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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Kumar, Nathi Kranthi Venkateswaramma, Jarabhala Nirosha, Chelumala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India St. Martin's Engineering College Dept. of Artificial Intelligence and Machine Learning Hyderabad India Hyderabad India Secunderabad India St. Martin's Engineering College Department of EEE Telangana Secunderabad500 100 India

ISBN: (纸本)9798331505745

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method. © 2025 IEEE.

关键词： Sensitive data

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Integrated Sensing and Communications for Unsourced Random Access: Fundamental Limits

arXiv

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arXiv 2024年

作者： Ahmadi, Mohammad Javad Schaefer, Rafael F. Poor, H. Vincent Chair of Information Theory and Machine Learning Technische Universität Dresden Germany Dept. of Electrical and Computer Engineering Princeton University United States Cluster of Excellence CeTI Germany BMBF Research Hub 6G-life Germany Barkhausen Institut Dresden Germany

This work considers the problem of integrated sensing and communications (ISAC) with a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals, without requiring scheduling with the base station (BS). Hence, the signal received from each user is affected by significant interference from numerous interfering users, making it challenging to extract the transmitted signals. UNISAC aims to decode the transmitted message sequences from communication users while simultaneously detecting active sensing users and estimating their angles of arrival, regardless of the identity of the senders. In this paper, we derive an approximate achievable result for UNISAC and demonstrate its superiority over conventional approaches such as ALOHA, time-division multiple access, treating interference as noise, and multiple signal classification. Through numerical simulations, we validate the effectiveness of UNISAC’s sensing and communication capabilities for a large number of users. © 2024, CC BY.

关键词： Direction of arrival

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The Iterative Dichotomiser 3 Method Improves Soft Skills Training Decisions Using Survey Data

The Iterative Dichotomiser 3 Method Improves Soft Skills Tra...

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machine learning and Applied Network Technologies (ICMLANT), IEEE International Conference on

作者： Subir Gupta Quang-Trung Hoang Bhaswati Roy Subrata Chowdhury Sandip Mukherjee Aditya Kumar Pathak Diep Can Duc Dept. of CSE(AIML) Haldia Institute of Technology West Bengal India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Vietnam Dept. of FMS Dr.B.C.Roy Engineering College Durgapur WB India Dept. of Computer Science and Machine Learning Sreenivasa Inst. of Technology and Management Studies Chittoor Andhra Pradesh India Dept. of IT Haldia Institute of Technology West Bengal India Electric Power University Hanoi Vietnam

ISBN: (数字)9798350389180

ISBN: (纸本)9798350389197

This investigation explores student expectations and perceptions regarding higher education institutions, focusing on aligning professional programs with student career aspirations. The study evaluates how effectively academic institutions understand and meet training needs essential for student success in professional fields. Students were categorized using the Iterative Dichotomiser 3 methodology to forecast their training requirements. Analytical tools such as the confusion matrix and the ROC curve assessed the precision of these predictions. The results show a specificity rate of 74.1 %, a sensitivity of 78.6%, and an overall accuracy of 76.7 % . The combined sensitivity and specificity score is 3.527, and the empirical area under the ROC curve is 0.76, suggesting the model's reasonable efficacy. The findings indicate a significant student demand for enhanced instruction in soft skills, underscoring the need for educational institutions to adapt their curricula to better prepare students for professional challenges.

关键词： Training Surveys Technological innovation Sensitivity Accuracy Focusing machine learning Sensitivity and specificity Iterative methods Decision trees

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Psi-zeta Approach for Assessing Network Efficiency in Log-Normal Fading and Spectrum Sharing Interference Environments

Psi-zeta Approach for Assessing Network Efficiency in Log-No...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Preeti Narooka Shekhar Verma Abhishek Dwivedi Keshav Kaushik Vineeta Singh Priyanka Gaur Artificial Intelligence and Machine Learning School of Computer and Engineering Manipal University Jaipur Rajasthan India Department of Computer Applications School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kanpur India Sharda School of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kanpur India Dept. of Computer Applications Chandigarh Group of Colleges Jhanjheri Mohali India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The paper introduced the Psi-zeta computation of network efficiency under a scenario of log-normal fading and spectrum sharing interferences. This model is aimed to improve different performance metrics such as throughput, Signal-to-Interference-plus-Noise-Ratio (SINR), energy efficiency and finally outage probability also reducing. Simulations demonstrate that when evaluating the overall performance, Psi-zeta is superior to traditional methods by managing interference and resource allocation agilely. The results, especially under high load and heavy interference conditions, exhibit remarkable enhancements in network performance. The strong modifiability and expandability of psi-zeta show that it can be a good candidate for improving network efficiency in future wireless networks like 6G, where parameters like performance, reliability and energy efficiency are the key factors. This work has significant potential to enable the next-generation communication systems.

关键词： Fading channels Wireless networks Interference Probability Throughput Energy efficiency Power system reliability Reliability Next generation networking Signal to noise ratio

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Modified EfficientNetB3 Deep learning Model to Classify Colour Fundus Images of Eye Diseases

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE International Conference on

作者： Riya Sharma Jayesh Gangrade Shweta Gangrade Ashish Mishra Gautam Kumar Vinit Kumar Gunjan Dept of Computer Science & Engineering Manipal University Jaipur Jaipur India Dept. of Artificial Intelligence & Machine Learning Manipal University Jaipur Jaipur India Dept of Information Technology Manipal University Jaipur Jaipur India Dept of Computer Science & Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes.

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Medical Image Classification Using Thermal Images and Diagnosis of Carcinogen

Medical Image Classification Using Thermal Images and Diagno...

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2024 IEEE International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Srinivas, K. Krishnaiah, Nallam Kumar, K. Laxman Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin’s Engineering College Hyderabad India St. Martin’s Engineering College Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Telangana Secunderabad India Deptartment of Computer Science and Design St. Martin’s Engineering College Hyderabad India

ISBN: (纸本)9798331505134

Lung and Colon (L&C) tumors are lethal sicknesses that can foster in a few organs all the while and, in specific circumstances, jeopardize human existence. Even though it is highly unlikely that these two types of cancer will develop simultaneously, delaying diagnosis significantly increases the likelihood of metastasis between the organs that are affected. To really treat specific sorts of malignant growth, histological determination is fundamental. In the past, doctors had to go through a long and difficult process to look at thermal images and figure out if a patient had cancer;however, this procedure may now be completed much more quickly thanks to the new technology options. Histological images of L&C tumors were classified using a hybrid Deep learning (DL) model with an attention mechanism and a multipath network in this study. To zero in on the main attributes and negligence the less significant ones, a consideration component was utilized. In a multipath network, data travels over a number of channels before each channel is converted and the output from all of the branches is combined. The multipath network is similar to grouped convolution when simplified. The five thermal image categories of the LC25000 dataset were utilized. Among these classes were two for colon disease and three for cellular breakdown in the lungs. The proposed model was compared to a number of well-known DL models, such as ResNet-50, VGG-16, and AlexNet. The proposed approach showed the best exhibition concerning exactness (99.2%), particularity (99.12%), responsiveness (99.28%), accuracy (99.12%), and F1 score (99.2%), as indicated by the exploratory discoveries. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Edge Multipath Data Security Offloading Efficiency via Sequential Reinforcement learning

Enhancing Edge Multipath Data Security Offloading Efficiency...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Wenxuan Qiao Yuyang Zhang Ping Dong Xiaojiang Du Chengxiao Yu Hongke Zhang Mohsen Guizani School of Electronic and Information Engineering Beijing Jiaotong University P. R. China Dept. of New Networks Peng Cheng Laboratory Shenzhen Guangdong China Dept. of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Dept. of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

The multipath transmission structure decouples network services from a single transmission carrier, which has great potential for shaping a more secure and efficient 6G network. Existing multipath transmission schemes face challenges such as network heterogeneity, perception lag, and additional scheduling delay, which limits their ability to improve bandwidth aggregation capacity and information security. To address these issues, we propose the Sequential Reinforcement Evolution (SRE) scheme, which utilizes deep reinforcement learning to predict the value of future scheduling actions based on past network states. The SRE scheme regards improving bandwidth aggregation capacity and anti-eavesdropping ability as optimization goals, and designs a semi-symmetric attention recurrent neural network (SARNN) to better mine the sequential nature of the scheduling process. The SRE scheme utilizes approximately 500 million real network data points to pre-train the SARNN model, and performs cycle optimization during the actual deployment process. Experimental results show that SRE significantly outperforms state-of-the-art scheduling schemes with a 32% increase in bandwidth aggregation and a 117% increase in traffic security dispersion with minimal impact on latency.

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Exploring Students' Attitudes Towards Robotics Technology in STEM Education: A machine learning-Based Prediction Analysis

Exploring Students' Attitudes Towards Robotics Technology in...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Mahmoud I. Abd El Aziz Ahmed M. Elshewey Zahraa Tarek El-Sayed M. El-kenawy Ahmed M. Ghazy Roheet Bhatnagar Mahmoud Y. Shams Department of Curriculum Instruction and Educational Technology Faculty of Education Kafrelsheikh University Kafrelsheikh Egypt Computer Science Department Faculty of Computers and Information Suez University Suez Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt Dept. of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheiku University Kafrelsheikh University Egypt

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

The aim of this paper is to predict the extent to which students will accept robotics technology in future teaching plans. The study involves students engaging in a two-week project-based learning program on robotics technology, after which they will decide whether it is suitable for them. To achieve this, the paper uses several prediction algorithms based on machine learning (ML), including Linear Regression (LR), Ridge Regression (RR), Elastic Net (EN) regression, and K-Nearest Neighbor (KNN) models. The sample group consists of 35 students from STEM high schools in Egypt. The research findings indicate that the students have gained confidence and satisfaction in robotics technology and believe it will have significant value in the future. The prediction results obtained from the ML regression models support these findings. The dataset used in the study includes seven input features and one target output representing the total prediction values of accepting and rejecting robotics technology in STEM schools. The regression models LR, RR, EN, and KNN achieve determination coefficients (R2) of 99.99%, 98.80%,99.20%, and 85.60%, respectively.

关键词： machine learning algorithms Computational modeling Linear regression Education machine learning Nearest neighbor methods Predictive models Prediction algorithms Robots STEM

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On the Solvability of Resource Allocation Problems for Wireless Systems on Digital computers

On the Solvability of Resource Allocation Problems for Wirel...

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IEEE International Conference on Communications (ICC)

作者： Holger Boche Andrea Grigorescu Rafael F. Schaefer H. Vincent Poor Chair of Theoretical Information Technology Technical University of Munich BMBF Research Hub 6G-life Munich Quantum Valley (MQV) Chair of Information Theory and Machine Learning Technische Universität Dresden Cluster of Excellence CeTI Dept. of Electrical and Computer Engineering Princeton University

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper examines the computability of optimal power allocation strategies for utility maximization and maxmin fairness. It is demonstrated that a computable constraint power function exists. However, when both total and individual power constraints are taken into account, it is determined that the optimal power allocation for maximizing network utility is not computable since every single power value is a non-computable number. Furthermore, it is established that within the same constraint context, both the max-min fairness level and its corresponding power values are non-computable numbers.

关键词： Wireless communication Digital computers Noise Interference Minimax techniques Approximation algorithms Vectors

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A New Intrinsic Complexity Metric for Air Traffic De-Conflicting

A New Intrinsic Complexity Metric for Air Traffic De-Conflic...

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Conference on Digital Avionics Systems (DASC)

作者： Patrice Zombré Daniel Delahaye Jules-Raymond Tapamo Oludolapo A. Olanweraju Felix Mora-Camino Air Navigation Directorate ASECNA Dakar Senegal Optimization and Machine Learning Lab. Ecole Nationale de l’ Aviation Civile-ENAC Toulouse France Computer Eng. Dept. University of KwaZulu-Natal -UKZN Durban South Africa Industrial Eng. Dept. Durban University of Technology-DUT Durban South Africa Industrial Eng. Dept. Universidade Federal Fluminense-UFF Associate DUT and ENAC Niteroi Brazil

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

This study in in the field of Air Traffic Flow Management and considers mainly the pre-tactical planning level of air traffic flows where their global balance is already fixed (flows structures and levels, control sectors and capacity) and where flights are treated on an individual basis in the space between the different departure/arrival airports. Then, for each flight, decisions such as departure rescheduling and/or rerouting may be taken by the Air Traffic Flow Management to cope with predicted traffic congestion situations resulting in conflicts. The adopted objective is the global reduction of interactions between flights while maintaining as much as possible the original efficiency of flight plans computed by airlines. A measure of the criticality of the separation between two aircrafts is introduced as a basis to construct new air traffic complexity measures, either local or global, either instantaneous or integrated over periods of time. It is shown in this study how the resulting metric can be used to perform through standard maneuvers the de-confliction of enroute air traffic over a given area and during a given time window. Two approaches are considered, one which is global and leads to the resolution of a large nonlinear optimization problem, and another one which is decentralized and makes use of directional components of the aircraft centric and flight centric complexity indexes. Then, in the case of flight centric traffic control, using the new flight centric complexity measure, the assignment of flights to the different controllers in charge of the monitoring of an extended area is formulated as an optimal partitioning problem where interactions between controllers must be minimized. To cope quickly with this combinatorial problem, a heuristic approach is developed.

关键词： Aerospace electronics Traffic control Time measurement Complexity theory Planning Air traffic control Aircraft Traffic congestion Standards Optimization

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