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Hybrid brave-hunting optimisation for heart disease detection model with SVM coupled deep CNN

International Journal of Intelligent Information and Database Systems 2025年第1期17卷 92-123页

作者： Tambe, Pravin M. Shrivastava, Manish Department of Computer Science and Engineering Vivekananda Global University Jagatpura Rajasthan Jaipur303012 India

This research proposes a novel hybrid optimisation method called brave-hunting optimisation (BHO) inspired by lion optimisation (LO) and coyote optimisation (CO). The BHO algorithm is employed to fine-tune the SVM parameters, enhancing its classification performance. Simultaneously, a deep CNN model extracts complex and informative features from medical data. The combined approach capitalises on the strengths of both optimisation techniques to create a robust and accurate model for heart disease diagnosis. The performance evaluation of our model is conducted using comprehensive metrics, which achieve an accuracy of 94.89%, an F1-score of 94.48%, a precision of 94.48%, and a recall of 94.58% for a 90 TP. In the context of a ten k-fold evaluation, achieved 94.78% accuracy, 94.36% F1-score, 94.55% precision, and 94.13% recall. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Development of Self-Powered e-Gear Selector Based on Single-Electrode Triboelectric Nanogenerator (TENG) Technology

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 20-29页

作者： Ali, Sam Maddipatla, Dinesh Bazuin, Bradley J. Atashbar, Massood Z. Western Michigan University Department of Electrical and Computer Engineering KalamazooMI49008 United States

A novel e-Gear selector single-electrode-based triboelectric nanogenerator (TENG) was successfully designed, fabricated, and tested on flexible substrates. The proposed device consists of four TENG sensors representing the traditional gear shift selector (P, R, N, and D). Leather, cotton, paper, and thermoplastic polyurethane (TPU) were selected as bottom triboelectric layers for TENGs 1–4, respectively. Silver (Ag) ink was screen-printed on the backside of each layer representing the single electrode. Human skin was chosen as the top triboelectric layer. Based on the selection of the materials, each TENG device generates different output voltages, enabling the selection of the intended gear mode without the need for a complex control algorithm. The TENG device was characterized in terms of open-circuit voltage (Voc) and short-circuit current (Isc). The repeatability and reproducibility of the TENG device were evaluated by fabricating three samples of each TENG device and by testing each TENG device three times. An average Voc of 4.27 ± 0.36, 7 ± 0.31, 9.05 ± 0.21, and 12.38 ± 0.34 Vpp, resulted in a power density of 0.37 ± 0.05, 0.72 ± 0.09, 0.95 ± 0.07, and 1.74 ± 0.10 μW/cm2, was measured for TENGs 1–4, respectively. The performance of the e-Gear selector showcased the potential of the TENG energy harvesting method for integration into the automotive industry, aligning with the rapid advancements in electric vehicle (EV) applications. This technology is particularly promising for future gear selector designs, as it eliminates the need for external power supplies, reduces cost, minimizes wiring, and offers greater flexibility in positioning within the vehicle. © 2022 IEEE.

关键词： Leather

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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Predictive Maintenance in Industrial IoT (IIoT)

Predictive Maintenance in Industrial IoT (IIoT)

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2024 International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors, MIEITS 2024

作者： Al-Anzi, Fawaz S. Al-Anzi, Abdullah F. Sarath, Sumi Department of Computer Engineering Abdullah Al Salem University Kuwait University Kuwait Electrical & Computer Engineering Department American University Kuwait Department of Computer Engineering Kuwait University Kuwait

ISBN: (纸本)9781510680814

Industrial maintenance practices have been transformed by the integration of the Industrial Internet of Things (IIoT) and AI. In the context of the IIoT, this research article seeks to examine the efficacy of AI-driven predictive maintenance. This study investigates how AI can forecast machinery and equipment breakdowns, enabling prompt maintenance measures, and drastically lowering downtimes. It does this by utilizing cutting-edge machine learning techniques. The methodology, data sources, model building, and real-world consequences of AI-based predictive maintenance in IIoT are all thoroughly examined in this study. © 2024 SPIE.

关键词： Learning systems

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Text steganography: enhanced character-level embedding algorithm using font attribute with increased resilience to statistical attacks

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Multimedia Tools and Applications 2025年第11期84卷 9367-9392页

作者： KL, Shreya Narasimhan R, Bala Krishnan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India

Text steganography, the science of hiding secret messages in innocent-looking text documents ensures the secrecy of the embedded secret. Cryptography, on the other hand, encrypts and converts the secret message into an unintelligible form ensuring confidentiality, integrity, and authentication of the message. It is to be noted that both steganography and cryptography are expected to work together hand-in-hand in order to attain a better security. Hence, a methodology that combines techniques from both steganography and cryptography is an unavoidable one for the complete protection of sensitive data. This paper aims to achieve one such methodology. The best existing method which works in similar direction is observed to use character spacing feature of text documents to embed the secrets. The method is reported to attain an average embedding capacity of ≈2 bits/cover-character and 8-bits/distortion with high imperceptibility and considerable cryptographic security. However, during experimentation, it is noticed that the methodology has the potential vulnerability of leaving clues for attackers with a probability of 0.51% which paves way for possible statistical attacks namely frequency analysis. This vulnerability occurred because of the constant left circular shift operation that is performed after embedding each secret character. This paper aims to address this vulnerability by proposing a novel modification that facilitates the methodology to perform both left and right circular shifts in an unbiased manner, based on the embedded secret, without affecting the notable achievements of the existing methodology. The proposed modification is successfully implemented, and further analysis indicated that the performed modification has in fact embedded the secret characters uniformly thereby reducing the standard deviation by 24%. This increased the resilience of the extended method to potential attacks that rely on statistical techniques. © The Author(s), under exclusive

关键词： Cryptography

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Encryption of medical data based on blockchain and multi-chaotic maps

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Multimedia Tools and Applications 2025年第17期84卷 17871-17892页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In this work, the SHA-256 mapper of the blockchain has been utilized to secure medical data from brute-force attacks. The uniform distribution and lower correlation of the encrypted data are achieved using the multi-chaotic in the encryption approach. Also, the circular shift method is employed in the approach to avoid differential attacks. The encryption approach is applied to secure the patient information, medical signals (ECG and EEG), and medical images (Grayscale, RGB, and Hyperspectral). The performance of the proposed approach has been analyzed for twenty signals and fifty medical images. Performance metrics such as MSE, SNR, PSNR, entropy, histogram, variance, chi-square, NPCR, UACI, and SSIM have been considered to check the resilience of the proposed approach against various types of attacks. The proposed approach satisfies the requirements of performance metrics by achieving higher MSE values with lower SNR and PSNR values for the encrypted data. Furthermore, the proposed methodology attains an entropy approaching 8, signifying robust randomness and resistance against entropy attacks. The SSIM values for encrypted images are close to 0, indicating significant dissimilarity from the original images. The NPCR values surpassing 99.60% and UACI exceeding 33.40% demonstrate resilience against differential attacks in the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Optimizing XGBoost for Enhanced Electrical Theft Detection: A Fusion of Particle Swarm and Jaya Optimization Techniques 4

Optimizing XGBoost for Enhanced Electrical Theft Detection: ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Prusty, Sashikanta Nayak, Debasish Swapnesh Kumar Moharana, Meena Prusty, Sushree Gayatri Priyadarsini Sahoo, Manmohan Rautaray, Jyotirmayee Department of Computer Science & Engineering Bhubaneswar India Centurion University of Technology & Management Department of Computer Science & Engineering Bhubaneswar India Department of Computer Science & Engineering Bhubaneswar India Odisha University of Technology & Research Department of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350383997

The most serious and hazardous for an electrical provider these days are non-technical losses caused due to electricity theft. The economy as a whole is impacted by fraudulent electricity usage, which lowers supply quality, raises generator load, and drives up costs for legal customers. To avoid these issues, this work proposes a smart electrical theft detection system using the XGBoost model combined with two optimization techniques Particle Swarm Optimization (PSO) and Jaya Optimization (JO). Furthermore, to handle the missing instances in the State Grid Corporation of China (SGCC) imbalanced dataset, we implemented a novel LORAS (Local Outlier-Robust Adaptive Synthetic) technique that balances the target class by making fake data points near the edges of different classes. Finally, the results show that the optimized XGBoost-PSO technique significantly classifies into two distinct categories (i.e. theft as '1' or no theft as '0') with having higher F1 Score (PSO Optimized): 0.9552 and AUC (PSO Optimized): 0.9667. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Content Moderation of Generative AI Prompts

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SN computer Science 2025年第4期6卷 1-28页

作者： Pardhi, Praful Computer Science and Engineering Department Shri Ramdeobaba College of Engineering and Management Nagpur India

Recent advances in Generative AI, have encouraged many industries to use generative models in their products for generating components such as images, text, audio, or video. Generative foundation models like Large Language Models and Diffusion Models are trained on large amounts of data scraped from the internet. Analysis with respect to the quality and content of this extensive data is not possible before training, which leads to generating inappropriate content that may contain profanity, violence, NSFW content, hate speech, and substance use. We cannot rely on the content the generative AI models may generate when the user prompts to generate the content. To generate fair and safe content the input prompt should be analyzed before generating the content to avoid generating unsafe content. If the prompt is unsafe the filters should avoid such a prompt or replace the improper and unsuitable words with the proper and suitable words. Here, we provide a framework for analyzing the text prompts based on the content moderation taxonomy. We define the taxonomy for content moderation and explore different language models to classify the prompt as per the moderation taxonomy. The proposed moderation taxonomy can be used to analyze user’s behavior and misuse of any generative AI application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content moderation Generative model Language model Large language models Prompt

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Energy efficient waste collection vehicle routing system using time series prediction

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Multimedia Tools and Applications 2025年第9期84卷 6157-6186页

作者： Kapadia, Neha Mehta, Rupa Computer Engineering Department Sardar Vallabhbhai National Institute of Technology University in Surat Gujarat India IT Department Thakur College of Engineering and Technology Mumbai India

The focus of this research is on addressing the inefficient management of Municipal Solid Waste (MSW), which often results in large quantities of waste being dumped into garbage bins. Previous approaches have struggled to effectively manage MSW promptly. To improve this process, we propose a time-series-based, energy-efficient waste prediction model. Initially, we convert the Bins Historical Time Series Dataset into a Comma-Separated Values (CSV) file format. The model, named Exponential Auto-Regressive Integrated Moving Average-Nesterov-accelerated Adaptive Moment estimation-Long-Short Term Memory (EARIMA-LSTM) using NADAM optimizer, is trained and tested using numeralized features extracted from the dataset. Smart bins equipped with Internet of Things (IoT) technology are then deployed to predict waste levels using the EARIMA-NADAM-LSTM model. After waste level detection, we employ the Scramble Mutation Coral Reefs Optimization-Haversine distance-based K-Means (SMCRO-HK-Means) clustering technique to group the bins. Next, we establish multiple paths between bins nearing capacity using the Ad-hoc On-Demand Distance Vector (AODV) protocol. Finally, the Optimal Path (OP) is determined using the modified A* algorithm based on multi-weight heuristics (Op A*). This approach effectively manages waste collection by preventing bin overflow. Our model achieves good accuracy for waste prediction model with a Mean Absolute Error (MAE) of 0.138%, and 42,518 ms for clustering 500 waste bins, outperforming existing methods. Also, it finds the dynamic optimal route for waste collection with optimal cost. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Fundamental Limits to Exploiting Side Information for CSI Feedback in Wireless Systems

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2417-2430页

作者： Kim, Heasung De Veciana, Gustavo Kim, Hyeji University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States

In modern wireless systems, the feedback of DownLink (DL) Channel State Information (CSI) from User Equipment (UE) to Base Stations (BS) may require substantial computational and feedback bandwidth overheads. A promising approach to improve feedback efficiency is to leverage side information which is correlated to DL CSI. Despite potential of doing so, critical aspects remain underexplored in current research, particularly the quantification of the benefits and the inherent limitations of utilizing side information. This paper addresses these gaps by introducing a novel algorithm to compute the rate-distortion function for general compression scenarios incorporating side information. We apply this algorithm to the DL CSI feedback problem having UL CSI as the side information and generate rate-distortion functions. Using the estimated rate-distortion functions, we measure the gain of side information over diverse feedback rates and UE mobility profiles. The results reveal that the benefits of leveraging side information are particularly significant for UEs characterized by high mobility and constrained to operate at low feedback overheads. © 1983-2012 IEEE.

关键词： Signal distortion

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