and at the same time, they are not good at maintaining a global contextual understanding. The chapter of this chapter introduces a better attention-directed multi-scale U-Net architecture to deal with these issues using more representative features and selective integration of information. The suggested architecture incorporates multi-scale feature aggregation and fine-tuned attention-based skip connections to prioritize anatomically important areas and avoid interference from the background. Experimental analysis in benchmark lung CT datasets proves significant results of improvement of the accuracy of segmentation, boundary delineation, and strength in comparison with traditional U-Net and attention-based variants. The given architecture can ensure high-quality segmentation of heterogeneous nodules with the characteristics and ensures a high level of computational efficiency that is appropriate to the clinical applications. This paper offers a deep learning architecture that is strong and scalable in the segmentation of pulmonary nodules and provides a valuable input to the optimization of medical image management and computer-aided diagnosis software in the future.
EVOLUTIONARY BASED ATTENTION NESTED U NET ARCHITECTURE FOR ECG -BASED HEARTBEAT CLASSIFICATION
Electrocardiogram (ECG) signals are widely used for diagnosing cardiac disorders, yet their reliability is often affected by various noise sources that distort waveform morphology and reduce diagnostic accuracy. Accurate heartbeat classification therefore remains a critical challenge in automated cardiac monitoring systems. This study aims to develop a robust and efficient ECG classification framework capable of enhancing noise resilience and improving diagnostic precision. The proposed method integrates an Improved Empirical Mode Decomposition (IEMD) technique for effective noise reduction, followed by Fast Fourier Transform (FFT) for extracting frequency-domain features. IEMD produces cleaner Intrinsic Mode Functions (IMFs) compared to conventional EMD, leading to better signal decomposition, while FFT provides a compact and discriminative representation of ECG characteristics. For classification, an Evolutionary Based Attention Nested U-Net (EBANU-Net) is employed, where the U-Net architecture is enhanced.
MACHINE LEARNING–DRIVEN DECISION SUPPORT SYSTEMS FOR RESOURCE OPTIMIZATION AND CURRICULUM PLANNING IN ENGLISH DEPARTMENTS
Managing resources and designing adaptive curricula in English departments has become increasingly complex due to fluctuating student enrollments, diverse course requirements, and evolving pedagogical demands. Traditional decision-making approaches often rely on static data and manual planning, leading to inefficiencies in faculty allocation, course scheduling, and curriculum relevance. This study proposes a Machine Learning (ML)–driven Decision Support System (DSS) to enhance resource management and curriculum planning in English departments. The system integrates multiple data sources, including historical enrollment records, student performance metrics, course feedback, and faculty workload data. Various ML models are employed, such as regression techniques for enrollment forecasting, classification algorithms for predicting student success, and clustering methods for identifying patterns in course demand. Additionally, recommendation algorithms are utilized to support dynamic curriculum development and elective course selection. The proposed approach is expected to improve decision-making accuracy, optimize resource utilization, and enable data-driven curriculum adjustments aligned with student needs and institutional goals. Preliminary findings suggest that ML-based DSS can significantly reduce planning inefficiencies while enhancing responsiveness to academic trends. The implications of this research extend to academic institutions seeking to modernize departmental management practices. By integrating intelligent systems into administrative processes, English departments can achieve greater operational efficiency, improve teaching outcomes, and foster a more adaptive and student-centered learning environment.
C. Jeevanantham, M.Ramakrishnan, S. Rajasekar, S.Arunkumar , V.Dhanalakshmi, E.Aksha Pricilda Reena
ANALYSIS OF THERMAL AND MECHANICAL PROPERTIES OF BIAXIALLY ORIENTED POLYPROPYLENE (BOPP) FILMS USED AS LABELS
This study conducts multi-dimensional thermal and mechanical property tests on commercial BOPP label films used for wrap-around labels and pressure-sensitive labels. It sequentially completed thickness and density measurement, DMA analysis, and heat shrinkage testing. The experiments cover a temperature range from -20°C to 120°C, while distinguishing performance differences between the material’s MD (longitudinal direction) and TD (transverse direction). Test results confirm that the film exhibits excellent production reproducibility and wide-temperature-range stability, and can meet the core application requirements of commercial labels including high-speed labeling and thermal-sensitive label applications. This study uses DMA to characterize the relevant properties of BOPP label films.
COMPARATIVE STUDY OF SID AND INK TRAPPING IN VIRGIN AND RECYCLED PAPER USING UV INKJET PRINTING
This study investigates how virgin paper and recycled paper affect the quality of prints by comparing the Solid Ink Density (SID) and Ink Trapping values using the cyan, magenta, yellow and black (CMYK) inks in UV inkjet printing. Virgin and recycled paper are used for the study that has same grammage of 80 GSM, but they have different fillers contents in them. Spectrophotometer is used to take measurements at room temperature condition. The results indicate that recycled paper produced higher SID values, this can be due to increased porosity and higher ink absorption which allows more ink to be absorbed in recycled paper, whereas the virgin paper achieved slightly greater Ink Trapping performance because of uniform surface structure and superior ink holdout properties of virgin paper. Overall, the prints on recycled paper are darker and more saturated, while those on virgin paper provided better colour reproduction and better colour strength. The differences between the two substrates were relatively small and fell within acceptable tolerance.
SUSTAINABLE INKJET PRINTING: PRINT PERFORMANCE ANALYSIS OF ECO-FRIENDLY INKS ON PLASTIC SUBSTRATES
Uv-curable piezoelectric inkjet technology has revolutionized wide-format printing by minimizing volatile compound emissions (VOC) released issues and high-fidelity output on non-absorbent polymeric substrates. The Greenguard Gold accreditation has emerged as the accepted standard for eco-friendly ink systems in the retail graphics, flexible packaging and indoor-outdoor advertising industries. The US EPA’s NESHAP compliance and the needs of circular sustainable economy are causing the industry’s rapid shift from polyvinylchloride film to easily recyclable polyester film substrates, necessitating the stringent measurable characterization of print performance difference under certified output constraints. No prior research has presented contemporaneous comparative Solid Ink Density (SID) and Tone Value Increase (TVI)/ Dot Gain data for Greenguard Gold certified UV-curable inks on both substrates under identical piezoelectric inkjet conditions across a rigorous specimen population. Under ISO-recommended measurement and process parameters, this study measures and compares SID and TVI (at 40% & 80% nominal tone) across all CMYK channels for n=50 specimen per substrates. Print samples was printed on a Mimaki UJV 100-160 Plus piezoelectric (dod) system at 1200 × 1200 dpi using Mimaki LUS-210 UV-ECO Series Greenguard Gold-Certified CMYK inks. An X-Rite eXact spectrophotometer was used to measure SID in accordance with ISO 5-3/ISO13655 (M0);TVI was quantify using the Murray-Davis equation in accordance with ISO 12647-1. PVCF & PF achieve across all CMYK channels for SID (C:1.34,M:1.35,Y:1.30,K:1.43 vs C:1.04,M:1.14,Y:1.12,K:1.25), with a mean inter-substrate difference of 0.24 D. According to TVI analysis Yellow was the highest dot gain channel on both substrates at 40% nominal tone (PVCF:31.20%; PF:26.87%) and Cyan was substantially greater on PF (16.34%) compared to PVCF (11.61%). Standard deviation values of (0.02-0.90) validated process overall reliability. These outcomes prove that substrate-specific ICC profile calibration and tone curve compensation are essential prerequisites for achieving chromatic and tonal parity with PVCF under identical conditions and also establish a statistically supported ISO-validated framework for substrate selection in UV inkjet production environments. This study contributes to five Sustainable Development Goals of the United Nations. Utilizing a low-voc Greenguard Gold-certified inks advance SDG 3 (Good Health and Well-Being ,Target 3.9) by removing toxic air pollutants (toluene, xylene and ethyl acetate) that are categorized under US EPA NESHAP. SDG 7 (Affordable and Clean Energy, Target 7.3) is supported by UV-LED curing, which uses 50-70% less energy than traditional mercury-arc systems. SDG 9 (Industry, Innovation and Infrastructure , Target 9.4) is in line with the use of ISO-validated piezoelectric DOD inkjet as a clean industrial process. SDG 12 (Responsible Consumption and Production, Targets 12.4 & 12.6) is addressed by comparing eco-preferred PET to PVC by lowering Industrial VOC and CO2 emissions throughout the printing Industry, these actions together meet SDG 13 (Climate Action, Target 13.2).
CRITICAL EVALUATION OF THE MECHANICAL AND CHEMICAL PERFORMANCE OF THE MULTI-LAYER (BOPP+ METALLIZED CPP) FLEXIBLE PACKAGING LAMINATES
This study provides the mechanical and chemical performance of multi-layer flexible packaging laminates composed of Biaxially Oriented Polypropylene (BOPP) and Metallized Cast Polypropylene (Met CPP). The research focuses on how varying conditioning temperature (10°C,25°C and 45°C) impacts substrate properties, including Seal strength, Bond strength, Coefficient of friction (COF), Water Vapor Transmission Rate (WVTR) and Oxygen Transmission Rate (OTR). Doing the experiments indicates that ambient conditioning (25° C) provides the most consistent mechanical performance, provides the highest seal and bond strength; however, in the chemical barrier, performance is highest at a lower temperature (10°C) because of the reduced molecular mobility, which minimizes gas and moisture permeation. On the other hand, at the higher temperature (45°C) it is found that it increases permeability and reduces surface resistance with an increase in slip, meaning a lower Coefficient of Friction (COF), reducing shelf-life protection of the flexible food packaging.
ARTIFICIAL INTELLIGENCE BASED BYZANTINE FAULT TOLERANCE CONSENSUS ALGORITHM FOR HEALTH CARE MONITORING SYSTEM
The rapid advancement of Internet of Things (IoT) and distributed computing has created unprecedented opportunities for real-time healthcare monitoring systems. However, these distributed systems face significant challenges related to fault tolerance, data integrity, and Byzantine failures — conditions where nodes may behave arbitrarily or maliciously, compromising the reliability of critical patient health data. This paper proposes a novel Artificial Intelligence Based Byzantine Fault Tolerance (AI-BFT) consensus algorithm specifically designed for healthcare monitoring systems. The proposed framework integrates machine learning-driven anomaly detection with a modified Practical Byzantine Fault Tolerance (PBFT) protocol to identify and isolate faulty or malicious nodes in real time. The AI component employs a Long Short-Term Memory (LSTM) neural network to model normal sensor behavior and detect deviations indicative of Byzantine faults. Experimental evaluations conducted on a simulated IoT-based healthcare network demonstrate that the AI-BFT algorithm achieves a fault detection accuracy of 97.4%, reduces consensus latency by 34% compared to classical PBFT, and maintains system availability above 99.2% under Byzantine fault conditions involving up to 33% faulty nodes. The proposed system provides a robust, scalable, and intelligent solution for ensuring data integrity and reliability in life-critical healthcare monitoring environments.
A Survey of Transformer Graph Neural Network Graph Attention Network and Capsule Network Approaches for Cardiovascular Disease Prediction
Cardiovascular disease is one of the important causes of morbidity and mortality in the world and early diagnosis and accurate prediction are required. The recent developments of Artificial Intelligence (AI) technologies have simplified the prediction of cardiovascular disease diagnosis by analyzing a variety of health-related data such as the analysis of an electrocardiogram (ECG), electronic health records (EHRs), medical images, data from wearable devices and even genomic data. The present review reviews advanced architectures of AI for cardiovascular prediction, including multimodal learning architectures, Capsule Networks (CapsNets), Graph Attention Networks, Graph Neural Networks and Transformer models. Methods that model specific temporal relationships in clinical data using a transformer architecture are effective for capturing temporal dependencies in sequential clinical data, whereas methods that model the relationships among various patients, diseases, and healthcare events using graphs are effective for modeling complex relationships. CapsNets are hierarchical feature representations for cardiovascular signal and image analysis. Moreover, multimodal AI enhances the predictability by combining multiple healthcare data sources, and Explainable Artificial Intelligence (XAI) techniques boost transparency and clinical trust. While there is great progress, there are issues with data heterogeneity and interpretability, computational complexity, external validation, privacy, and regulatory compliance. Clinically deployable cardiovascular prediction systems are likely to continue to be increasingly improved in future developments of multimodal learning, XAI, foundation models, and personalized medicine.
FROM SIGNAL PROCESSING TO DEEP LEARNING: A REVIEW OF FETAL HEALTH MONITORING TECHNIQUES USING BIOMEDICAL SIGNALS
Abstract—Fetal health monitoring is essential for early de-tection of life-threatening conditions such as hypoxia and ar-rhythmia; however, conventional techniques including CTG and Doppler ultrasound are limited by subjectivity, operator de-pendency, and inter-observer variability. Non-invasive modalities such as fetal electrocardiography (FECG) and fetal phono-cardiography (FPCG) enable continuous monitoring but are significantly affected by maternal physiological interference and environmental noise, motivating the adoption of advanced artifi-cial intelligence approaches. This paper presents a structured comparative analysis of deep learning-based fetal signal pro-cessing frameworks, focusing on convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and hybrid multimodal fusion architectures. A unified evaluation frame-work is employed using clinically relevant metrics, including accuracy, sensitivity, specificity, and signal-to-noise ratio (SNR) improvement, enabling consistent cross-method comparison. The analysis demonstrates a clear performance hierarchy, where deep learning approaches outperform traditional handcrafted meth-ods, hybrid CNN–LSTM models effectively capture spatiotem-poral dependencies, and multimodal fusion systems integrating electrical, acoustic, and physiological signals achieve state-of-the-art performance of approximately 92%–97%. Despite these advances, clinical deployment remains constrained by limited annotated datasets, computational complexity, and lack of real-time validation. The study highlights key research gaps and future directions involving IoT-enabled wearable sensing, edge AI deployment, and explainable deep learning for robust and personalized fetal healthcare.
Yashaswini N G, Om Prakash Yadav, Yojana Yadav
INTEGRATING CLOUD-ENABLED INTELLIGENT DRUG DELIVERY SYSTEMS INTO CROSS-DISCIPLINARY COMMUNICATION AND INNOVATION MANAGEMENT IN ENGLISH STUDIES
This study explores the evolving role of English Studies in shaping and mediating technological innovation within contemporary society. As digital technologies increasingly influence communication, culture, and knowledge production, the humanities—particularly English Studies—offer critical frameworks for interpreting and guiding these transformations. The purpose of this research is to examine how literary analysis, rhetorical theory, and critical discourse practices contribute to the ethical, cultural, and linguistic dimensions of technological development. The central argument posits that English Studies functions as a crucial intermediary between technological advancement and human experience, enabling more inclusive, reflective, and responsible innovation. By analyzing narratives embedded in digital platforms, language use in artificial intelligence, and the cultural implications of emerging media, the discipline helps decode and reshape the meanings attached to technology. Methodologically, this study adopts an interdisciplinary approach, combining textual analysis, discourse analysis, and case studies of digital technologies and communication systems. It also incorporates theoretical perspectives from media studies and cultural studies to contextualize findings. The expected outcomes include a clearer understanding of how humanistic inquiry informs technological design and usage, as well as practical insights into integrating ethical and cultural awareness into innovation processes. Ultimately, this research aims to reaffirm the relevance of English Studies in a technology-driven world, highlighting its capacity to bridge gaps between technical expertise and human values.
M.Prabhakaran, K.Poomalar, V.Gunasekaran, M.Lavanya, S.Vimala, S.Parthiban
A QUANTUM-RESISTANT FRAMEWORK FOR SECURE RGB IMAGE TRANSMISSION USING ADVANCED COMPUTATIONAL TECHNIQUES
Digital RGB images are widely used in applications such as medical imaging, surveillance, and multimedia communication, where they often carry sensitive information that must be protected during transmission and storage. To address this need, this work presents a quantum‑resistant framework for RGB color image encryption and decryption that combines a large‑dimension color key image with computationally efficient operations. The proposed scheme uses a secret key image of arbitrary size and resolution, eliminating the need to transmit the key while significantly enlarging the effective key space and making brute‑force attacks computationally infeasible. Experimental evaluation on multiple color images of varying resolutions demonstrates lossless reconstruction, with peak signal‑to‑noise ratio (PSNR) approaching infinity and mean square error (MSE) equal to zero between the original and decrypted images. Measured encryption and decryption times show an approximately linear relationship with image size, indicating that the method scales predictably while maintaining high throughput. These results suggest that the proposed framework is suitable for real‑time, secure RGB image transmission in resource‑constrained and quantum‑aware environments.
Rajesh Kumar Tiwari, Prabhjot Kaur, Ritu Mishra, Kanchan Nahar, Bhushan,Ashok Singh Gaur
INTELLIGENT OPTIMIZATION OF MULTICROPPING STRATEGIES ACROSS IRRIGATION SYSTEMS USING GENETIC ALGORITHM, DEEP LEARNING, AND PARTICLE SWARM OPTIMIZATION
Agriculture plays a crucial role in ensuring food security and sustainable economic development. However, increasing population growth, limited water resources, climate variability, and inefficient crop planning practices present significant challenges to agricultural productivity. Multicropping systems offer an effective solution for improving land utilization and enhancing farm profitability, but determining optimal crop combinations under different irrigation conditions remains a complex optimization problem. This study proposes an intelligent hybrid framework integrating Deep Learning (DL), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing multicropping strategies across irrigation types. The Deep Learning model is employed to predict crop yield based on environmental and agricultural parameters, while the Genetic Algorithm generates optimal crop allocation strategies. Particle Swarm Optimization further refines the GA-generated solutions by optimizing resource allocation and irrigation efficiency. The proposed framework is evaluated using an agricultural dataset containing soil characteristics, climatic conditions, irrigation methods, crop information, yield, and profit data. Experimental results demonstrate that the integrated GA-DL-PSO framework achieves superior performance in terms of crop yield, water-use efficiency, and economic profitability when compared with traditional farming methods and standalone optimization techniques. The findings indicate that the proposed model provides an effective decision-support system for sustainable agriculture and intelligent resource management. The developed framework can assist farmers and agricultural planners in making data-driven decisions that improve productivity while minimizing resource consumption.
AN AI-DRIVEN MACHINE LEARNING FRAMEWORK FOR SUSTAINABLE DEVELOPMENT: ENERGY EFFICIENCY, CLIMATE MODELLING, AND INTELLIGENT WASTE CLASSIFICATIONAN AI-DRIVEN MACHINE LEARNING FRAMEWORK FOR SUSTAINABLE DEVELOPMENT: ENERGY EFFICIENCY, CLIMATE MODELLING, AND I
In this research paper, the design, implementation, and evaluation of a all-encompassing machine learning system to support sustainable development is conducted, covering three interrelated areas, including energy efficiency forecasting at the household level, downscaling climate change and detecting extreme events through deep learning, and automated waste classification with convolutional neural networks. It has been applied to real-world data using Python and TensorFlow, Scikit-learn, and PyTorch, such as the UCI Household Power Consumption dataset (2,049,280 samples), ERA5 reanalysis climate data, and the joint TrashNet/TACO waste image dataset (4,000+ labeled images). An forecaster of standardised hourly energy yields RMSE = 0.0237 (R² = 0.9995) with a Random Forest Regressor and a stable sequential learning MSE = 0.385 at 50 training epochs with a network based on an LSTM. A CNN-LSTM hybrid model decreases climate downscaling RMSE by 63.2% compared to bilinear interpolation baselines (R 2 = 0.924). A fine-tuned ResNet-50 is over 90 percent waste classification accuracy (weighted F1 = 0.902) six waste types. Analyses of SHAP and LIME indicate that scientific validity of model outputs are valid, and autoregressive lag features explain more than 42 percent of the importance of energy predictions. The integrated framework specifically responds to SDG 7, SDG 12 and SDG 13 of the UN Sustainable Development Goals, offering a deployable, modular and interpretable AI system to sustainable infrastructure management.
Priyanka Banth, Dr.Ankita Makker, Touseef Ahmad Lone
PSYCHOLOGICAL CHALLENGES FACED BY EMPLOYEES IN THE AI ERA: A SYSTEMATIC REVIEW THROUGH THE JOB DEMANDS-RESOURCES FRAMEWORK
Background: In the last ten years, artificial intelligence and automation have transformed the nature of work in almost every industry. AI-driven systems have taken over, causing psychological challenges that were not anticipated by employees worldwide since 2015. A significant amount of research has been conducted on the tech and economic aspects of this transition, but there is no cohesive theory that unites these two phenomena in relation to psychology. Objective: The review combines data from 60 peer-reviewed studies published between 2015 and 2025, all of which examine the psychological challenges that employees face in the AI age. It is based on the JD-Res model, which helps explain how new AI-driven stressors (and thus jobs) are being created as well as job demands, and how organizations can provide resources to help people cope. Methods: I used standard review methods, which included searching PubMed and PsycINFO as well as Web of Science & Scopus, along with key journals on organizational psychology. From January 2015 to December 2025, studies were required to be published in English and evaluate employees' psychological outcomes related to AI integration. All research was empirical. The narrative synthesis focused on four key points: (1) how AI-based job demands trigger distress, (2) the danger to professional identity and fear over outdated skills, (3) technostress and cognitive overload, and (4) the role of organizational resources and strategies for managing stress. Results: A definite story is conveyed through the 60 studies that span various countries and industries. First, AI brings a heavy psychological burden—placebos, constant need for rehabilitation and repetition of skills in new roles; the constant scrutiny of algorithms; and the lack of trust in technology. At moderate to severe levels, 25% to 26% of employees express fear of AI. Many individuals are experiencing a systematic and gradual erosion of their professional identity, which can be easily testified by displaced workers' shared emotions that reflect both grief and existential loss. The correlation between lower well-being and feeling overwhelmed by technology, including being overwhelmed, bewildered, insecure, or uncertain (r ≈ 0.30-0.40), is significant. By offering clear communication, proper retraining, social assistance in understanding various situations, and employee participation in AI's expansion of work, organizations can mitigate the psychological impact of these changes. Conclusion: AI’s arrival at work is more than just a tech upgrade—it’s a complex psychological crisis. Workers are losing resources they rely on: their sense of identity, autonomy, employability, and belonging. The JD-R model makes one thing clear: when AI introduces new job demands and organizations don’t step up with enough support, people struggle. Burnout, anxiety, depression, and falling job satisfaction follow. The solution isn’t simple. Companies need to offer mental health services, bring clarity around AI’s role, involve people in decision-making, and design technology that puts humans first. Still, big research gaps remain. We need more long-term studies, intervention trials, and data on the employees most at risk.
DETECTION, CLASSIFICATION, AND RATING OF ELECTRICAL FAULTS AND POWER QUALITY
Power system faults are unavoidable and can seriously damage costly equipment such as generators, transformers, and motors. They may also cause overvoltages, high fault currents, power outages, explosions, and even loss of life. Therefore, an effective protection system is required for fast fault detection, classification, and localization. Accurate fault analysis helps ensure reliable power supply, reduce outages, and protect electrical infrastructure. This study presents a framework for detecting, classifying, and locating power system faults. Its objectives are to identify different fault conditions at various locations and resistances, determine the causes of interruptions, support quick power restoration, and reduce recurring failures. The study also improves understanding of protection system components to minimize equipment damage and service disruptions. Advanced machine learning techniques, including Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), are used for fault analysis. These models identify fault types, locate faults, classify disturbances, and assess power quality across five classes. Implemented in MATLAB/Simulink on a test network, the proposed framework effectively addresses fault detection, classification, localization, and power quality assessment, improving the protection, stability, and efficiency of modern power systems.
THE BEHAVIOUR OF MAGNETIZED STRANGE QUARK MATTER ON BIANCHI TYPE-I UNIVERSE IN THE FRAMEWORK OF f(R,T) THEORY OF GRAVITY
In this study, we examine a Bianchi type-I cosmological model filled with Magnetized strange quark matter (MSQM) within the framework of gravity. To obtain solutions of the modified field equations, we employ dynamical cosmological parameters by using an equation of state (EoS) for strange quark matter in the Bianchi type-I universe. The corresponding magnetic field is also derived. Furthermore, various physical and geometrical characteristics of the model are calculate and discussed.
P.A. Chavhan, S.P. Kandalkar , A.S.Nimkar , T.D.Nakade
DEEP LEARNING FOR PLANT SPECIES CLASSIFICATION: A COMPREHENSIVE CNN-BASED APPROACH WITH FEATURE VISUALIZATION AND PERFORMANCE BENCHMARKING
Plant species classification is a critical problem in precision agriculture, in the conservation of biodiversity and ecological monitoring. In this paper, a thorough deep learning framework is proposed using Convolutional Neural Networks (CNNs) for high accuracy plant species classification using the V2 Plant Seedlings Dataset of 12 plant species. Multiple CNN architectures were systematically evaluated on a variety of standard metrics such as accuracy, precision, recall, and F1-score across the benchmark datasets.Various CNN architectures such as custom CNN, ResNet-50, DenseNet-121, EfficientNet-B3 and hybrid CNN-Transformer models were benchmarked using various metrics like accuracy, precision, recall, and F1-score across the benchmark datasets. The proposed pipeline includes three key steps: preprocessing the data (standardized resizing, normalization, and background segmentation), data augmentation, and visualization of the features using UMAP. The best model (CNN-Transformer) outperforms the current baselines by 2–5% with 98.9% classification accuracy. The separation between classes is robust as confirmed by feature analysis using UMAP projections, where the overlapping clusters are morphologically similar species. The model's discriminative power is additionally confirmed by the evaluation carried out with confusion matrices and precision-recall curves. This work sets a reproducible standard for plant classification under controlled conditions and lays the groundwork for implementation in the actual agricultural system.
Mr. Bikramjeet Singh, Mr. Touseef Ahmad Lone
GREEN COMPUTING AND CARBON FOOTPRINT REDUCTION: A COMPREHENSIVE FRAMEWORK FOR SUSTAINABLE IT INFRASTRUCTURE MANAGEMENT
Abstract— The explosive proliferation of digital services, cloud computing platforms, and artificial intelligence workloads has transformed the information and communication technology (ICT) sector into one of the most energy-intensive industries on the planet. Global data centers consumed an estimated 415 terawatt-hours of electricity in 2024, and projections indicate this figure will surpass 900 terawatt-hours by 2030 if present growth trajectories continue unchecked. Concurrently, the embodied carbon locked into semiconductor manufacturing—a dimension frequently neglected in sustainability reporting—can exceed the entire operational carbon of a computing device over its useful lifetime. This paper presents the Integrated Carbon Management Framework (ICMF), a five-layer architecture that delivers real-time visibility into all material sources of IT-related carbon emissions and converts that visibility into automated, policy-driven reduction actions. The framework encompasses operational electricity monitoring linked to live grid carbon intensity feeds, amortized embodied carbon tracking across full hardware lifecycles, software carbon intensity scoring aligned with the ISO/IEC 21031:2024 specification, and a carbon-aware workload scheduling algorithm that temporally and spatially shifts delay-tolerant jobs to minimize aggregate emissions without violating service-level agreements. Evaluation against six months of anonymized enterprise workload traces demonstrates aggregate operational carbon reductions of 21.8%, with temporal scheduling alone achieving 32.4% savings for batch workloads. Lifecycle-guided hardware replacement further reduces annual embodied carbon procurement by 18.3% relative to conventional warranty-based policies. A vendor-agnostic reporting protocol is additionally proposed to resolve the methodological divergence identified across leading cloud provider carbon calculators, reducing inter-calculator variance from 43% to below 5%. The paper concludes with a roadmap for extending these contributions to edge computing environments and federated sustainability benchmarking.
Krishan Singh, Touseef Ahmad Lone, Arvin Vinayek
NEXT-GENERATION NANOMATERIAL- BASED ENERGY SYSTEMS FOR EFFICIENT POWER CONVERSION COMPUTATIONAL MODELING APPROACHES
Abstract: As the world needs sustainable energy solutions more than ever, research on new materials with the potential for better energy generation, storage, and conversion efficiency has surged. Of these innovations, next-generation nanomaterials have kept their wares showing great potential for future applications in energy systems to improve energy system performance, mainly on basis of their unique properties and applications in the fields of structure, electrical, thermal and optical properties. Applications of nanomaterial based energy systems such as solar energy conversion, fuel cells, thermoelectric devices, batteries, supercapacitors, and hydrogen energy technologies have shown great potential. The incorporation of the nanomaterials can contribute to better charge transfer, increased surface area, decreased energy losses, and increased conversion efficiencies, in comparison to traditional materials technologies. The complexity of nanoscale interactions and material behaviors, however, poses a great challenge in investigating the phenomena by experiment alone. Therefore, computational modelling techniques are playing a vital role in predicting material properties, optimizing the device performance and in speeding up the development of advanced energy technologies. Among various methods, density functional theory, molecular dynamics simulations, finite element analysis, machine learning algorithms, as well as multiscale modeling all offer useful insights about phenomena at the nanoscale and play a vital role in the design of efficient energy systems. This research investigates the energy conversion technology with next generation of nanomaterials and considers the contribution of computational approaches to the enhancement of system performances and materials optimization. The research reviews recent advancements, technological opportunities, emerging trends with respect to energy systems based on nanomaterials and the importance of computational technologies to problems in energy. The results illustrate the role that advanced materials and simulation techniques can play in the quest for more efficient, sustainable and innovative energy solutions.
Dr V Kamalaker, Raj Kumar Golconda, Dr Janardhan Eppakayala , Jaya Prakash Reddy Kasu , Niran I R