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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Agarwal, Ankita Patil, Nagaraj Rajeswari, M. Yadav, Gitanjali B. Mishra, Rahul Sood, Gourav Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Deep gaining knowledge of and records science are the most important and surprisingly influential fields of technology. Deep studying can system extensive statistics to discover styles, create insights, and have their personal choices concerning further analysis. Facts and technological know-how are centered on information-pushed business selection-making, using predictive analytics, machine studying, and other more recent algorithms to perceive, refine, and increase intelligent choices based on information. Those fields are rapidly developing and overlapping, creating an ever-increasing demand for each technical professionals and people with an intensive knowledge of statistics science and deep mastering. Integrating deep gaining knowledge of and records technology can create some challenges and possibilities which have to be explored. © 2024 IEEE.

关键词： Contrastive Learning

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Detection of Abnormalities in Chest X-Rays Using Convolutional Neural Networks

Detection of Abnormalities in Chest X-Rays Using Convolution...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Babu, Harshith Mishra, Rahul Karthigaikumar, P. Mahalle, Parikshit N. Parid, Prasanta Kumar Pund, Sachin Hindusthan College of Engineering & Tech Computer Science and Engineering Coimbatore India Centre for Interdisciplinary Research in Business and Technology Institute of Engineering and Technology Chitkara University Punjab India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Kiit University Department of Rural Managementn Bhubaneswar India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350329773

The purpose of this look is to increase an automated gadget for detecting chest X-ray imaging abnormalities through a Convolutional Neural community (CNN). The usage of a CNN permits real-time detection of abnormalities that could otherwise be missed via radiologists, as well as increases the efficiency of clinical experts by using being able to hit upon ability abnormalities without the need for extra tests. In this observation, 285 chest scans had been accrued from a local health facility and analyzed using a custom-built CNN. Through the use of various convolutional layers, various functions have been extracted and applied to stumble on abnormalities. Adjustments to the model have been made by including additional convolutional layers and adjusting the filters. The ensuing model carried out a ninety-seven - 2 % accuracy fee in identifying abnormalities in chest X-rays. The outcomes of this look display the hit implementation of a CNN in detecting abnormalities in chest X-rays. In addition, further studies are had to refine the model and boom accuracy. Moreover, similar research is recommended to examine its use in scientific decision-making for sufferers. The results of this have a look at can be seen as a step forward in the improvement of automated clinical analysis structures and their usage in clinical decision-making. © 2023 IEEE.

关键词： Convolution

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical Recording systems 9

Certain Investigation of Optimization Methods of Sensor Node...

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

作者： Ashokkumar, N. Pappa, C. Kanmani Siva Kumar, A. Srinivasan, Dhamodharan Vijay, M.M. Mohan Babu University Department of ECE Tirupati India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of ECE Tamilnadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India SCAD College of Engineering and Technology Department of ECE Tirunelveli India

ISBN: (纸本)9798350397376

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. Downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence. © 2023 IEEE.

关键词： Electrocardiograms

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Exploring the Benefits of DNS-Based Authentication for Wireless Network Security 3

Exploring the Benefits of DNS-Based Authentication for Wirel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swarup, Lakshya Jayadurga, R. Borade, Monali R. Ramachandran, T. Singh, Akhilendra Pratap Prabha, Savita Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Wi-wireless networks are becoming more pervasive and are frequently the primary goal of malicious attackers. Therefore, it is miles crucial that wireless networks are nicely secured to defend their users from malicious moves. DNS-based total authentication is a promising answer for attaining ease of authentication of wi-wireless customers in an open Wi-Fi community. This paper surveys numerous current works that discover the wi-fits wireless of DNS-based total authentication for Wi-Fi wireless community safety. especially this paper opinions three one-of-a-kindwireless strategies of using DNS for authentication purposes: making use of DNS's cache for identifying windows-primarily based Wi-Fi wireless clients;Open Mobile Alliance (OMA)'s tool control (DM) projects to relaxed get right of entry to;and a modern scheme for protecting the use of the DNS-based techniques. Wi-finally, this paper discusses a few safety-demanding situations and future studies directions. © 2023 IEEE.

关键词： authentication initiatives management protecting secured

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Exploring the Security Implications of Stream and Block Ciphers in Cryptography and Network Security 3

Exploring the Security Implications of Stream and Block Ciph...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mondal, Dipannita Hemelatha, S. Jayadurga, R. Sharma, Nakul S. Jain, Sidharth Kumar, Jambi Ratna Raja Dr. D.Y.Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350319125

This paper examines the security implications of movement and block ciphers in cryptography and network security. Circulate ciphers encrypt facts as they are transmitted over a network. A block cipher encrypts and decrypts a set amount of information. The paper discusses how these types of cipher paintings and opinions affect data security. Additionally, the paper looks at several demanding situations associated with using them, along with crucial changes, block size, critical duration, and overall performance. The paper provides a complete assessment of the pros and cons of circulate and block ciphers and gives pointers for which sort of cipher to apply in one-of-a-kind situations. © 2023 IEEE.

关键词： assessment ciphers duration performance pointers

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Disease Detection in Arecanut using Convolutional Neural Network

Disease Detection in Arecanut using Convolutional Neural Net...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： V Arun Karthik Jatin Shivaprakash D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Arecanut, also known as betel nut, is a tropical crop predominantly grown in India. The country holds the second position globally in terms of the production and consumption of arecanut. Throughout its life cycle, the areca nut plant is susceptible to a variety of diseases affecting its roots, trunk, leaves, and fruits. Some of these diseases are visible to the naked eye, while others are not. Farmers traditionally analyze every crop to detect any signs of disease, a process that is extremely *** this study, we propose an automatic system that aids in detecting the diseases of arecanut leaves using Convolutional Neural Networks (CNN) and deep learning algorithms. A Convolutional Neural Network is a Deep Learning algorithm that takes an image as input, assigns learnable weights and biases to various features in the image, and then learns from the results to distinguish one feature from another. This approach significantly reduces the manual effort required in disease detection and increases the efficiency of the process. A datset is developed with many photos of healthy and sick arecanut leaves in order to train and evaluate the CNN model. An 80:20 split of the dataset was made into training and testing data. The goal of training the model over multiple epochs was to maximize validation and test accuracy while minimizing loss. It was discovered that the suggested method for diagnosing arecanut illnesses was precise and successful. This study contributes to the ongoing efforts to leverage technology in agriculture, with the aim of improving crop health and productivity. This study indicate that the proposed system can accurately identify diseases in arecanut leaves with a good and best accuracy. The system was able to distinguish between healthy and diseased leaves, and suggest appropriate remedies for the detected diseases. These findings demonstrate the potential of using Convolutional Neural Networks and deep learning algorithms in the field of agriculture, par

关键词： Deep learning Training Productivity Plant diseases Accuracy Crops Agriculture

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Violence Detection system using MobileNetV2

Violence Detection System using MobileNetV2

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Rahul Kumar Aniket Gupta D Rajeswari Department of Data Science and Business Systems School of computing College of Engineering and technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The number of cases of violence and fights has been increasing around the world. With the use of CCTV, such incidents can be recorded but the detection of Violence is a major issue around the globe, and it plays a vital role in the safety and security of the area. It helps in effective surveillance and law enforcement. The effectiveness of any violence detection system depends on the speed and accuracy with which it detects the events and how it performs over different types of videos of varied sizes and formats. In the proposed algorithm, the dataset used contains videos of different types and scenarios to help the model understand and train better. To improve the overall effectiveness of the model, Lightweight CNN MobileNetV2 has been used along with that Bidirectional LSTM (BiLSTM) has been used. The accuracy rate of the proposed model is 98%.

关键词： Analytical models Accuracy Computational modeling Surveillance Bidirectional control Feature extraction Security

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN Computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity 22

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023

作者： Al Sibahee, Mustafa A. Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq University of Basrah College of Education for Pure Sciences Department of Computer Science Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China

ISBN: (纸本)9798350381993

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections. © 2023 IEEE.

关键词： Cryptography

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