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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Real-Time AI Driven Shooting Posture Assessment and Correction for Professional and Military Training Using Machine Learning, OpenCV, and MediaPipe 4

Real-Time AI Driven Shooting Posture Assessment and Correcti...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sarswat, Dhairya Moradabad Institute of Technology Aktu University Computer Science and Engineering Dept. U.P. Moradabad India

ISBN: (纸本)9798350387490

Shooting is one of the most popular Olympic Games in India As for now Shooting contributed 4 out of 35 Olympic medals for the country. Thus, the presented paper aims to expose some advanced training methodologies in order to achieve the maximum potential of this kind of sport. This paper also describes an implementation of an AI-based system for shooting posture evaluation and feedback by employing computer vision and sophisticated posture recognition methods in real-time control system. For image acquisition and processing, the system incorporates OpenCV and, for body landmarks detection, it uses MediaPipe's pose estimation models. It therefore computes outstanding angles as well as changes in relation to these postures to allow users to correct their positioning. Refinement of the training process in the usage of weapons is evident by adoption of the proposed solution that has displayed efficient concerning the professional shooters, military personnel, and the target consumers. The ability of the system to give real-time accurate feedback on the participants' posture while at the same time increasing the training quality and performance efficiency minimizes the gap between conventional and contemporary technological approaches in shooting training. This research also discusses the utility of the system in multiple shooting disciplines and situations. Through the use of real-time video data and through the utilization of sophisticated machine learning algorithms, the system is able to tailor feedback depending on the shooting position or shooting posture or tactic that the user signifies to the system. Thus, the proper combination of real-time posture assessment guarantees that users get necessary feedback instantly and apply it in the process of enhancing their technique. The research therefore establishes the role of the system in enhancing training efficiency and effectiveness;though this study could be relevant to other fields such as security, the application

关键词： Feedback

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Nutrition Estimation and Dietary Assessment in Indian Food through Deep Learning

Nutrition Estimation and Dietary Assessment in Indian Food t...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Nandanwar, Amit Kumar Kushwaha, Mohit Vishwatej, Kondabathula Singh, Dhirendra Pratap Choudhary, Jaytrilok Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering M.P Bhopal India

ISBN: (纸本)9798350348460

Food Nutrition prediction is an interesting area of work before 10 years. There was a active research went on. But after that there was a break of research on this area. Now after corona some active work is going on. As the people are interested to know the intake of their food nutrition. And in this paper proposing the nutrition prediction in the Indian food. In this paper followed the deep learning approach to predict the nutrition in the food. The person who is interested in finding the nutrition intake will capture the image of the food before intake and can get the nutrition values before going to consume. So, it can help the people to get an idea and track of the nutrition they are *** this we are using a deep learning approach to find the nutrition in the image. User will capture the food image then that image is sent through CNN model. then by the CNN model will find what food item is captured. And before capturing the image will ask user information also like name, age, height and weight. So will calculate the BMI and the nutrition required for the person. And will store the nutrition content user is consuming in every meal. So, at end will show the BMI of the user, and present nutrition user is consuming from the meal and total nutrition user has to consume in a day. Our model for food classification achieved an accuracy of 91.87% © 2024 IEEE.

关键词： BMI (Body Mass Index) Convolutional Neural Networks Data collection Deep Learning Dietary management Keras Machine Learning Pretrained models TensorFlow Transfer Learning

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Classifying Temporomandibular Disorder and Trigeminal Neuralgia using Self-Supervised and Deep Learning Approach 4

Classifying Temporomandibular Disorder and Trigeminal Neural...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Saigaonkar, Swati Narawade, Vaibhav Ramrao Adik Institute of Technology D Y Patil Deemed to be University Dept. of Computer Engineering Nerul India

ISBN: (纸本)9798350353266

The study explores the challenges of accurately diagnosing Temporomandibular Disorder (TMD) and Trigeminal Neuralgia (TN), two conditions that often result in severe facial pain. Proper assessment of pain characteristics is essential to avoid misdiagnosis and ensure effective treatment for these conditions, and clinical notes play a significant role in distinguishing between these disorders as they contain rich information. The model was initially trained using a selfsupervised learning approach, where descriptions of both TMD and TN were fed using a Masked Language Modeling (MLM) technique. A custom deep learning model was built. This approach was chosen because the quantity of clinical notes from the MIMIC-III database alone was insufficient to train a model capable of precisely distinguishing between the two diseases. The accuracy obtained is significantly high with a value of 0.98 as against fine-tuned baseline model DISTILBERT. For rare diseases, often the problem data scarcity arises which could be tackled using the suggested approach. © 2024 IEEE.

关键词： Federated learning

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Recognition and Processing of phishing Emails Using NLP: A Survey

Recognition and Processing of phishing Emails Using NLP: A S...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Anilkumar, Chunduru Karrothu, Aravind Mouli, Nandam Sri Tej, Chaduvula Bhanu GMR Institute of Technology Rajam Dept of Information Technology Andhra Pradesh Vizianagaram India GMR Institute of Technology Dept of Computer Science and Engineering India

ISBN: (纸本)9798350348217

Email is preferred by both individuals and businesses as one of the most important forms of communication. Despite the popularity of alternatives like electronic communications, mobile applications, and social networks, email usage and importance have been raising continuously. Phishing is the fraudulent practice of stealing any person's confidential information, such as financial details, login information etc. In which they look completely authentic and genuine and extremely hard for victim to detect whether the mail is spam or not. Moreover, the number of phishing emails is growing daily. So, handling of phishing emails is a crucial research issue. NLP techniques are useful for training data to detect spam content. In this work we studied leading research areas in phishing email detection and features used in spam emails. Feature extraction and selection plays a key role in phishing email detection. This paper also explains how actually the phishing attack happens and obtained results including information about concepts of natural language processing and working procedure. © 2023 IEEE.

关键词： Electronic mail

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Real-Time Detection of Sea Urchins Using Convolutional Neural Networks

Real-Time Detection of Sea Urchins Using Convolutional Neura...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nair, Vismaya V Bolem, Srinivas Nanthini, N. Parveen Akhther, A. Sathyabama University Dept. of Computer Science Engineering Chennai India National Institute of Ocean Technology Chennai India

ISBN: (纸本)9798331540821

The marine ecosystem contributes significantly to our planet's ecological balance. It supports a wide range of organisms, from microscopic plankton to giant marine mammals. Each species plays a unique role in the proper functioning of the ecosystem. One such species that significantly contributes to the biodiversity of the marine environment is the Sea urchins. The Sea urchins are known as the' indicator species' of the marine ecosystem and their presence provides valuable insights on the health and overall condition of the marine environment. Monitoring, understanding, and analyzing the population growth of sea urchins is vital for research and conservation. There has been multiple research that focus on identification and classification of other marine species but only a few have ventured into the domain of sea urchin detection. This paper presents an approachto automate the detection of sea urchins on the ocean floor using Convolutional Neural Networks (CNNs), computer vision and image processing. By harnessing CNN, we aim to simplify the labor-intensive task of manually identifying sea urchins, which are still largely carried out by divers. The proposed approach aims to provide a robust solution for the detection of sea urchins, which in turn would help in enhancing marine research and conservation techniques. Additionally, the model is loaded into Raspberry Pi, which is placed on a surveillance vehicle or aplatform above the surface of the water, it processes the input received from the camera that is deployed underwater in a watertight casing. The results are then transmitted to the user via GSM model of communication to any device within its vicinity. Our model shows an accuracy rate of 95.31%. By automating the process, this model facilitates the study of sea urchins and the surrounding marine environment, making it more accessible. This method helps to close the gap between marine biology, computer vision and deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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Improved Semi Supervised Image Classification using GAN

Improved Semi Supervised Image Classification using GAN

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Kushwaha, Mohit Nandanwar, Amit Kumar Joy, Ashley Varghese Choudhary, Jaytrilok Singh, Dhirendra Pratap Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering M.P Bhopal India

ISBN: (纸本)9798350348460

Image classification is the process of categorizing images into one or more pre-defined classes or categories based on the visual content of the image. The goal of image classification is to create a model that can accurately predict the class of an input image that it has not seen before. In this paper, a semi-supervised model has been proposed for image classification that uses Generative Adversarial Network (GAN) that can give excellent results using only few labelled training data. © 2024 IEEE.

关键词： CIFAR-10 GAN Image classification MNIST Semi-supervised learning

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