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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kalai Kannan, P. Vidhya, S. Kg College of Arts and Science Department of M.Sc. Software Systems & Computer Science Coimbatore India Kg College of Arts and Science Department of Information Technology Coimbatore India

ISBN: (纸本)9798350384277

Wireless sensor network has shown a tremendous multifold growth in terms of applications in multiple domains. These domains are with quite continuous execution in nature. The survival time of the sensor-node's (SN) exist in WSN acts a crucial role in making the monitoring or sensing of the entire applications. In recent days the survival-time of the mote (SN) in WSN seems to be the hot research topic for most of the researchers, so in order to increase the survival time of WSN, the selection of cluster-head (CH) is essential one corresponding to clustering techniques. The cluster model of the WSN helps for grouping the sensor nodes into various sub groups. These subgroups are packed with either fixed or random number of sensor nodes. Though the architecture pattern of these clusters differed based on the implementations, the filtering and selection of cluster head (CH) is a challenging task that plays as an intermediate effective node to receive the data's & transmitting it to the base station (BS). The Preference of CH takes place with the nature or ability of sensor nodes with all aspects respect to reception and transmission of data effectively from the entire clusters. Based on these preference nature particular sensor node will be nominated as CH. This paper proposes a Preference based nomination of CH that would be compared with the existing algorithm and proves better in performance in terms of network lifetime. © 2024 IEEE.

关键词： Sensor nodes

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Convolutional Neural Network Based Thermal Image Classification 2

Convolutional Neural Network Based Thermal Image Classificat...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Ashfaq, Qirat Usman Akram, Muhammad National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Classification of Thermal Images has been extensively used for its significant applications in many fields. There are many problems with the visible spectrum like object shadows, clothes or the body of a human being matches the background and different lighting conditions. These limitations are overcome by using thermal imaging. Each and every object emits heat (Infrared energy) according to its temperature. Normally the hotter object emits more radiation than the colder one. As all objects have a mostly different temperature so thermal camera detects them and these objects get appear as distinct objects. In the start, thermal imaging was used by the military for detection, recognition, and identification of enemy personnel and equipment. Nowadays it is extensively used in the detection of face, self-driving car, detection of pedestrians and it also has application in the field of environmental work that is monitoring for energy conservation and pollution control. This research presents a novel study for the classification of thermal images using convolutional neural networks (CNN). The research focused on developing a framework that detects multiple thermal objects using CNN. Developed a framework based on deep learning Inception v3 model;work with thermal images that are captured by Seek Thermal and FLIR. For training and testing of the model two datasets are used that include three classes' cat, car, and man. For the FLIR dataset the highest accuracy achieved is 98.91% and for Seek thermal dataset highest accuracy achieved is 100%. A comparison of the proposed framework with some other CNN models (DenseNet, MobileNet, and YOLOv4), with a customized CNN model and with a conventional model is also presented. The results of the proposed framework and comparison with other models prove that the proposed framework is effective for the classification of thermal images. © 2022 IEEE.

关键词： Convolutional neural networks

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Adaptive Climbing Strategy of Snake Robot Based on Compliance Control 4

Adaptive Climbing Strategy of Snake Robot Based on Complianc...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Yang, Huizhuo Dong, Jiyuan Wang, Meng Meng, Shiyong Yang, Lei Xue, Jiashuo Tiangong University School of Electronic and Information Engineering Tianjin China Tiangong University Department of Computer Science and Technology Tianjin China Tiangong University Department of Software Engineering Tianjin China

ISBN: (纸本)9798350364798

Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregular, which may hinder the snake robots from achieving optimal surface conformation. Additionally, the presence of numerous branching structures in pipes introduces unpredictability in pipe climbing. To address these challenges, we propose an adaptive climbing strategy for snake robots based on compliant control. By adjusting a pre-set elasticity coefficient, the snake robot modulates the output torque of each joint, thereby altering the target angle of the joint. This adjustment enables the snake robot to adapt to unstructured environments. We conducted an experiment on climbing curved pipes to verify the effectiveness of this method. The result demonstrate that under unknown conditions, the robustness of the snake robot's climbing motion is enhanced by modifying the pre-set elasticity coefficient based on compliant control. This finding underscores the potential of compliant control in improving the adaptability and performance of Snake robots in complex environments. © 2024 IEEE.

关键词： Microrobots

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A Brief Overview of Deep Learning Approaches for IoT Security 4

A Brief Overview of Deep Learning Approaches for IoT Securit...

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4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Hassaan Khalid, Muhammad Sharif, Hanan Rehman, Faisal Naeem Ullah, Muhammad Shaukat, Shahbaz Maqsood, Hadia Nouman Ali, Chaudhry Hussain, Ayaz Iftikhar, Irfana Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan Mehran University of Engineering and Technology Szab Department of Software Engineering Khairpur Mir's Pakistan

ISBN: (纸本)9798350335316

The continuous increasing usage of internet devices in many areas of human life is continuously growing and demanding a proper method to protect these IoT devices from cyber-attacks and vulnerabilities. In this aspect, deep learning neural networks are a very helpful and beneficial method to provide protection to internet devices in many aspects. This study's objective is to systematically research and examine the background of research about neural networks tactics functional to different internet-based devices' safety use-cases. We put the influences we looked at into many different groups to show where this research is lacking. This particular research or findings mainly captures the articles related to 'Deep neural networks', 'Cyber risks' and 'IOT' in four databanks, namely Science Direct, ACM Digital Library, IEEEXplore and Springer Link. These studies concern 3 main studies, which involve safety aspects, their own Deep Learning network designs, and the occupied datasets. The final round is to find out the loopholes to be examined in the future and vulnerabilities and drawbacks in the IoT security scenario. © 2023 IEEE.

关键词： Deep neural networks

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Revolutionizing Image Recognition: Next-Generation CNN Architectures for Handwritten Digits and Objects 8

Revolutionizing Image Recognition: Next-Generation CNN Archi...

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8th IEEE Symposium on Wireless technology and Applications, ISWTA 2024

作者： Absur, Md Nurul Nasif, Kazi Fahim Ahmad Saha, Sourya Nova, Sifat Nawrin Department of Computer Science City University of New York New York United States College of Computing and Software Engineering Kennesaw State University GA United States Department of Computer Science Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9798350351354

This study addresses the pressing need for computer systems to interpret digital media images with a level of sophistication comparable to human visual perception. By leveraging Convolutional Neural Networks (CNNs), we introduce two innovative architectures tailored to distinct datasets: the MNIST handwritten digit dataset and the Fashion MNIST dataset. Unlike traditional machine learning methods such as Support Vector Machines (SVM) and Random Forests, our customized CNN models remarkably enhance image attribute comprehension and recognition accuracy. Specifically, the model developed for the MNIST dataset achieved an unprecedented accuracy of 98.71% without any bias, while the Fashion MNIST model reached 91.39%, marking significant advancements over conventional algorithms without any bias. This research showcases the superior efficiency of CNNs in processing and understanding digital images. It underscores the potential of deep learning technologies in bridging the gap between computational systems and human-like visual recognition. Through meticulous experimentation and analysis, we illustrate how deep CNNs require less preparatory work than other image-processing algorithms, setting a new benchmark in computer vision. © 2024 IEEE.

关键词： Support vector machines

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Surface EMG Signal Analysis using Hand-Crafted Features for Detection and Classification of GTC seizures 2

Surface EMG Signal Analysis using Hand-Crafted Features for ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Naveed, Maryam Gul Khawaja, Sajid Usman Akram, Muhammad National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Epileptic seizures with the risk of sudden unexpected death in epilepsy affect the quality of life. Nearly, one-fourth of the individuals suffer from seizures that cannot be treated with medications. Due to the high-level possibility of injuries and complications, generalized tonic-clonic seizures have a considerable contribution to unexpected death. These generalized tonic-clonic seizures activity need to be detected and identified through brain and muscle activity, heart rates, and EMG signals. In this paper, we propose a framework for distinguishing normal activity from seizure activity along-with its categorization. Proposed framework focuses on extraction of multiple sEMG hand-crafted features with the time and frequency domain analysis. The proposed methodology for sEMG signals and for GTC class detection has been tested using multiple classifiers including KNN, SVM and ensembles. The obtained results have shown 10% improvement in classification over the state-of the-art approaches available in literature. © 2022 IEEE.

关键词： Feature extraction

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

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computers, Materials & Continua 2023年第2期74卷 3881-3898页

作者： Asma Sajjad Humaira Ashraf NZ Jhanjhi Mamoona Humayun Mehedi Masud Mohammed A.AlZain Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan School of Computer Science SCSTaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubangJaya47500SelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityKSA Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The most valuable resource on the planet is no longer oil,but *** transmission of this data securely over the internet is another challenge that comes with its ever-increasing *** order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic *** objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video *** the previous approach,DNA was used only for frame *** this DNA is compromised,then our frames with the hidden and unencrypted data will be *** the frame selected in this way were random frames,and no consideration was made to the contents of *** data in this way introduces visible artifacts in *** the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex *** chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random *** results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual ***,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved *** proposed methodology has been implemented in Matlab.

关键词： Video steganography data encryption DNA embedding frame selection

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Comparative Performance Evaluation of Random Forest, Extreme Gradient Boosting and Linear Regression Algorithms Using Nigeria’s Gross Domestic Products 6th

Comparative Performance Evaluation of Random Forest, Extreme...

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6th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2023

作者： Adewale, M.D. Ebem, D.U. Awodele, O. Azeta, A. Aggrey, E.M. Okechalu, E.A. Olayanju, K.A. Owolabi, A.F. Oju, J. Ubadike, O.C. Otu, G.A. Muhammed, U.I. Oluyide, O.P. African Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Computer Science University of Nigeria Nsukka Nigeria Department of Computer Science Babcock University Ogun Ilishan-Remo Nigeria Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia

ISBN: (纸本)9783031518485

Statistical methods like linear regression analysis are frequently used to create predictive analytic models. However, these methods have limitations that may affect the accuracy of the models. Using a typical dataset, this study seeks to accomplish two main goals. First, we fitted three predictive models, including linear regression analysis and two ensemble machine learning algorithms: Random Forest Regressor and Extreme Gradient Boosting Regressor. Secondly, we compared the performance of the models using a 5-fold cross-validation technique. The Random Forest Regressor outperformed the other models, with a Mean Absolute Error (MAE) of 10.138, Mean Square Error (MSE) of 139.729, Mean Absolute Percentage Error (MAPE) of 0.071, Root Mean Square Error (RMSE) of 11.821, and Normalised Mean Square Error (NMSE) of 13.782. These results suggest that the Random Forest Regressor is optimal for developing predictive models with similar datasets. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Machine learning

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