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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Sabahath, Asfia Begum, Arshiya Reddy, Pundru Chandra Shaker Radha, Marepalli Pawar, Jay Mithra, C. King Khalid University College of Computer Science Department of Computer Science Abha Saudi Arabia Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Cvr College of Engineering Mangalpalli Telangana Hyderabad India Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

Facebook, Twitter, and other social media sites have many positive uses, but they also have many negative aspects. Cyberbullying (CB) is a problem on various social media sites. Since each individual's reaction to cyberbullying is unique, the damage it does to victims' lives is difficult to put a price on. While some may view the message as natural, victims may perceive it as bullying. Finding the bully material in cyberbullying communications is quite difficult due of their ambiguity. There has been some study on using textual posts to solve this problem. Cyberbullying detection using images, on the other hand, has gotten comparatively less focus. The overarching goal of this study is to provide a framework for addressing the problem of cyberbullying on social media sites that makes use of images. Developing models is the initial use case for the convolutional-neural-network(CNN) that is based on deep-learning (DL). In the subsequent sections, this study employs transfer-learning(TL) models. The experimental results of varying the hyper-parameters verified that the model based on TL is the superior option for this issue. With a bestcase accuracy of 95.6%, the suggested algorithm successfully identifies the vast majority of cyberbullying posts. Combining DL with TL techniques yielded better results than applying either method alone, according to experiments conducted on an image dataset. © 2024 IEEE.

关键词： Tweets

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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UWB Radar Performance Analysis Based on OFDM Pilots

UWB Radar Performance Analysis Based on OFDM Pilots

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2023 IEEE International Conference on Control, Electronics and computer technology, ICCECT 2023

作者： Liu, Jing Tian, Jin Zhang, Shuaishuai Jinling Institute of Technology Engineering School of Networks and Telecommunications Nanjing China Anhui University of Science and Technology College of Computer Science and Engineering Huainan China

ISBN: (纸本)9798350399028

The technology of radar - communications integration can obtain situational awareness and unlimited ability to communicate simultaneously without increasing bandwidth. A single wireless hardware platform with a single signal provides both functions of radar and communication i.e. so as to integrate radar and communication systems into a system. Additionally, the hardware platform is very promising since the high performance is provided in radar and communication. Therefore, integrate radar and communication is an emerging and feasible trend. The paper focuses on UWB radar function applications, and system researching the radar performance based on MB-UWB. MB-UWB signal bandwidth is 528 MHz, which includes 12 pilot subcarriers. This paper analyzes radar detection performance of MB-UWB with 12 subcarriers on maximum unambiguous range and velocity, false alarm rate, misdetection rate and loss power induced by pilot. The numerical analysis shows that the MB-UWB specification is suitable for the corresponding radar application as the impulse UWB. This research lays the foundation for the research of radar technology that combines UWB technology and OFDM technology, and provides a new idea for Integrate Radar and Communication with integrated waveform. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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An Articulate Heart Attack Detection System Using Mine Blast Optimization (MBO) Based Multilayer Perceptron Neural Network (MLPNN) Model

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第2期4卷 143-155页

作者： Rajesh Pandian, N. Shanthi, D. Selvaganesh, N. Karnataka Bengaluru560069 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Kothandaraman Nagar Tamil Nadu Dindigul624622 India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Sriperumbudur602117 India

The creation of an automated system for heart disease detection was once one of the more common undertakings in the healthcare industries. For this purpose, the different types of big data analytics technologies are developed in the conventional works to predict the heart disease. Still, it limits with the problems associated to the elements of high complexity, time consumption, over fitting, and mis-prediction results. Because the previous methods did not optimize the best features, they did not give accurate results in heart attack detection, so the system is needed to control the death ***, the proposed work objects to implement a novel Mine Blast Optimization (MBO) based Multi-Layer Perceptron Neural Network (MLPNN) technique to predict the heart attack from the given datasets. The proposed detection framework includes the stages of preprocessing, feature optimization, and classification. Here, the regression based preprocessing model is implemented to normalize the attributes for increasing the quality. Then, the MBO technique is also used to choose the relevant features based on the best optimal solution. It also helps to reduce the increase the training of classifier with reduced time consumption and high detection accuracy. Finally, the MLPNN technique is utilized to predict the classified label as whether normal or disease affected. During analysis, the results of the proposed MBO-MLPNN technique is validated and compared by using various measures. Here the proposed method achieved 98% accuracy performance for heart attack detection than former methods. © 2023 Novel Carbon Resource sciences. All rights reserved.

关键词： Multilayer neural networks

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Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

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IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

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Blockchain-Enabled Defence Strategy for Attacks Prevention and Detection in IoT Ecosystem 4

Blockchain-Enabled Defence Strategy for Attacks Prevention a...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Maurya, Vinay Rishiwal, Vinay Jat, Dharm Singh Yadav, Preeti Yadav, Mano Jain, Arpit Chaurasia, Brijesh Kumar MJPRohilkhand University Department of CSIT MJPRohilkhand University Bareilly India Namibia University of Science and Technology Department of Computer Science Namibia Bareilly College Bareilly Department of Computer Science Bareilly India PSIT Department of CSE Kanpur India

ISBN: (纸本)9798350353266

The Internet of Things networks (IoT-N) enable devices to communicate via diverse methods and protocols. However, they also present significant security hazards. Yet, they also pose substantial security risks. Addressing these risks is quite challenging as traditional security measures, like software and firewalls, often need to catch up to attain the specific requirements of IoT devices. The proposed work presents a hybrid intrusion detection system (HIDS) incorporating blockchain and machine learning technologies to register IoT devices, detect and prevent attacks, and verify nodes through smart contracts. The proposed approach enables rapid and efficient detection of malicious nodes to enhance security measures. To evaluate the performance of the BC-based HIDS in safeguarding IoT networks against threats, we considered the performance metrics, viz. threshold, packet, and regular attacks. The results demonstrate the proposed HIDS's efficacy and outstrips with traditional intrusion detection approaches that offer a more robust security framework. Furthermore, BC plays a vital role in storing corrupted data in IoT networks and enhancing security. The performance of the proposed HIDS signifies that uniting ML and BC is promising to protect IoT networks from malicious security threats. © 2024 IEEE.

关键词： Network intrusion

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An effective algorithm for predicting load and dynamic task scheduling in cloud fog architecture for smart homes

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International Journal of Cloud Computing 2025年第1期14卷 54-85页

作者： Agrawal, Krishna Kant Kumar, Sujeet Seth, Jitendra Kumar Gupta, Abhishek Kumar Lamba, Sonia School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India GL Bajaj Institute of Management Uttar Pradesh Greater Noida India Department of Information Technology KIET Group of Institutions Uttar Pradesh Ghaziabad India Department of Information Technology Jagannath International Management School New Delhi India Department of Computer Science ABES Engineering College Uttar Pradesh Ghaziabad India

The need for smart homes with many devices and services continues to rise quickly. With this surge, smart homes need task scheduling and load-prediction algorithms to provide the proper services for the residents. A deep learning-based dynamic job scheduling and load prediction technique for cloud-fog smart homes is proposed in this paper. This algorithm forecasts task arrival rates at each fog node and assigns them to available fog nodes. It dynamically schedules tasks based on fog node workload. Another option is to send non-real-time jobs to the cloud and real-time tasks to the fog layer. This optimises load distribution for performance. Using these task assignee models and features, the program optimises prioritised tasks, scores, network latency, and device resource characteristics. We simulate the algorithm’s performance in various workloads in this part. The proposed algorithms achieved in higher percentile for 93.79% latency, 95.00% throughput, 95.34% response time, 96.28% scalability, 94.20% fault-tolerance, 97.41% scheduling capacity, 91.41% load balancing capacity, and 95.22% priority management. The results indicate that such an algorithm significantly surpasses the conventional task scheduling methods in load balancing and shortens the average task response time. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Scheduling algorithms

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A HYBRIDIZED MODEL USING INDEPENDENT COMPONENT ANALYSIS WITH AN IMPROVED GENETIC ALGORITHM FOR CLASSIFYING BLURRED TEXT DETECTION IN WILD SCENE

A HYBRIDIZED MODEL USING INDEPENDENT COMPONENT ANALYSIS WITH...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Nwufoh, Chinonyelum Sakpere, Wilson Olufemi, Tolulope Kolapo, Ridwan Federal College of Animal Health and Production Technology Computer Science Department Oyo State Ibadan Nigeria Lead City University Computer Science Department Oyo State Ibadan Nigeria West Midland University Lagos Computer Science Depart Lagos Nigeria Nile University of Nigeria Information Technology Department Abuja FCT Nigeria

ISBN: (纸本)9798350376838

Removing text from images is a challenging task, particularly when dealing with fuzzy borders, shading, and embossing. This process is essential but can be difficult to achieve using existing solutions, which may not be adequate to address the multifaceted issues that occur in the wild. The history of writing spans several centuries and having accurate and rich semantic information in text is crucial in various vision-based use cases. As a result, researchers in the fields of computer vision and pattern recognition have been actively investigating text recognition in natural settings. Deep learning has gained popularity in recent years, resulting in the development of unique, useful, and successful approaches. In this study, we propose a robust approach for detecting blurred text in wild scenes using a hybridized model. Our model integrates independent component analysis (ICA) with an improved genetic algorithm (IGA) to enhance feature extraction and selection, respectively, resulting in improved classification accuracy. We compare our approach with existing state-of-the-art methods using a publicly available dataset and demonstrate that our hybridized model outperforms them, achieving a significantly higher accuracy of around 98.65%. We employ support vector machine (SVM) and k-nearest neighbor (KNN) classifiers to classify the extracted features. The results show that both classifiers perform well, with the KNN classifier achieving slightly higher accuracy than the SVM classifier. This approach can be extended to other image classification tasks in various domains, providing a powerful tool for real-world applications. The findings of this study have significant implications for the development of more accurate and efficient methods for detecting blurred text in challenging environments, paving the way for the use of semantic information in various vision-based applications. © 2024 IEEE.

关键词： Semantics

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Dual-band and high-isolation shared-aperture antenna for broadband airborne applications

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Journal of Electronic science and technology 2024年第4期22卷 9-18页

作者： Yuan Zhang Dong-Jun Wang Chuan Wu Qiang-Ming Cai Run-Ren Zhang Rong-Qiang Li Peng Gao Yan-Wen Zhao School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu610091China Electronic Engineering College Chengdu University of Information TechnologyChengdu610225China School of Information Engineering Southwest University of Science and TechnologyMianyang621010China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA

A dual-band and high-isolation shared-aperture antenna for unmanned aerial vehicle(UAV)platforms has been *** shared-aperture antenna consists of a rectangular monopole antenna and a 4-element multiple input multiple output(MIMO)*** order to increase the isolation,several double split ring metamaterial(MTM)structures are introduced between antenna *** antenna radiator and the MTM structure are located on the front and back of the same dielectric substrate,respectively,and are perpendicular to a circular metal *** overall size of the antenna substrate is 124 mm×38 mm×1.016 ***,the antenna prototype is constructed and measured,and the simulated and measured results are in good *** measured results show that the-10 dB bandwidth of the monopole antenna is 1.92 GHz to 2.75 GHz,and the common-6.0 dB bandwidth of the MIMO antenna element is 4.75 GHz to 4.91 GHz,covering 2.2 GHz to 2.4 GHz in the S-band and 4.8 GHz to 4.9 GHz in the 5G band,*** the 5G band,the isolation between any element of the MIMO antenna and the S-band monopole antenna is not less than 21 dB,and the isolation between the MIMO antenna elements is better than 23 dB,indicating t-hat the isolation between the antenna elements is *** proposed antenna is suitable for the application on UAV airborne platforms.

关键词： Dual-band antenna High-isolation Monopole antenna Multiple input multiple output(MIMO) Shared-aperture Unmanned aerial vehicle(UAV)

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