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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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A Smart Approach to Coconut Leaf Spot Disease Classification using computer Vision and Deep Learning Technique

A Smart Approach to Coconut Leaf Spot Disease Classification...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Brar, Simrat Kaur Sharma, Rishabh Vats, Satvik Kukreja, Vinay Chandigarh Group of Colleges Department of Computer Applications Landran India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350311204

Coconut leaf spot (CLS) disease is a major threat to coconut production and can cause severe economic losses. In this study, we propose a deep learning (DL)-based ResNext50 model for automated detection and severity classification of CLS disease. Our model leverages a ResNext50 mode;and is trained and tested on a dataset of coconut leaf images with six severity levels, ranging from healthy leaves to critical severity. The proposed approach achieves high accuracy in detecting and classifying the severity levels of the disease. Our findings suggest that the proposed method is successful in properly identifying and categorizing the illness severity levels, with an accuracy rate of 91.77% overall. The strategy that has been presented has the possibility to significantly improve the efficiency and accuracy of CLS disease detection and monitoring, ultimately leading to better management strategies and increased productivity in the coconut industry. © 2023 IEEE.

关键词： computer vision

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Graph Encoding-Enhanced Transformer for Drug Recommendation

Graph Encoding-Enhanced Transformer for Drug Recommendation

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Cai, Xunsheng Thamrin, Syauki Aulia Chen, Arbee L.P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350324457

Doctors prescribe drugs for the patient with the objective of curing the patient. Some drugs cannot be consumed together since doing so may cause negative effects. This can be avoided by knowing the effects caused by consuming combinations of drugs. However, for complex cases of a patient, it can be difficult to decide the best combination of drugs. Therefore, automatic drug recommendation method was used to recommend drugs with minimal negative effects. It is performed by using a deep learning model which is trained on drug data. A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about the combination of drugs prescribed in the past for a patient is also important for drug recommendation. It can also be represented as a graph called drug concurrence relation (DCR). The DDI and DCR graphs can be input to the deep learning model through an encoding process. In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network (GAT). The graph encoding model has multi-head attention, which makes the GEET model aware of the most important DDI and DCR from the graphs. Additionally, the encoding outputs are combined, and activation function and normalization methods are used to improve the performance. The model has been evaluated on the publicly available MIMIC-III dataset and has the best results on F1, Jaccard and PRAUC scores compared to the models proposed by the existing related research papers. © 2023 IEEE.

关键词： drug concurrence relation drug graph encoding Drug recommendation drug-drug interaction graph attention network transformer

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Sentiment classification of reviews using combination of Firefly Algorithm and ANN 21

Sentiment classification of reviews using combination of Fir...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Rao, Ch. Chakradhara Pandey, Trilok Nath KIIT Deemed to Be University School of Computer Applications Odisha Bhubaneswar India Raghu Engineering College Department of Computer Sc. & Engg AP Viskahapatnam India Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350358230

With the availability of many social media sites, the reviews and comments by the customers are freely available for analysis. Most of these reviews are textual as per the content ***, proper processing need to be done for future use of the reviews. As reviews are in text, the amount of words need to be processed for any analysis work is very large in amount. So, an approach needs to be adopted to minimize the amount of word without affecting the result of the analysis. Thus, in this paper the Firefly algorithm is used to select the words / features that can act as a representative of the whole set of words. The output of Firefly algorithm i.e., selected features act as input of ANN. In this paper, a hybridization of Firefly algorithm and ANN is carried out for better accuracy result. The result of the proposed approach is analyzed by using confusion matrix and various evaluation parameters. © 2023 IEEE.

关键词： Sentiment analysis

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Intelligent traffic controller

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International Journal of Information Technology (Singapore) 2022年第4期14卷 2141-2153页

作者： Kumar, Sachin Baliyan, Anupam Tiwari, Anurag Tripathi, Aniket Kumar Jaiswal, Balram Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad India Bharati Vidyapeeth’s Institute of Computer Applications and Management New Delhi India

This paper explores the application of dynamic traffic control timings using predefined input parameters. The method is a dynamic traffic algorithm that takes the rate of inflow, rate of outflow and queue length as input parameters to estimate the green-time that must be allocated to each road. The basic idea is to efficiently distribute the green-time based on traffic congestion in contrast to traditional methods of fixed time for traffic lights irrespective of their traffic status. The paper also compares the differences between these two methods based on some efficiency parameters using a simulator. © 2019, Bharati Vidyapeeth's institute of computer applications and Management.

关键词： Average waiting time Congestion Control IOT Queue length

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Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Sentiment analysis of reviews using Nature Inspired PSO and ANN

Sentiment analysis of reviews using Nature Inspired PSO and ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Rao, Ch. Chakradhara Pandey, Trilok Nath Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India Raghu Engineering College Department of Computer Sc. & Engg AP Viskahapatnam India Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350308167

Social media now plays an important role is sharing the views or comments about a topic. These comments are available to the users for their processing and future decision making process. As the reviews contains mostly text or emojis, thus, processing is required to obtained the required information. Nature is always a source for inspiration and *** activities of the nature can act as a source for optimization and classification. In this paper, two nature inspired algorithms are considered for analysis. Firstly, Particle Swarm Optimization (PSO) act as an optimization technique for feature selection i.e., best features are selected and these best features act as input to Artificial Neural Network (ANN) for further classification purpose. The reviews are processed using the proposed algorithms and finally, the confusion matrix is generated, which helps to generate the evaluation parameters to evaluate the results. © 2023 IEEE.

关键词： Artificial Neural Network (ANN) Confusion matrix Particle Swarm Optimization(PSO) Performance evaluation parameter Sentiment analysis

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CDCAT: A Multi-Language Cross-Document Entity and Event Coreference Annotation Tool

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Tsinghua science and Technology 2022年第3期27卷 589-598页

作者： Yang Xu Boming Xia Yueliang Wan Fan Zhang Jiabo Xu Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China Research Institute with Run Technologies Company Ltd.Beijing 100192China School of Information Engineering Xinjiang Institute of EngineeringUrumqi 830091China

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition(CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool(CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.

关键词： event coreference entity coreference manual annotation tool natural language processing

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Hybrid Classifier for Software Defect Prediction by Using Filter-Based Feature Selection Techniques 3rd

Hybrid Classifier for Software Defect Prediction by Using Fi...

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Kumar, Raghvendra Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur603203 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Gunupur India

ISBN: (纸本)9789819705726

Software’s capacity for error-free operation determines how reliable it is. Unfortunately, mistakes can happen at any stage of the software development process. Predicting software flaws early on in the development process is now of utmost importance in the field of software engineering. Using classification techniques, which are a popular strategy for predicting software faults, modules that are illustrated by a group of metrics or code properties are divided into defective or non-defective categories. However, there are low-quality, unreliable, redundant, and noisy data sources that have an adverse impact on the process of observing information and helpful patterns. Researchers must therefore use feature selection techniques to extract pertinent info from massive databases. In feature selection, the redundant and unnecessary features are eliminated in order to focus on the most significant ones. In this paper, we looked in to the impact of filter-based feature selection on classification techniques in software defect prediction. NASA and the Metric Data Program software repository both include publicly available datasets that were used in this study. Principal component analysis (PCA) and CFS was used to assess the dataset’s top discriminator features. The datasets were classified by using hybrid ensemble learning, for hybrid ensemble learning classifiers like DT, SVM, K-NN, NB, and LR were used. The experimental findings showed that it is preferable and should be encouraged to apply feature selection to datasets prior to classification in order to anticipate software defects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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