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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

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Multimedia Tools and Applications 2025年第14期84卷 14047-14070页

作者： Vedavathi, N. Suhas Bharadwaj, R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru570002 India Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysuru570008 India

Food Infestation Detection is more important for food safety and health concerns. It is a challenging task to separate the grains into infested or non-infested. It is found that in the existing system, there is no effective approach for the early detection of pest populations and for classifying crops as infested or non-infested. Therefore, the identification and categorization of early-stage grain infestations is in high demand. To address the above problem, this research presents a better technique for detecting and classifying infested and non-infested grains. Initially, the maize, wheat and olive image datasets are collected, which are then pre-processed using data normalization and adaptive wiener filtering (AdwF) approach to improve the excellence of the input images. Image quality helps in achieving better performance and also helps in analyzing the quality of food grains effectively. Feature extraction is done using the Dense Residual Pyramidal Dilated Convolution Network (DR-PDCNet) technique, and feature selection is done using the Linear Discriminant Coati Optimization (LD-CoA) algorithm. The Hybrid Convolutional Bi-LSTM based Deep Maxout Network (HCBiL-DMN) model is used to detect the infected food grains in the preserved food grains with high accuracy. This research explores the potential and future possibilities for using technology to ensure the quality, assessment, and monitoring of nutritional grains during storage. The performance results are achieved with 98.3% accuracy, 99.12% recall, 98.65% precision, 98.02% MCC, and 99.32% F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Securing the Mobile Future: An Extensive Analysis of the Threat Landscape from Mobile Devices User Perspectives

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第8期19卷 140-158页

作者： Aljamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Mughaid, Ala Al-Jamal, Wafa’ Q. Twairesh, Abdullah Ewayed Department of Cybersecurity Irbid National University Irbid Jordan Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Gulf University of Science and Technology Mubarak Al-Abdullah Kuwait Nilai Malaysia Department of Finance and Insurance College of Business Administration Northern Border University Arar Saudi Arabia

Mobile devices are becoming increasingly popular in the modern era, posing a significant threat to the security of organizations. As a result, mobile devices have become more and more vul-nerable to cyberattacks. The current study aims to assess the threat landscape of mobile devices from the perspectives of users, highlighting the importance of user-centered perspectives in designing solutions to mobile security threats. A total of 47 participants of different demographic profiles were recruited as respondents, and the primary data were collected through an online questionnaire. The study concludes with a discussion of the findings’ implications for research and practice in mobile security. The paper outlines the current state of the art in mobile device security, including vulnerability to security threats, privacy risks, trust in security measures, awareness of potential threats, and satisfaction with the security provided by mobile operating systems. The results demonstrated that users are unaware of data protection and mobile device security, which have a substantial impact on an organization’s performance. However, this study lays a foundational perspective for future advancements in security audits, aiming to enhance cybersecurity practices in an increasingly mobile-centric corporate landscape. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cyber attacks

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Deep Learning-Driven Edge-Enabled Serverless Architectures for Animal Emotion Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 33-48页

作者： Benedict, Shajulin Subair, Rubiya Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala India Technical University Munich Garching Germany

Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverless-based solutions, both of which are affordable. Treating the emotions of animals increases their productivity, especially among trained elephants when subjected to carrying logs or undertaking gargantuan tasks. However, existing infrastructures are inefficient in handling long-running animal emotion detection-related tasks. This article proposes a deep learning-driven edge-enabled serverless architecture after evaluating several existing animal emotion detection techniques. Additionally, we perform an exploratory study on the cost impact of incorporating serverless-enabled approaches to animal emotion detection architectures. We observed that the proposed edge-enabled serverless architectures saved over 13,000 dollars annually compared to traditional animal emotion detection approaches. In addition, the article provided a few research directions to develop novel edge-enabled serverless architectures that boost socio-economic situations while avoiding human-animal conflicts. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Invertebrates

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Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

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Iran Journal of computer science 2025年 1-28页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm Task scheduling problem

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Intelligent Healthcare Applications Based on GraphModo: A Comparative Study of Deep Learning Models for Lung Anomaly Detection 24

Intelligent Healthcare Applications Based on GraphModo: A Co...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Yang, Peng Zhou, Yanyang Liu, Yiliang Zeng, Zhifeng Department of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798400717826

The aim of this study was to build ten novel deep learning models using the GraphModo system and then evaluate their ability to detect lung abnormalities in medical *** research employs accuracy, precision, recall, F1 score, and AUC value as evaluation metrics for a comprehensive assessment of model performance. Experiments are based on the ChestX-ray14 and LUNA16 datasets. The results indicate that DenseNet and EfficientNet achieve high accuracy in identifying lung diseases, while the Vision Transformer (ViT) excels in capturing global features of images. Additionally, U-Net demonstrates outstanding performance in CT image segmentation tasks, and MobileNetV3 maintains a high accuracy level in resource-constrained environments. These findings not only confirm the feasibility of model construction using the GraphModo system but also reveal its potential in assisting clinical decision-making, providing robust data support for model selection in medical image analysis. © 2024 Copyright held by the owner/author(s).

关键词： Lung cancer

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Multi-domain feature-enhanced attribute updater for generalized zero-shot learning

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Neural Computing and Applications 2025年第14期37卷 8397-8414页

作者： Shi, Yuyan Jiang, Chenyi Song, Feifan Ye, Qiaolin Long, Yang Zhang, Haofeng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science Durham University DurhamDH1 3LE United Kingdom

The goal of generalized zero-shot learning (GZSL) is to transfer knowledge from seen classes to unseen classes. However, a significant challenge is the single-category attributes are often inadequate to capture the intra-class variability of diverse image samples. During training, the model tends to cluster samples of seen categories based solely on semantic descriptions, overlooking subtle yet important visual differences. To address these challenges, this paper introduces an instance-level attribute updating method, termed as multi-domain feature-enhanced attribute updater (MDAU), which establishes a one-to-one alignment between visual and semantic by obtaining multiple instance-level attributes that closely correspond to the natural intra-class distribution of the image, in contrast to the many-to-one alignment typically used in traditional GZSL approaches. Specifically, we initially compute feature weights for each individual image, and then, the visual information is incorporated as conditions to facilitate the personalized updating of class attributes. We evaluate our model on three benchmark datasets, the results show that our model can achieve the state-of-the-art performance and detailed analysis also illustrates the effectiveness of each proposed module. Code is available at https://***/S-Silvia/MDAU. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Zero-shot learning

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