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A self-organized priority-based MAC protocol in wireless sensor networks based on SDR-RHSO optimal relay node selection and HL-ANN wake-up scheduling

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11093-11102页

作者： Thalagondapati, Veeraiah Singh, Maheshwari Prasad Computer Science and Engineering Mahindra University Telangana Hyderabad India Computer Science and Engineering National Institute of Technology-Patna Bihar Patna India

A budding technology that possesses an extensive range of potential applications comprising environmental monitoring, medical systems, smart spaces, along with robotic exploration is a wireless sensor network (WSN). Medium Access Control (MAC) is a vital technique that evades collisions such that two interfering nodes do not transfer simultaneously for the WSN’s successful operation. However, owing to high energy consumption (EC), non-scalability, Delay, etc., designing an effective MAC protocol for WSN is a complex task. An efficient self-organized MAC protocol in WSN is formed by the work for conquering the issue. Utilizing Linear Scaling centered Social Spider Optimization Algorithm (LS-SSOA), the work is initiated with optimal cluster head (CH) selection under the Mahalanobis Distance centered K-Means Clustering (MD-KMC) technique, which extends the network lifetime (NLT) along with the stability of WSN. Afterward, an optimal relay node (RN) selection is created using Swap Displacement and Reverse-Based Rock Hyraxes Swarm Optimization (SDR-RHSO) for minimizing the energy loss throughout data transmission from sensor node (SN) to CH. Lastly, by identifying the active nodes and non-active nodes using Hebbian Learning-centered Artificial Neural Network (HL-ANN), a wake-up scheduling is performed betwixt the nodes that decrease the extra EC along with delay. A better network lifetime, lower energy consumption, higher throughput value, and accurate wake-up scheduling is attained by the work as exhibited by the experimental analysis and stays effective as analogized to the existing top-notch method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Scheduling

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on Robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Enhancing Malware Detection: A Comparative Analysis of Ensemble Learning Approaches 1

Enhancing Malware Detection: A Comparative Analysis of Ensem...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Ajvad Haneef, K. Shashank, N.S. Madhu Kumar, S.D. National Institute of Technology Department of Computer Science & Engineering Calicut India National Institute of Technology Department of Computer Science & Engineering Calicut India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392111

Malware represents the greatest threat to cybersecurity and serves as the weapon of choice for carrying out nefarious activities in the digital realm. The proliferation of malware poses significant risks to individuals and organizations. Therefore, malware detection is essential for companies to strengthen their cyberspace, protect sensitive data and minimize financial losses from cyberattacks. Ensemble learning methods have emerged as powerful tools in this domain, leveraging the strengths of multiple models to enhance detection accuracy and reliability. We present a comparative analysis of ensemble learning approaches for malware detection using the EMBER dataset. We evaluate six ensemble models, including Random Forest, AdaBoost, Extra Trees, XGBoost, LightGBM, and Bagging, based on key performance metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that all ensemble models demonstrate strong performance in malware detection, with Bagging ensemble achieving the highest accuracy and overall performance. The results highlight the effectiveness of ensemble learning techniques in enhancing malware detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

An Intelligent Analysis of Mobile Evidence Using Sentimental...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Jones, G. Maria Santhiya, P. Winster, S. Godfrey Sundar, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology and Panimalar Engineering College Chennai India

ISBN: (纸本)9789819950904

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Enhanced Malware Image Classification through Ensemble model 3

Enhanced Malware Image Classification through Ensemble model

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kotha, Eshwar Palivela, Lakshmi Harika Begum, Afruza Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Classification of malwares and viruses is a very important work in the cyber security field to protect the computers and systems from threats and attacks. In this paper, we proposed a novel approach for the classification of the malwares by combining 2 models EfficientNet B0 and EffiCBNet models. Here we have used Malimg dataset which is available in Kaggle and it contains around 9,339 malware images and these malwares are from 25 different families, for evaluating the models. EffcientNetB0 is a Convolution Neural Network (CNN) model, which showed great and high results on various image classification works. We used transfer learning to fine tune the EfficientNet model;whereas the proposed EffiCBNet model is a deep learning model which contains of convolution layers and batch normalization layers. The results have shown that the proposed model has given the highest accuracy of 99.70 on the dataset we have used, exceeding all other models. The proposed model is the combination of EfficientNet and EffiCBNet, which showed great results for the classification. The proposed model can also be used in real time applications for detecting malware attacks and threats © 2024 IEEE.

关键词： Image classification

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Manipify:An Automated Framework for Detecting Manipulators in Twitter Trends

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Journal of Social Computing 2023年第1期4卷 46-61页

作者： Soufia Kausar Bilal Tahir Muhammad Amir Mehmood Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan

The rapid adoption of online social media platforms has transformed the way of communication and *** these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in ***,these trends are used for political campaigns,public awareness,and brand ***,these trends are sensitive to manipulation by malicious users who aim to mislead the mass *** this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in *** propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter *** framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend *** user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour ***,the module classifies human and bot ***,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different ***,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user *** user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,*** further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million *** analysis of trends reveals that the trending panel is mostly dominated by political *** addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.

关键词： trend manipulation bot classification user analysis text classification manipulator detection

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Railway track fault detection using optimised convolution neural network

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International Journal of Reliability and Safety 2024年第2期18卷 163-186页

作者： Chitra, R. Bamini, A.M. Anusha Brindha, D. Jegan, T.M. Chenthil Kirubakaran, S. Stewart Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Mechanical Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

Railway accidents are an under-scrutinised cause of death in India. Train accidents are caused by various consequences of collisions, derailments, signal errors and so on. Furthermore, when train derailments become disastrous, they can have tremendous repercussions. It is practically difficult to identify the cause of the derailment efficiently within a limited period. In recent years, we have been making progress in reducing derailments, but even if not deadly, identifying faulty tracks can waste a lot of time and money. And doing this error-free is a pressing matter, as tracks always experience wear and tear with more usage. Here is where neural networks can pitch in their solution. We can train a model to look at train tracks and identify any issues. This paper goes into the methodology of achieving this and optimising a neural network to predict problems in the track with the best possible accuracy that images can provide. The objective of this paper is to identify, develop and optimise neural networks to detect faulty tracks. In this work, a good Convolution Neural Network model is developed to identify the crack in the railway track. The developed model produced 95.54% accuracy in fault classification. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Derailments

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A Novel Deep Reinforcement Learning Approach for Stability-Based Parallel Disassembly Sequence Planning Problem

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SN computer science 2025年第3期6卷 1-19页

作者： Kumar, M. R. Mahesh Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Parallel Disassembly Sequence Planning (DSP) deals with obtaining the order of disassembling the product with multiple parts disassembling simultaneously. Existing studies derive the product’s disassembly order based on the requirement for liaison data. However, the absence of stability data does not ensure that all the product parts are disassembled without damage. Since the order of retrieval of the product can also be obtained from stability data, this reduces the requirement of an initial precedence graph based on liaison data. The complexity of the problem increases if we adopt different representations for precedence and stability conditions of the product for constraint checking throughout the disassembly process. This paper constructs a stability-based precedence graph based on only the product’s stability data. The developed graph represents the precedence of the stable parts for disassembly, and the same is utilized to formulate the problem as a model-free deep Reinforcement Learning (RL) problem. A novel Exploration–Exploitation (EE) strategy-based Deep Q-Learning (DQL) algorithm is proposed to parallel DSP minimizing direction changes. Our effective reward mechanism accelerates the learning process for all the conditions and objectives of the problem considered. Based on past performance, the proposed algorithm dynamically decides the exploration and exploitation strategy to avoid local optima. We applied the proposed algorithm on five different-scale products and compared the results with uniform-sampling-based model-free deep RL algorithms. It showed significant improvements, particularly in cumulative rewards and disassembling all the parts for large-scale products with early convergence. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Q-learning Deep Q-Networks Disassembly sequence planning Parallel disassembly Precedence graph Stability graph

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