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Cognitive Transformation in Personal IoT: Pioneering Intelligent Automation

Cyber-Physical systems 2025年第2期11卷 183-240页

作者： Gulzar, Bisma Ahmad Sofi, Shabir Sholla, Sahil Department of Information Technology National Institute of Technology Srinagar India Department of Computer Science Engineering Islamic University of Science & Technology Srinagar India

In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing personal IoT applications. These objects use Neural Networks to predict optimal actions based on user behavior patterns. A Continuous Learning mechanism enables real-time adaptation of the network to evolving user interactions. The study highlights the role of Cognitive Transformation in Personal IoT, driving intelligent automation and enhancing user experience. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of Things (IoT) Personal Internet of Things (PIoT) Social Internet of Things (SIoT) IoT architecture network infrastructure

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Enhancing rheology and mechanical properties of DLP 3D-printed Si_(3)N_(4) materials via composition optimization and gas-pressure sintering

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International Journal of Minerals,Metallurgy and Materials 2025年第5期32卷 1220-1233页

作者： Qing Qin Gang Xiong Lin Han Yujuan Zhang Zhen Shen Changchun Ge Institute of Powder Metallurgy and Advanced Ceramics School of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and TechnologyInstitute of AutomationChinese Academy of SciencesBeijing 100190China

Digital light processing(DLP)is a crucial additive manufacturing(AM)technique for producing high-precision ceramic *** study aims to optimize the formulation of Si_(3)N_(4)slurry to enhance both its performance and manufacturability in the DLP process,and investigate key factors such as particle size distribution,photopolymer resin monomer ratios,and dispersant types to im-prove the slurry’s rheological *** these optimizations,a photosensitive Si_(3)N_(4)slurry with 50vol%solid content was de-veloped,exhibiting excellent stability,and low viscosity(2.48 Pa·s at a shear rate of 12.8 s^(-1)).The effects of gas-pressure sintering on the material’s phase composition,microstructure,and mechanical properties were further explored,revealing that this technique significantly increases the flexural strength of the green sample from(109±10.24)to(618±42.15)*** sintered ceramics exhibited high hard-ness((16.59±0.05)GPa)and improved fracture toughness((4.45±0.03)MPa·m^(1/2)).Crack trajectory analysis revealed that crack deflec-tion,crack bridging,and the pull-out of rod-likeβ-Si_(3)N_(4)grains,are the main toughening mechanisms,which could effectively mitigate crack *** these mechanisms,crack deflection and bridging were particularly influential,significantly enhancing the frac-ture toughness of the Si_(3)N_(4)***,this research highlights how monomer formulation and gas-pressure sintering strengthen the performance of Si_(3)N_(4)slurry in the DLP three-dimensional printing *** work is expected to provide new insights for fabricat-ing complex Si_(3)N_(4)ceramic components with superior mechanical properties.

关键词： additive manufacturing Si_(3)N_(4)slurry low viscosity pressure sintering

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

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computers, Materials & Continua 2023年第7期76卷 1027-1045页

作者： Sharad Saxena Shailendra Mishra Mohammed Baljon Shamiksha Mishra Sunil Kumar Sharma Prakhar Goel Shubham Gupta Vinay Kishore Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjab147001India Department of Computer Engineering College of Computer&Information ScienceMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Engineering National Institute of TechnologyJamshedpur831001India

In recent years,the growth of female employees in the commercial market and industries has *** a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s *** exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and *** the time of the attack,it appears the women were immobilized and needed immediate *** self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or *** proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud *** is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud *** can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location *** has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 *** average activation time is 2.079 s.

关键词： Perpetrator safety IoT women security GPS tracking GSM

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Manhattan-based k-member clustering and enhanced rabbit optimization algorithm for k-anonymization in social network

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Multimedia Tools and Applications 2024年第30期83卷 74739-74756页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of computer science and engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of computing technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier

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International Journal of Wireless and Mobile Computing 2024年第2期26卷 168-181页

作者： Vani, B.V. Guruprakash, C.D. Information Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumkur India Department Computer Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumkur India

Predicting crop disease on the image obtained from the affected crop has been a potential research topic. In this research, the Localise Search Optimisation Algorithm (LSOA) enabled deep Convolutional Neural Network (deep CNN) is used to predict the crop disease for which the dominant statistical and texture features are utilised and LSOA as a training algorithm. The experiments were done on an apple data set and a corn data set, and the results show that the LSOA-deep CNN model attains 98.474% of accuracy, 92.837% of sensitivity and 99.00% of specificity in k-fold training data and 94.683% of accuracy, 95.489% of specificity and 99.00% of specificity with 80% training data for the corn data set. With the apple data set, the developed method achieves 94.587% of accuracy, 99.00% sensitivity and 99.00% specificity under k-fold training, while for the 80% of training, 97.959% accuracy, 96.233% sensitivity and 99.005% specificity are attained. © 2024 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

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Improved security with novel M-Log chaos steganography algorithm for huffman compressed english text

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Multimedia Tools and Applications 2025年第2期84卷 665-688页

作者： Raghuvanshi, Devvrat Joshi, Kamaldeep Nandal, Rainu Sehrawat, Harkesh Singh, Surender Singh, Srishti Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Haryana India

This paper addresses the underexplored landscape of chaotic functions in steganography, existing literature when examined under PRISMA-ScR framework it was realized that most of the studies predominantly focuses on utilizing chaotic functions for either concealing or diffusing messages within cover images. This study explored a different direction of using chaotic function as guards for concealed data by proposing the M-Log chaos algorithm which employs logarithmic chaotic function to drive a unique bomb generator equation, strategically placing bomb bits in the image. These bombs act as sentinels, safeguarding the concealed message. Successful retrieval involves diffusing the bombs before extracting data from the rest of the image. Failure to defuse these bombs prior to access results in irreversible damage to concealed information, thwarting unauthorized access. Our proposed method demonstrates notable advancements across quantitative performance metrics, including a 40% increase in payload capacity, a mean PSNR of 45.8981, and a mean SSIM of 0.9931. Future avenues include exploring multi-dimensional chaotic functions and employing nature-based searching algorithms such as genetic algorithms or particle swarm optimization for optimal chaos bomb placement. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Steganography

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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