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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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Distributed Energy Bank Optimization Towards Outage Aware Sustainable Cellular Networks

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 503-514页

作者： Balakrishnan, Ashutosh De, Swades Wang, Li-Chun Delhi Department of Electrical Engineering New Delhi India IIT Delhi Department of Electrical Engineering Bharti School of Telecommunication New Delhi India Department of Electrical and Computer Engineering Taiwan

Grid connected and solar powered base stations (BSs) acting as distributed energy sources are increasingly becoming a popular solution to mobile operators. These networks experience double stochasticity due to the space-time variations in energy harvest and BS traffic. Hence, accurate and efficient green energy outage estimation in such networks is a challenging task. In this work, we propose a complutationally efficient cooperative energy transfer based distributed energy bank strategy to alleviate green energy outage and design energy sustainable networks. We first develop low-complexity Markovian frameworks to estimate green energy outage in a standalone BS without energy cooperation (WEC) and a multi-BS energy-cooperative (EC) setting, respectively. For the WEC system, we present a computationally efficient three-state discrete time Markovian statistical model, while the multi-BS EC framework is characterized by a two-state Markov model. The energy outage is studied as a function of capital expenditure (CAPEX), manifesting engineering insights from a service provider's perspective. Subsequently for the EC framework, we formulate a CAPEX optimization problem by jointly optimizing the BS cluster size and solar provisioning on individual BSs. Our results demonstrate that the proposed EC framework alleviates the green energy outage significantly, providing computational efficiency gains and CAPEX savings over the state-of-art approaches. © 2016 IEEE.

关键词： Batteries Green Energy Computational Modeling Power Demand Green Products Computational Efficiency Sustainable Development Green Computing Estimation Energy Exchange Cooperative Energy Transfer Computation Efficiency Energy Sustainability Solar Powered Base Station Traffic Energy Imbalance Network Of Cells Energy Source Optimization Problem Computational Efficiency Renewable Energy Energy Transfer Base Station Energy Harvesting Solar Power Capital Expenditure Three State Model Energy Cooperation Skewness Intermediate State Transition Probabilities Power Grid Average Energy Energy Deficit Probabilistic Analysis Sustainability Performance Net Energy Outage Probability Battery Level Photovoltaic Panels Consecutive Hours Discrete Time Markov Chain Solar Harvesting Outage Performance Depth Of Discharge Upcoming Sections

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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

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Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease Dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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Contactless and Real-time Hand Gesture Recognition using Inductive Proximity Technique for Wrist-worn Wearables

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IEEE Sensors Journal 2025年第11期25卷 20474-20485页

作者： Lin, Pei-Jung Shih, Chi-Huang Weng, Tzu-Hsuan Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung411 Taiwan

This study proposes a contactless and real-time hand gesture recognition system suitable for smartwatches. The proposed system adopts inductive proximity sensing to collect Mechanomyography (MMG) signals induced by finger-based gestures above the wrist. After the signal processing and feature extraction stages, machine-learning models can be built for gesture classification. Compared with the Electromyography (EMG)-based method generally deployed in the forearm, inductive MMG applies an electromagnetic field to the body surface and is suitable for use on the wrist. Compared to the existing contact methods, such as EMG and accelerometer-based MMG, the inductive MMG method does not require contact with the body surface to collect muscle action signals and can avoid interference from environmental factors such as sweat and dust. The main contributions of this study are (1) a watch-type prototype with inductive MMG sensing technique, including hardware and firmware;(2) a lightweight and efficient signal processing mechanism that can capture the inductive signal characteristics of gestures for the above-mentioned embedded system (i.e., watch-type prototype);and (3) the tempo-spatial feature extraction method to improve the accuracy of gesture recognition. The experiment results show that for six common machine learning models, the gesture recognition accuracy of the proposed system is above 95%, with a maximum of 97.47%. In the briefing control application for verification purposes, the average accuracy of the inductive gesture recognition system can achieve 98.26%, and the average processing time is 14.37 ms. According to the experimental results, the inductive MMG system can provide a feasible gesture recognition solution for wrist-worn wearable devices. © 2001-2012 IEEE.

关键词： Firmware

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A feature selection-aided deep learning based deepfake video detection method

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Multimedia Tools and Applications 2025年 1-24页

作者： Mohiuddin, Sk Roy, Ayush Pani, Saptarshi Malakar, Samir Sarkar, Ram Department of Computer Science Asutosh College Kolkata700026 India Department of Electrical Engineering Jadavpur University Kolkata700032 India Department of Computer Science UiT The Arctic University of Norway Tromsø9019 Norway Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

The rapid advancements in deepfake technology are imposing significant challenges in detecting manipulated media contents. In this work, we have introduced a deepfake detection method that utilizes three pre-trained convolutional neural network models, namely DenseNet-121, EfficientNet-B0, and Xception to extract deep learning features from preprocessed extracted video frames. Next, these features are stacked to generate diverse feature representations, and thus end up with high-dimensional features. After that, a feature selection strategy is employed on the combined features to eliminate redundant and less effective features. The selected features are then fed into the K-nearest neighbors classifier to decide whether a questioned video is a fake video or not. Two widely used deepfake video datasets, namely FaceForensics++ and Celeb-DF (V2) are used to evaluate the performance of the proposed technique following two experimental setups: intra- and inter-datasets. The results show that the model achieves area under curve scores of 98.50% (85.70%) and 97.70% (79.00%) in the intra-dataset (inter-dataset) setups while trained on the FaceForensics++ and Celeb-DF (V2) datasets, respectively. The performance of the proposed model is comparable to that of state-of-the-art methods. Source code of the proposed method can be found at: here. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Real-Time Lie-Speech Determination Using Voice-Stress Technology

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第2期5卷 81-93页

作者： Al-Dhaher, Fadi K. Mohammed, Duraid Y. Khalaf, Mohammed Al-Karawi, Khamis A. Sarfraz, Mohammad Al Maathidi, Muhammad Mazin Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq General Directorate of Education Anbar Ramadi31001 Iraq Computer Science Department Al-Maarif University College Ramadi31001 Iraq University of Diyala Iraq Electrical Engineering Department Aligarh Muslim University Aligarh India Computer Science Department University of Bahrain Bahrain

Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such as voice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, a historical and generally established approach, may be enhanced or replaced by these revolutionary real-time techniques. Traditional lie detection procedures, such as polygraph testing, have been challenged for their lack of reliability and validity. Newer techniques, such as brain imaging and machine learning, might offer better outcomes, although they are still in their early phases and require additional testing. This project intends to explore a deception-detection module based on sophisticated speech-stress analysis techniques that might be applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns, to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy of the system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems, by addressing the limitations of old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy, which uses random forest (RF) to select only the significant features for training when a real-life trial dataset consisting of audio files is employed. Next, utilizing the RF as a classifier, an accuracy of 88% is reached through comprehensive evaluation, thereby confirming its reliability and precision for lie-detection in real-time scena

关键词： National security

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Cloud computing security: a taxonomy, threat detection and mitigation techniques

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International Journal of computers and Applications 2024年第5期46卷 348-361页

作者： Rani, Pinki Singh, Sukhdip Singh, Karanbir Computer Science and Engineering Department Deenbandhu Chhotu Ram University of Science and Technology Sonipat India Computer Engineering Department Seth Jai Parkash Polytechnic Yamuna Nagar India

Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. Cloud customers can access these services according to their need and pay only for those services which they utilize. Almost every industrial sector like banking, retail, education, healthcare, manufacturing and businesses is adopting cloud techniques within their enterprise either privately or in sharing with other enterprises. However, many security issues must be resolved because they are a barrier in adopting this fast-growing technology. This paper reviews various threats on cloud computing like distributed denial of service attack, spoofing attacks (e.g. ARP Spoofing, IP Spoofing, DNS Spoofing), man-in-the middle attack, data loss, data breach, reused IP address attack, malicious insiders, insecure interfaces & APIs etc. These security issues prevent users from adopting cloud services. This study presents various detection and prevention methods to address cloud security concerns. The effectiveness of each strategy is then analyzed based on its attributes, benefits and drawbacks. Deep learning and machine learning algorithms produce more precise findings for both known and unidentified attack patterns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： MapReduce

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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An efficient weighted partial MaxSAT encoding for scheduling in overloaded real-time systems

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International Journal of computers and Applications 2024年第3期46卷 175-183页

作者： Pourhashem Kallehbasti, Mohammad Mehdi Pirgazi, Jamshid Ghanbari Sorkhi, Ali Kermani, Ali Department of Electrical and Computer Engineering University of Science and Technology of Mazandaran P.O. Box 48518-78195 Behshahr Iran

Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting in missed deadlines and degraded system performance. Therefore, scheduling algorithms play a critical role in ensuring that high-priority tasks are completed on time while minimizing the impact of incomplete lower-priority tasks. This paper proposes an efficient Weighted Partial MaxSAT(WPMS) encoding that returns an optimal solution in which the total weight of incomplete tasks is minimized in a single machine environment. To assess the efficiency of our proposed formulation, a comparative analysis is conducted alongside the state-of-the-art encoding. By examining the solving 75 distinct problems, it becomes evident that the WPMS encoding proposed herein exhibits a considerable advantage in terms of both time and memory efficiency. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Real time systems

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Multistage Parameter Optimization for Rule Generation for Multistage Manufacturing Processes

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IEEE Transactions on Industrial Informatics 2024年第3期20卷 3857-3867页

作者： Wahyuni, Ida Chang, Chin-Chun Yang, Hua-Sheng Wang, Wei-Jen Liang, Deron National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic accuracy, and accommodate manufacturing tolerances. However, existing studies only focused on optimization in a single manufacturing stage of MMP, such as the weaving stage in fabric manufacturing. Furthermore, existing methods optimize for a single value rather than a range. Thus, we propose a novel approach called multistage parameter optimization for rule generation (MPORG) to prevent the occurrence of defects in MMPs. In the proposed approach, key parameters are identified and optimized to a range for each defect type. Subsequently, the optimized parameters for each defect type are merged. Our approach is novel because it optimizes parameters to a range rather than a single value, allowing engineers to select a value in this range according to their experience. It also provides results that are specific to a product type. Our approach outperformed the classification and regression tree (CART) algorithm and multiresponse CART method in experiments on an empirical fabric manufacturing dataset that we gathered. The experimental results demonstrated that the MPORG approach can prevent the occurrence of single-type or multiple-type defects by approximately 89%. © 2005-2012 IEEE.

关键词： Defects

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