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Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

computer Systems Science & engineering 2023年第2期44卷 1531-1549页

作者： N.Krishnammal C.Kalaiarasan A.Bharathi Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia School of Engineering Presidency UniversityBengaluruIndia Department of Information Technology Bannari Amman Institute of TechnologySathyamangalamIndia

Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as signiﬁcant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identiﬁed that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efﬁciency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.

关键词： Wireless mesh access networks mobile ad-hoc network reinforcement learning multi-objective constraint asymmetric link

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Atrous Convolution-Based Fusion Attention Mechanism for Brain Tumor Segmentation

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IEEE Access 2025年 13卷 92611-92621页

作者： Jadhav, Abhishek Rasool, Akhtar Gyanchandani, Manasi Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

Accurate medical image segmentation is pivotal for advanced diagnostic and therapeutic planning, especially for intricate tasks such as brain tumor delineation. However, existing segmentation methods often struggle with challenges such as heterogeneous tumor morphologies, diffuse boundaries, and low-contrast regions, limiting their reliability in complex medical imaging scenarios. In this work, we introduce an Atrous Convolution-Based Fusion Attention Mechanism, a novel framework that combines local and global attention through an innovative fusion block. Local attention leverages atrous convolutions to capture fine-grained spatial features at multiple scales, while global attention extracts long-range contextual dependencies across the image. These complementary features are dynamically fused via a novel fusion block, ensuring balanced representation and robust feature aggregation for precise segmentation. The proposed model is rigorously evaluated on the BraTS 2019, 2020, and 2023 datasets, demonstrating consistent and significant improvements across key segmentation metrics compared to baseline models. Quantitatively, the model achieves an absolute average increase of 0.2522, 0.1873, and 0.1934 in the Dice Coefficient, and 0.3071, 0.2365, and 0.2369 in the Intersection over Union (IoU) for enhancing tumor, tumor core, and whole tumor, respectively, across all datasets. The results highlight robustness and adaptability of the proposed architecture, underlining its potential to address critical challenges in brain tumor segmentation and advance the state-of-the-art in medical image analysis. © 2013 IEEE.

关键词： Image segmentation

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Spider monkey ınspired optimization algorithm based clustering for face recognition

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International Journal of Information technology (Singapore) 2024年 1-13页

作者： Gopal, Veni devi Devi, G. Shree Geetha, Angelina Department of Computer Science and Engineering B S Abdur Rahman Institute of Science & amp Technology Chennai India Department of Computer Science and Engineering Hindustan Institute of Science and Technology Chennai India Dept of Computer Applications BSA Crescent Institute of Science and Technology Chennai India

Face recognition is a fast-growing technology that is widely used in forensics such as criminal identification, secure access, and prison *** contrasts from other classification issues in that there are normally a more number of classes in face recognition. Thus, the validation accuracy of the conventional subsystem face recognition is unsuitable. In order to overcome the problems that are likely to be encountered, the proposed method utilizes the Spider Monkey inspired Optimization (SMO) algorithm for face clustering and recognition. At first feature reduction is done by Advance Principal Component Analysis (APCA) which is a hybridization of PCA and linear discriminant analysis (LDA). After reducing the features, the selected features are grouped and faces are recognized by means of SMO algorithm. From the food search behavior of spider monkeys, SMO algorithm is developed. The performance of the proposed method is evaluated by means of sensitivity, specificity, and accuracy. Here the proposed method accomplishes 98.66% accuracy. Using the MATLAB platform, the recommended technique is implemented. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Accuracy Linear discriminant analysis Principal component analysis Sensitivity Specificity Spider monkey optimization

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Service Quality and Experience Based Service Selection Framework in Cloud 4

Service Quality and Experience Based Service Selection Frame...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yadav, Anurag Yadav, Neeraj Goraya, Major Singh Indian Institute of Technology Department of Computer Science and Engineering Roorkee India Bennett University Department of Computer Science and Engineering Greater Noida India Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India

ISBN: (纸本)9798350395914

Selecting a cloud service provider (CSP) from a wide range of options with varying offerings poses a crucial challenge for service requesting consumers (SRCs). The decision criteria outlined in Service Level Agreement (SLA), which defines the Quality of Service (QoS) offered by the CSP, help in selecting a suitable provider. However, relying solely on the SLA for evaluating the merits of a CSP is insufficient. Therefore, the Quality of Experience (QoE) has gained significance in assessing the overall satisfaction of the (SRC). Additionally, monitoring consumer buying behavior is essential from a business perspective, as all aspects of a business transaction should be mutually beneficial for both the CSP and SRC. Therefore, a new framework is proposed to determine the relative rankings of CSPs and SRCs. Effectiveness of the framewo rk is established through experiments on the QWS cloud dataset, which reveal positive results in terms of implementation complexity and time required. © 2024 IEEE.

关键词： Cloud platforms

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ConciseNet: A Robust Model for Enhancing Signal Reconstruction in Wireless Sensor Networks 4

ConciseNet: A Robust Model for Enhancing Signal Reconstructi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumari, P. Meena Sekar, K. Jagadeesh, P. Dheepa, T. Avn Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India National Institute of Technology Silchar Department of Computer Science and Engineering Assam Silchar India Barath University Department of Computer Science and Engineering Tamilnadu India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350343670

This paper introduces a novel data collection model called ConciseNet that leverages a cutting-edge data reconstruction method to significantly enhance signal reconstruction accuracy in wireless sensor networks (WSNs). In traditional WSNs, data collection is prone to errors and losses, especially in long-range scenarios. The proposed model addresses this challenge by employing ConciseNet, an attentive sequential model that effectively compresses sensor signals without compromising information fidelity. The unique strength of ConciseNet lies in its ability to capture essential features from raw sensor data, allowing for efficient and precise reconstruction over extended periods. The result is a more robust and accurate representation of collected data, thereby improving the quality of information gathered by the WSN and maximizing the lifetime of sensors and WSNs. © 2024 IEEE.

关键词： Compressive sensing Data Reconstruction Time dependency Transformer Neural Network Wireless Sensor Network

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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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IoT-Enabled Intelligent Framework for Real-Time Mosquito Detection and Monitoring

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SN computer Science 2025年第5期6卷 1-14页

作者： Seervi, Aman Mulani, Naman Anwar, Mohammed Junaid Venkanna, U. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India

The increasing spread of diseases transmitted by mosquitoes, including malaria and dengue, poses a major global health challenge. Traditional mosquito detection methods, which are based on manual trapping and counting, are time-consuming and inefficient for continuous monitoring. Recently, sensor-based systems have been developed that utilize acoustic signatures. Still, their effectiveness is limited by deep learning models that struggle with noisy environments and fail to adapt to new conditions. Additionally, the scarcity of labeled data for training these models remains a significant obstacle, further reducing their accuracy and generalizability. This paper proposes a novel approach to overcome these limitations by developing an adaptable pipeline to create environment-specific deep-learning models for mosquito detection at diverse locations. This study addresses the challenge of data scarcity and evaluates various feature extraction strategies, such as log-mel and per-channel energy normalization (PCEN), can enhance model robustness in different environmental settings. Our proposed solution successfully creates models that achieve accuracy greater than 90% for any given environment, improving adaptability and supporting public health efforts to control vector-borne diseases. Experimental results confirm this by testing CNN and TCN models in different environments. PCEN preprocessing outperformed log Mel, with the CNN model achieving the highest accuracy of 93.25% in the open environment. Cross-testing results further justify the approach of using environment-specific models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Machine learning Mosquito acoustic event detection PCEN

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Multi-class Classification Approach for Smart Intrusion Attack Detection Using AI and ML Classifiers 8th

Multi-class Classification Approach for Smart Intrusion Atta...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Prajapati, Ramesh Rathod, Ghanshyam Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE Faculty of Engineering and Technology GLS University Gujarat Ahmedabad India Computer Engineering Department Shree Swaminarayan Institute of Technology Gujarat Gandhinagar India Department of Computer Application Parul Institute of Computer Application Baroda India

ISBN: (纸本)9789819766772

Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion categories and demand a high processing power usage. Notwithstanding the fact that there is a wealth of literature on IDS issues, our objective is to provide a more thorough review. We offer the taxonomy to differentiate between modern IDSs based on the extensive survey and intricate organization. In addition, the architecture we provided in the content aids the reader in grasping the whole idea of IDSs with ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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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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A no-code swarm simulation framework for agent-based modeling using nature-inspired algorithms

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International Journal of Information technology (Singapore) 2024年第7期16卷 4693-4699页

作者： Hasan, Ishraq Islam, Rubyeat Sharmin, Nusrat Md. Akhtaruzzaman Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Swarm robotics describes the coordination among multiple robots assigned to perform a single task collectively and work as a system. The system is usually used in search and-rescue missions in adverse natural environments. Research in this field involves testing theories and algorithms by using physical robots which requires expensive environment setup. This study presents a novel no-code simulation framework for swarm robotics. The software framework provides a set of building block of various components to customize the research and development process. Besides the various platforms that exist nowadays, this proposed framework overcomes the common problem of low flexibility in modeling, resulting significant improvements in the development of any simulation through customization of algorithms and multi-agent modeling. For rapid development of simulation instances, this study proposes a swarm simulation framework that allows multi-agent modeling in a no-code development environment by defining the number of agents and the behaviors they are expected to exhibit. A random environment is generated where the agents and specific target is defined to test swarm robots for search and rescue operations. The framework mainly proposes a no-code environment and provides the opportunity to test the swarm behavior and relevant theories with flexibilities to make necessary adjustments. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Multi-agent modeling No-code environment Simulation framework Swarm robotics

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