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Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

computers, Materials & Continua 2023年第2期74卷 2981-2998页

作者： Guangjun Liang Jianfang Xin Linging Xia Xueli Ni Yi Cao Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina National and Local Joint Engineering Laboratory of Radio Frequency Integration and Microassembly Technology Nanjing University of Posts and TelecommunicationsNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center NanjingChina Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous *** are two priority transmission types of data in wireless communication,such as video or telephone,which always meet the requirements of high priority(HP)data transmission *** there is a large amount of low priority(LP)data,there will be a large amount of LP data that cannot be *** situation will cause excessive delay of LP data and packet dropping *** order to solve this problem,the data transmission process of high priority queue and low priority queue is *** the priority jump strategy to the priority queuing model,the queuing process with two priority data is modeled as a two-dimensionalMarkov chain.A state dependent priority jump queuing strategy is proposed,which can improve the discarding performance of low priority *** quasi birth and death process method(QBD)and fixed point iterationmethod are used to solve the causality,and the steady-state probability distribution is further ***,performance parameters such as average queue length,average throughput,average delay and packet dropping probability for both high and low priority data can be *** simulation results verify the correctness of the theoretical ***,the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

关键词： Stochastic geometry queuing theory D2D heterogeneous networks quasi-birth and death process

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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A Structured Analysis on IPL 2022 matches by approaching various data Visualization and Analytics

A Structured Analysis on IPL 2022 matches by approaching var...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Selva Birunda, S. Nagaraj, P. Jency A Jebamani, B. Revathi, B. Muneeswaran, V. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

The main purpose of making this report is to give the information to the franchise to make their decision on buying the players by bidding in the auction based on their performances. There are many models of reports which describe the IPL seasons. This report's purpose is to give a brief analysis of the IPL 202215th league. In this report, much of the data from the datasets of matches is utilized and analysis is made by using Python packages specifically, Pandas, NumPy, and matplotlib. By making graphs and tables a brief analysis with complete knowledge of an IPL season is given. This analysis describes every good performance and gives good chance to the best players. © 2023 IEEE.

关键词： data visualization

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Decoding the Brain Neuro-Inspired Deep Learning Architectures of Cognitive Computing

Decoding the Brain Neuro-Inspired Deep Learning Architecture...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Latha, J. Murugan, S. Karthick Jayachandran, S. Maheshwari, G. Naveen, G. Navaneethan, S. R. M. K. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India R. M. D. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tami Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359688

In response to the growing need for advanced artificial intelligence (AI) systems, the study thoroughly examines neuro-inspired deep learning architectures within the domain of cognitive computing. The need for such innovation originates from the constraints of traditional systems, characterized by their inefficiency in duplicating complex cognitive activities. Traditional techniques often need help with complex tasks and more biological neural network flexibility. The proposed system uses neuroscience principles to solve these limitations and create advanced models such as spiking neural networks and hierarchical attention processes, increasing machine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably improves the system's performance across several dimensions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI development. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive computing. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimized Machine Learning for Classifying Colorectal Tissues

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SN computer Science 2023年第5期4卷 461页

作者： Tripathi, Ashish Misra, Anuradha Kumar, Kuldeep Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Center for Data Analytics Bond Business School Bond University Gold Coast QLD Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Due to numerous deaths, colon cancer treatment and diagnosis are viewed as societal and financial challenges. The most severe reason for death worldwide is colorectal cancer. The classification of colon cancer tissues through images is presented in this paper as a multifaceted task. Classifying an illness at a premature stage increases its chances of existence, as late detection can be mortal which results in metastasis and a poor prognosis. The microscopic examination and classification of infected colon tissue sample images is a complex task. Also, the failure to manually detect the abnormality in the tissue by a pathologist might increase the severity of the disease. With the aid of intelligent machines, and automated diagnosis the classification of tissues from images can be done in much less time. These algorithms can learn by analyzing the patterns in the images and support the pathologist in completing the task with greater accuracy. In this research article, we proposed a tuned machine learning model, with the application of five machine learning techniques (K-Nearest Neighbor, Decision Trees, Random Forest, Categorical Boosting, and Gaussian Naive Bayes) for accurately classifying histopathological colon cancer tissues images of National Center for Tumor diseases Bank. The results demonstrate that the Categorical Boosting model has the best performance and is the most viable approach (accuracy: 0.9067, F1-Score: 0.9053, specificity: 0.9739, and sensitivity: 0.9757). © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Colorectal tissue classification Differential-Box-Count Histopathological image classification Machine Learning

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Cost Optimization Approaches for Task and Workflow Scheduling in the Cloud Environment 15th

Cost Optimization Approaches for Task and Workflow Schedulin...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Mahendar, A. Senthamarai Kannan, S. Anantha Sivaprakasam, S. Santhosh Kumar, P.N. Pradeep Ghantasala, G.S. Kallam, Suresh CMR Technical Campus Telangana Hyderabad India Department of AI and Data Science SRM TRP Engineering College Irungalur Tamilnadu Thiruchirapalli India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Tamilnadu Chennai India CMR Technical Campus Hyderabad Telangana Hyderabad India Department of Computer Science and Engineering Alliance University Bengaluru India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India

ISBN: (纸本)9783031810855

The use of information technology by companies and individuals in cloud computing revolutionizes. Cloud computing offers virtualized tools over the Internet for dynamic services. This ensures that end-users with resources-virtualization maintained and controlled can create, deploy and manage applications "on the cloud." The preparation of tasks in cloud computing systems is one of the most challenging issues in fulfilling user-defined service quality while minimizing the cost of executing tasks. Several cost optimization approaches have been proposed to improve the economic aspect of task scheduling in cloud computing. A full analysis of approaches to promoting cost optimization in connection with task planning in cloud computing has not yet been published in the literature. This paper is intended to examine the problems of cost optimization in task scheduling through a detailed analysis of current task scheduling methods in cloud computing and to identify aspects and parameters of cost optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost benefit analysis

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A Cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR Technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： Cognitive radio

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3D CNN on Nodule data 2

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Carmel Mary Belinda, M.J. Kannan, E. Alex David, S. Kannan, Sriram Ravi Kumar, S. Vathani, Anstey Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Fidelity Investments Data Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India

ISBN: (纸本)9798331534950

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated. © 2024 IEEE.

关键词： Lung cancer

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IoT-Based Behavioral Analysis Model for Smart Learning

IoT-Based Behavioral Analysis Model for Smart Learning

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sathia Priya, M. Shamna Parveen, S. Saravanan, S. Kalaivani, R. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350313987

The role of education is of paramount importance in influencing the cognitive development of future generations. This paper presents a novel IoT-based Behavioural Analysis (IoT -BA) Model for Smart Learning integrated with neural network technology. The Internet of Things (IoT) enables educational institutions to perform management tasks more quickly and risk- freely by analyzing organized data and acquiring insights from it to inform decisions. The significance of teaching college students in the self-directed application of effective learning strategies has evolved with various trending digital twin frameworks. The proposed IoT -BA model can collect the motion features and facial expressions of the students from the classroom video while attending classes through self-learning applications. The collected images can then be preprocessed using augmentation and a local binary pattern approach. This deep-preprocessed data has been passed to the deep learning algorithm convolutional neural network (CNN) that is trained with the preprocessed data for classification. Spatiotemporal feature extraction and selection from the preprocessed data can be performed using the preprocessing algorithm principal component analysis (PCA). The student's attention level classification for the IoT -BA uses the CNN algorithm. This experimental research uses the real-time data collected by setting the classroom environment for model training and validation. The CNN model has been trained with the data collected from the environment using the simulation platform TensorFlow and performs five-cross-validation with the realistic data. The IoT -BA model can predict the student's attention level in the classroom based on the collected features from the classroom video. The simulation results of the CNN -based IoT -BA model showed better performance in terms of 96.42% classification accuracy and a minimal error rate of 1.82%. © 2023 IEEE.

关键词： Behavioral Analysis Classroom CNN Smart Learning Spatiotemporal Features Student Attention

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