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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Gupta Bhol, Seema Mohanty, Jnyana Ranjan Patnaik, Prasant Kumar Kiit University Department of Computer Applications Bhubaneswar India Kiit University Department of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350375237

Multi-criteria decision-making, MCDM consists of many methods to assist the decision-maker in selecting the best alternatives. MCDM has seen its application in a huge variety of problem domains and the healthcare sector is no exception. Medical domain is more challenging as it involves uncertainties, conflicting requirements, judgments from more than one decision maker and a mix of qualitative and quantitative data. The healthcare sector hugely depends upon various imaging techniques for the diagnosis of disease. In the past few decades, ultrasound techniques emerged as the most popular and effective aid in disease diagnosis. Thus for good and cost-effective diagnostics, medical experts should be equipped with good ultrasound machines. Selecting a good ultrasound machine needs evaluation of many features, thus MCDM methods can be deployed. The present paper proposes a comparative the study of most popular MCDM method, AHP and not so not-so-popular Electre method in the medical device selection in healthcare sector. The present study is considering Six Criteria and five alternatives, that can be scaled up to include more criteria and alternatives. © 2024 IEEE.

关键词： Ultrasonics

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Advances in Fuzzy Relation Inequalities and Optimization Techniques 2

Advances in Fuzzy Relation Inequalities and Optimization Tec...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Deo, Udit Jain, Jay Kumar Beg, M.M.S. Department of Mathematics and Computer Applications MANIT Bhopal Bhopal India Department of Computer Engineering Zakir Husain College of Engineering and Technology AMU Aligarh India

ISBN: (纸本)9798331530389

Fuzzy relation inequalities (FRIs) play a crucial role in various optimization and decision-making problems that involve uncertainty and imprecision. The solution of such inequalities has been an active area of research, leading to the development of a wide array of advanced algorithms designed to handle fuzzy constraints in optimization problems. This paper reviews the most prominent algorithms developed for solving fuzzy relation inequalities, focusing on the evolution of methods from traditional linear programming to modern metaheuristic algorithms. We explore linear programming approaches that integrate fuzzy relational constraints using different composition operations, such as max-min, max-Lukasiewicz, and addition-min compositions. The advancements in these methods, particularly in handling large-scale and complex fuzzy systems, are highlighted. Additionally, the paper examines the application of metaheuristics, including Particle Swarm Optimization (PSO), in solving fuzzy optimization problems under relational inequalities. The use of bipolar fuzzy relations in optimization models is also reviewed, emphasizing their ability to model conflicting objectives in decision-making. Moreover, geometric programming, hybrid algorithms combining fuzzy relations with other optimization paradigms, and future trends in integrating machine learning techniques into fuzzy optimization are discussed. The review provides valuable insights into the state-of-the-art algorithms for fuzzy relation inequalities, offering potential directions for future research and applications in dynamic, real-world systems. The proposed methods are crucial for improving decision-making in fields such as healthcare, finance, and intelligent systems, where uncertainty and imprecision are inherent. © 2025 IEEE.

关键词： Heuristic algorithms

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Enhancing Image Quality with GAN Super Resolution Evaluation 5

Enhancing Image Quality with GAN Super Resolution Evaluation

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kumari, Saumya Jha, Resham Ray, Aarushi Bilgaiyan, Saurabh Gourisaria, Mahendra Kumar Sharma, Ashish School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar India Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350379945

The present study presents a novel approach to enhance the quality of celebrity photos by utilizing an innovative framework known as Profile SR-GAN. The aim is to increase the resolution of low-quality celebrity photos, so they appear significantly better. In this work, Generative Adversarial Network (GAN) has been applied as it is highly efficient at generating images. Two key performance metrics have been used, PSNR and SSIM, to evaluate the effectiveness of our approach. In comparison to the noise in the original low-quality photographs, PSNR enables us to determine how clear the high-resolution images are. SSIM analyzes brightness, contrast, and general structure to assist us compare the original and upgraded photos. The effectiveness of GAN model has been demonstrated in enhancing the image resolution through our detailed study. This research study has gained a greater knowledge of this technique's effectiveness and its potential uses in many contexts. © 2024 IEEE.

关键词： Generative adversarial networks

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Computational Analysis for Candidate X-ray Images Using Generative Adversarial Network

Computational Analysis for Candidate X-ray Images Using Gene...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Kumar, Pradeep Raja, Linesh Kumar, Ankit Department of Computer Applications Manipal University Jaipur Jaipur India Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (纸本)9789819909803

Medical image analysis is becoming a critical deep learning application based on machine learning by contributing to developing a more sustainable health system, which probably overpowers doctors’ workload drastically. With the advancement in deep learning technique, there is rise in the samples for training of diagnosis and treatment models. Generative Adversarial Networks (GANs) have sought attention in the field of medical image processing by their outstanding image generation capabilities and data generation without mapping the probability density function explicitly. GAN methods simulate the actual data distribution and reconstruct estimated accurate data. Medical images are available in less amounts, and the acquiring of medical image annotations is costly;therefore, generated data can solve the problem of data insufficiency or data imbalance. GANs are proven very useful in data augmentation and image translation. These qualities of GAN have fascinated researchers, and rapid adoption is noticed in the reconstruction, synthesis, segmentation, denoising detection, and classification of medical images. Finally, GAN models are extensively used for feature selection and extraction for medical image analysis and early diagnosis of diseases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis 7

A comparative analysis of using Deep Learning and Machine Le...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Divya, S.B. Mary, S. Prince Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai India Mangalam College of Engineering Department of Computer Applications India

ISBN: (纸本)9798350372816

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020-2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered. © 2024 IEEE.

关键词： Network intrusion

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Severity Level Prediction and Finding Cause in Flight Accidents Using Machine Learning

Severity Level Prediction and Finding Cause in Flight Accide...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Kumar, M. Sunil Neeraja, P. Amulya, R. Swarna, Kopparthi Reddy, Tadipatri Surya Prakash Department of Computer Applications AP Tirupati India Sree Vidyanikethan Engineering College Department of Computer Applications AP Tirupati India

ISBN: (纸本)9798350329186

The safety of the airlines and their passengers should be our top priority. Various safety checks are performed continuously and manually round-the-clock, and the airline team takes care of all safety precautions and measures, but there are still some cases of accidents due to a variety of factors. To improve aviation safety and stop future accidents, it is essential to estimate how severe a flying mishap would be. In this study, we provide a method that estimating the seriousness of flying incidents. Our findings show that the suggested method beats conventional machine learning methods, predicting the severity of aviation accidents with an accuracy of up to 85%. Our work stresses the value of enhancing the effectiveness of models for predicting the seriousness of aircraft accidents. The suggested method may be applied by regulators and specialists in aviation safety to improve aircraft safety by creating more potent accident prevention measures. © 2023 IEEE.

关键词： Aircraft accidents

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Real-Time Identification of Medicinal Plants Using Deep Learning Techniques 2

Real-Time Identification of Medicinal Plants Using Deep Lear...

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

作者： Girinath, S. Neeraja, Peddinti Kumar, M. Sunil Kalyani, S. Mamatha, B. Lakshmi Gruhalakshmi, N.R.T. AP Tirupati India Department of Computer Applications Sree Vidyanikethan Engineering College Mohan Babu University AP Tirupati India Department of Computer Applications Sree Vidyanikethan Engineering College AP Tirupati India

ISBN: (纸本)9798350384277

A significant portion of the global population uses medicinal plants as a primary source of therapeutic chemicals and as an alternate source of pharmaceuticals. Plant identification from photos is a rapidly developing field of research at the moment due to recent developments in computer vision. High precision, accuracy, and practical application were demonstrated by several findings. Exact automatic identification of medicinal plants was the aim of this study. The illumination in the area is uneven, making it challenging to distinguish a leaf from its backdrop. We provide a procedure that uses a sample of a plant's leaves to identify the species. We provide a new collection of medicinal plant photos, which includes one and ten (10) different plant species categories. In the suggested technique, relevant information is automatically extracted from plant photos using convolutional neural networks (CNNs). Large-scale high-resolution photographs of a variety of therapeutic plant species are used to train the CNN model. Using distinguishing visual characteristics, such as leaf form, color, and texture, the trained model is able to identify several plant species. Utilizing a smartphone or other portable device, users may snap images of plants with the developed system and share them with others for real-time plant identification on a mobile or embedded platform. Following analysis of the obtained image using the trained CNN model, a precise classification result is obtained fast. © 2024 IEEE.

关键词： Plant diseases

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Internet of Brain, Thought, Thinking, and Creation

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Chinese Journal of Electronics 2022年第6期31卷 1025-1042页

作者： ZHANG Zhimin YIN Rui NING Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Engineering Research Center for Cyberspace Data Analysis and Applications

Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain(IoB), Internet of thought(IoTh), and Internet of thinking(IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation(IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the Io C stage.

关键词： thinking space IoTk Internet of thinking IoTh Internet of thought cyberspace development Internet of Things Internet of brain cyber-physical systems IoB human civilization emergence

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An Enhanced Career Prospect Prediction System for Non-computer Stream Students in Software Companies 1st

An Enhanced Career Prospect Prediction System for Non-comput...

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1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Abraham, Biku Ambili, P.S. Department of Computer Applications Saintgits College of Engineering Kottayam India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9789811984921

The stakeholders of the present education system have a key focus on the chances of on-campus employability of their wards. Since software companies play prominent role as campus recruiters, there is a low demand for the traditional engineering branches. The need to enhance skills for software related employment is a challenge to the private sector professional institutions for the existence of non-computer streams in this scenario. In this study, the authors being academic experts in the similar field, attempt to support institutions, students, curriculum designers, and the faculty by providing insight to better employment possibility in campus. Data set for the study is from the same institution for which the placement prediction was completed. The methodology models the problem as a sequential event prediction problem and employs deep learning techniques. The system extracts data from data set with 19 attributes. The results indicate that this predictive approach provides enhancement methods by which a non-computer stream student can ensure full-time employment in software related jobs through in-campus selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Analysis of Feedback and Re-Circulating Buffer Based Optical Packet Switch 1

Analysis of Feedback and Re-Circulating Buffer Based Optical...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sinha, Amit Bhardwaj, Diwakar Shukla, Vaibhav Computer Engineering &Applications GLA University Mathura India Tech Mahindra Mumbai Mumbai India

ISBN: (纸本)9798331518578

In optical packet switching (OPS), contention is major phenomenon where more than one packet tries to occupy the same output port. This paper introduces a new design for an optical packet switch based on hybrid buffering strategy. In this hybrid scheme both Feedback and Re-Circulating buffers are used to hold the contending packets. OPS is a latest transmission technology which is based on the optics and provide high bandwidth and very low latency. However, as the traffic conditions varies continuously, therefore a key challenge is to design buffering strategy. In this study, we explore how combining the benefits of both Feedback and Re-Circulating buffers can improve the overall performance of OPS. The performance of the switch is measured in terms of packet loss probability. By looking at packet loss and buffer usage under different traffic conditions, main aim to understand the impact of this hybrid approach. Our results show that using both types of buffers in the same switch leads to a lower chance of packet loss, improving the overall network performance. We found that the proposed design performs better in terms of packet loss probability compared to other approaches. © 2025 IEEE.

关键词： Packet loss

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