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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Reddy, dasari Anantha Shambharkar, Saroj Chaudhari, dipti Jyothsna, Kandi Somkunwar, Rachna K. Srinish Reddy, Amireddy Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India K.I.T.S. Ramtek Department of Information Technology Nagpur India Pimpri Chichwad College of Engineering and Research Department of Computer Engineering Pune Ravet India Mlrit Department of Computer Science and Engineering Hyderabad Dundigal India Dr. D. Y. Patil Institute of Technology Department of Computer Engineering Pune Pimpri India Cmrit Department of Computer Science and Engineering Hyderabad Telangana Kandlakoya India

ISBN: (纸本)9798350344387

Skin diseases such as psoriasis and vitiligo can greatly impact a patient's quality of life. For correct diagnosis and therapy planning, it is crucial to accurately segment and classify various skin disorders. Existing segmentation & classification models for identification of skin diseases either have low efficiency or have higher complexity when used for clinical scenarios. In order to address these problems, we provide a deep learning model in this study that can automatically separate and categorize vitiligo and psoriasis skin lesions from clinical photos. The suggested model is built on a Convolutional Neural Network (CNN) architecture that has been specifically designed for use in medical image processing applications. Initially, all the collected images are augmented via scale, rotate, width shift, height shift, shear and zoom operations. In proposed approach, train the model on this augmented dataset of clinical images for psoriasis and vitiligo lesions via Transfer learning based ResNet50 CNN, and evaluate its performance using various metrics such as accuracy, precision, recall, and F1-scores. Proposed approach results show that the proposed model achieves high accuracy in both segmentation and classification tasks. The proposed deep learning model can assist dermatologists and medical professionals in accurately diagnosing and treating skin diseases, potentially leading to better patient outcomes. © 2023 IEEE.

关键词： Classification CNN disease learning Scenarios ResNet50 Segmentation Skin Transfer

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VAdER-SC: A Model Agnostic Tool for large Scale, AI driven Automated Source Code Summarization

VADER-SC: A Model Agnostic Tool for Large Scale, AI Driven A...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Horne, dwight Pierson, Anthony Hedary, Elvis Freddo, Garrett Trejo, luis Matis, Mark Mask, lonnie Agile Dev. Engineering L3Harris Technologies GreenvilleTX United States Texas A&m University Deptartment of Computer Science and Engineering College StationTX United States

ISBN: (纸本)9798350327595

Production of a natural language description for the function of a source code segment is commonly referred to as source code summarization. Useful comments in source code can facilitate more rapid onboarding of new engineers and contribute to decreased maintenance costs. Unfortunately, the documentation task can also be labor intensive. In this paper, we introduce a new model agnostic tool for AI driven automation of source code summarization at scale. The initial version of the adVanced AI driven Enhancement to Readability for Spurce Code (VAdER-SC) software offers numerous options for customization and the ability to leverage a variety of AI models to enable experimentation in resource constrained environments, while also scaling up to benefit from larger models in contexts with increased compute resources. It further supports private cloud, self-hosted, and air-gapped network configurations for environments with strict intellectual property protections or processing of sensitive or controlled data. Qualitative and quantitative results suggest model selection, fine-tuning, and multi-shot tailoring significantly impact the quality of generated comments. VAdER-SC could be an enabler for practitioners to explore large-scale automation of AI driven source code summarization and researchers may find it enables studies with larger volumes of disparate data across a diversity of AI model options and target programming languages. © 2023 IEEE.

关键词： AI driven documentation automated source code summarization deep learning neural machine translation program comprehension software maintenance VAdER-SC

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Responsible AI on National Cybersecurity Strategy with Enhancement of Cybersecurity in Education

Responsible AI on National Cybersecurity Strategy with Enhan...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Tingare, Bhagyashree Ashok Prasanna lakshmi, G. Khedkar, Vijayshri Suresh Babu, Rakesh Thoppaen dhar diwan, Tarun Khatkale, Pravin B D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Department of Computer Science & Engineering Pune India HEXAWARE TECHNOLOGIES INC NJ United States Atal Bihari Vajpayee University Bilaspur India Sanjivani University Maharashtra Kopargaon India

ISBN: (纸本)9798350390049

For national cybersecurity to keep up with the digital transition, AI is more important than ever. Responsible AI improves cybersecurity and education by emphasizing openness and ethics. Ethical design is stressed from the outset in responsible AI. This article analyzes how artificial intelligence (AI) influences the national cybersecurity strategy, focusing on cybersecurity education development and effectiveness. The National Cybersecurity Strategy will frame this article. The major results show that responsible AI improves threat detection and response, cybersecurity infrastructure, talent shortage filling, collaborative research, and public trust. Educational institutions may now offer innovative training programs thanks to artificial intelligence. Completing this aim requires closing the gap between conventional methodologies and modern challenges. Closing gaps is vital. Functional artificial intelligence improves national cybersecurity and educational quality. This analysis shows that this conclusion is possible. This allows experts to properly react to shifting dangers. This is why it matters. © 2024 IEEE.

关键词： AI Integration AI-based Training Programs Cybersecurity Education Cybersecurity Infrastructure Ethical AI Governance National Cybersecurity Strategy Public Trust Responsible AI Skills Gap Threat detection

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Wearable devices for Patient Monitoring System using IoT 8

Wearable Devices for Patient Monitoring System using IoT

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Arunkumar, J.R. Raman, Ramakrishnan Sivakumar, S. Pavithra, Ravilla Modern Institute of Technology and Research Centre Department of Computer Science Engineering Rajasthan Alwar India Symbiosis Institute of Business Management Maharashtra Pune India Department of Computer Science Tamil Nadu Perambalur India R.M.D Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396638

The most crucial components of life is health. People could still not access adequate healthcare, nevertheless. It is brought on by restrictions on the technology used in hospitals and on how people may travel to hospitals. One of today's most popular topics, the Internet of Things (IoT), already provides many solutions, including in the healthcare industry. The field of wearable medical technology has emerged as one of the promising healthcare technologies. By actively collecting physiological indicators and monitoring metabolic status, these intelligent sensors help individuals lead healthier lifestyle and provide continuous healthcare information for disease diagnosis and treatment. Wearable technology is a new technique to track patients' post-intervention progress. It may lead to better patient outcomes and less demand for healthcare resources by enabling a faster and safer discharge from the hospital. Current findings have looked into the usage of wearable technology for monitoring. The ability to incorporate gadgets that can connect to the internet, provide data on patient health, and give real-time data to doctors who can help is becoming more and more possible due to the Internet of Things (IoT). It is obvious that health indicators like temperature, heartbeat, high blood pressure, and humidity represents a significant global economic and social issue. The patient's health is being monitored for health issues, and details can be viewed for obtaining the live patient data on a mobile web server at the correct time. © 2023 IEEE.

关键词： Internet of things

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Optimal Energy Management based on dynamic Programming

Optimal Energy Management based on Dynamic Programming

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing landscape of AI, ICSCAI 2024

作者： Haripriya, d. dahiwade, Mandakini Kotiyal, Arnav Al-Farouni, Mohammed Yadav, Kanchan Francis Raj, J. Relin Department of CSE Veltech Ranagarajan Dr.Saguntala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India Department of Basic Engineering Indira College of Engineering and Management Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350355338

The strategy based on reinforced learning will be able to present a solution that is close to the optimal solution achieved by using dynamic Programming (dP), and the power distribution for hybrid vehicles also seems promising with dP. It addresses nonlinear dynamical systems' optimal control issues. However, dP cannot be applied to the majority of problems of practical interest due to its high complexity in terms of computation and dimensionality. In various energy policies, variability has not been addressed, and it is based on fixed driving profiles, which affect vehicle operation. The proposed study's goal is to create an optimal hybrid energy management system that generates more power at the lowest possible cost. The control strategy has been formed based on dP through the rule extraction process, and the fuel consumption must be minimized for the drive cycles. It has simple mathematical foundations, ensures the optimal solution for a given tolerance, and can be easily implemented in a short computer program. The strategy is evaluated in terms of real and predicted values, which makes the performance indices, and this comparison is done on different levels of time. © 2024 IEEE.

关键词： Hybrid power

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Quantum-Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

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IEEE Internet of Things Journal 2025年第12期12卷 21225-21239页

作者： Jeong, Seon-Geun duc, Pham dang Anh do, Quang Vinh Noh, dae-Il Tung, Nguyen Xuan Chien, Trinh Van Pham, Quoc-Viet Hasegawa, Mikio Sekiya, Hiroo Hwang, Won-Joo Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Hanoi University of Science and Technology School of Information and Communication Technology Hanoi100000 Viet Nam Ton Duc Thang University Faculty of Electrical and Electronics Engineering Wireless Communications Research Group Ho Chi Minh City700000 Viet Nam Trinity College Dublin School of Computer Science and Statistics DublinD02PN40 Ireland Tokyo University of Science Department of Electrical Engineering Tokyo162-8601 Japan Chiba University Graduate School of Engineering Chiba263-8522 Japan Pusan National University School of Computer Science and Engineering Center for Artificial Intelligence Research Busan46241 Korea Republic of

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, subchannel assignment, and power allocation in a multiautonomous aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two subproblems: 1) a QA-based clustering and 2) subchannel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two subproblems. For the first subproblem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second subproblem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time. © 2014 IEEE.

关键词： Combinatorial optimization

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Effect of Transformer Windings (Not under the Test) on the Frequency Response (FR) of the Winding under the Test 4

Effect of Transformer Windings (Not under the Test) on the F...

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4th International Conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

作者： Gojiya, Mayur V Badgujar, Ketan P Electrical Engineering Department Shantilal Shah Engineering College Gujarat Technological University Bhavnagar India Electrical Engineering Department L. D. College of Engineering Gujarat Technological University Ahmedabad India

ISBN: (纸本)9781665456357

Sweep frequency response analysis (SFRA) is most conscious to changes in the electrical elements (R, l, C) related to the test setup and test object. Therefore, in order to monitor even the smallest winding movement in a transformer, SFRA testing is a very effective technique. The effect of changes in such electrical elements associated with the test setup (outside the test object) on the FR of the winding should be neglected. Similarly, the FR of the test object is affected by a factor located inside the transformer and unrelated to the test object. It is the transformer winding that is not being tested. All transformer windings are connected together. Even if one of the windings has a mechanical fault, it can interfere with the frequency response of the other healthy windings, even if it is not tested. In this article, experiment results are used to explore this idea. © 2022 IEEE.

关键词： Frequency response

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Borewell Rescue Operation Using Android Applications

Borewell Rescue Operation Using Android Applications

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： K Mohana Sundaram R Sasikumar Aarthi V Hemalatha J Hemapriya G Janani S Department of Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The urgent need for effective rescue operations in the context of borewell accidents, where innocent lives, especially children, are at risk. These bore-wells are trapping a lot of innocent kids, who are dying as a result. While bore- wells are intended to save lives, they have instead started to take a great number of innocent lives. Several times, large machines and a lot of human labor are used in the rescue efforts. These rescue operations are typically very drawn-out, difficult, and time- consuming processes. Conventional rescue methods involving excavation are slow, labor-intensive, and hazardous. In response to this challenge, the report proposes an innovative approach to borewell rescue operations. This method employs a mechanical apparatus equipped with a gripper arm, which navigates through the borewell channel under the control of a microcontroller. Introduces a novel approach to borewell rescue operations utilizing Android applications. The traditional methods of rescue involving excavation are often slow, laborious, and risky. This paper proposes an innovative solution leveraging Android technology to streamline and enhance the rescue process. The system employs an Android application that communicates with a mechanical apparatus equipped with a gripper arm, designed to navigate through the borewell channel. Through the application interface, rescue personnel can remotely control the movement and operation of the apparatus, optimizing the rescue efforts. This integration of Android technology aims to expedite rescue operations, minimize risks, and ultimately save lives more efficiently in borewell emergencies.

关键词： Pediatrics Navigation Prototypes Excavation Emergency services Real-time systems Personnel Grippers Remote control Accidents

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FusionSegNet: Integrating Modalities for Enhanced Brain Tumor detection and Segmentation

FusionSegNet: Integrating Modalities for Enhanced Brain Tumo...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Husain Rehmanji Sonia d’Silva Kunj Thakker Sudhir Bagul Dept. of Computer Engineering D.J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Improving diagnostic accuracy and striving for thorough analysis is always crucial in medical imaging, Multi Modal Medical Imaging has proved to be a useful approach in this area, providing computational power and accuracy for tasks that humans would otherwise have a tough time doing. This paper investigates the fusion of Magnetic Resonance Imaging (MRI) with Fluorodeoxyglucose Positron Emission Tomography (FdG-PET) or Technetium-99m-Tilmanocept Single-Photon Emission Computed Tomography (TITc-SPECT) scans which are generally used for detecting Gliomas or Metastatic Bronchogenic Carcinoma (Brain Tumors that are formed due to lung cancer). This paper targets a pipeline that will investigate the fusion of Magnetic Resonance Imaging (MRI) with Fluorodeoxyglucose Positron Emission Tomography (FdG-PET) or Technetium-99m-Tilmanocept Single-Photon Emission Computed Tomography (TITc-SPECT) scans which are usually used to detect these brain tumors. The denoising diffusion image Fusion Model (ddFM) is used to combine the structural and functional information from these medical scans and the fused results of these images are then used as input to CNN as well as a U-Net model for brain tumor detection and segmentation respectively. The performance of these models is evaluated based on the probability of tumor detection and the accuracy of segmentation. The main objective of this paper is to validate the effectiveness of the ddFM fusion approach for medical scans by demonstrating tumor detection and classification accuracy in the fused multi-modal images. The results of this paper, therefore, hold a potential for improving the clinical decision-making process and making more effective strategies for brain tumor diagnosis and its management through the use of complementary imaging data.

关键词： Image segmentation Accuracy Magnetic resonance imaging Computed tomography Computational modeling Brain modeling Telecommunication computing Positron emission tomography Medical diagnostic imaging Image fusion

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TMG-deepNet: A Novel deep learning Model for Tomato Maturity Grading

TMG-DeepNet: A Novel Deep Learning Model for Tomato Maturity...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Stanley, Berakhah F. Saumiya, S. Nisha, S.Rahmath Prabhu, T. R.m, Anushia Kumar R, Jeen Retna Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Electronics and Communication Engineering Chennai India Chennai Institute of Technology Chennai India SRM Institute of Science and Technology Tiruchirapalli India Dr. Sakunthala Engineering College Department of Biomedical Engineering Vel Tech Multi Tech Dr. Rangarajan Chennai India Dmi College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387230

Tomato maturity grading is a vital task in agriculture, directly impacting product quality and market value. Conventional grading techniques are unreliable and susceptible to inaccuracies since they frequently involve human examination. In order to overcome these obstacles, this study presents TMG-deepNet, a novel deep learning model for autonomous tomato maturity assessment that combines the positive aspects of Extreme learning Machine (ElM) and leNet-5. The leNet-5 architecture is utilized for feature extraction, capturing essential visual patterns related to tomato ripeness, while ElM is employed for fast and accurate classification of the extracted features. The proposed hybrid model leverages the simplicity and efficiency of leNet-5 along with the speed and performance of ElM, resulting in a robust and effective solution for grading tomatoes across different maturity stages. Multiple evaluations suggest that TMGdeepNet performs superior in terms of accuracy and computational performance than both current models and conventional techniques. This study demonstrates the way deep learning and ElM can be combined for useful automation in agricultural applications. © 2024 IEEE.

关键词： Fruits

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