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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Shariff, m. Ahsan Vimal, S.P. Gopi, B. Anbarasi, A. Tharun, R. Srinivasan, C. M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Tamil Nadu Chennai India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Tamil Nadu Rasipuram India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mother Theresa Institute of Engineering and Technology Department of Civil Engineering Andhra Pradesh Palamaner India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

To improve text input for motor-disabled people, this research uses the Internet of Things (IoT) and machine learning. Swipe-to-Type, a popular touch-based input technique, is the study's focus. User swipe motions and contextual data are collected in real-time using IoT devices. A machine learning framework trains the algorithm to adapt to disabled users' motor skills and preferences. Addressing motor impairment problems, the suggested method improves text input efficiency and personalization. The Swipe-to-Type algorithm is constantly adjusted based on learned patterns to maximize text input speed and accuracy. Integration of IoT devices allows continuous monitoring and adaption, guaranteeing a responsive and user-centric solution. The study technique includes motor disability data gathering, machine learning model creation, and algorithm refining. Preliminary text input performance enhancements may improve communication and accessibility for motor-disabled people. By using IoT and machine learning to empower motor-disabled persons, this research advances assistive technology. The results show that adjusting input techniques to varied user demands is feasible and successful, encouraging digital inclusion. © 2024 IEEE.

关键词： Assistive technology

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Comparative Analysis of Non-Blind Deblurring methods for Noisy Blurred Images

arXiv

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arXiv 2022年

作者： Dasgupta, Poorna Banerjee Researcher M.Tech Computer Science and Engineering Nirma Institute of Technology Gujarat India

Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with the associated point spread function, along with additive noise. However, the blurred image often contains inadequate information to uniquely determine the plausible original image. Based on the availability of blurring information, image deblurring methods can be classified as blind and non-blind. In non-blind image deblurring, some prior information is known regarding the corresponding point spread function and the added noise. The objective of this study is to determine the effectiveness of non-blind image deblurring methods with respect to the identification and elimination of noise present in blurred images. In this study, three non-blind image deblurring methods, namely Wiener deconvolution, Lucy-Richardson deconvolution, and regularized deconvolution were comparatively analyzed for noisy images featuring salt-and-pepper noise. Two types of blurring effects were simulated, namely motion blurring and Gaussian blurring. The said three non-blind deblurring methods were applied under two scenarios: direct deblurring of noisy blurred images and deblurring of images after denoising through the application of the adaptive median filter. The obtained results were then compared for each scenario to determine the best approach for deblurring noisy images. © 2022, CC BY-NC-ND.

关键词： Image enhancement

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Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

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measurement: Sensors 2024年 33卷

作者： Yamini, B. G, Pradeep D, Kalaiyarasi m, Jayaprakash G, Janani G S, Uthayakumar India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology R.M.K. Engineering College India Department of Information Technology R.M.D Engineering College India Department of Electronics and Communication Engineering St.Joseph's Institute of Technology India

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data. © 2024

关键词： Internet of things

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Deep Transfer Learning model-Based Automated Detection of COVID-19 from X-ray Images and Interpretation of COVID-19 Images Using GLCm Texture Features 6th

Deep Transfer Learning Model-Based Automated Detection of CO...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Ankalaki, Shilpa Shorya, Kartikeya majumdar, Jharna Department of M.Tech CSE Nitte Meenakshi Institute of Technology Bangalore560064 India Department of CSE Nitte Meenakshi Institute of Technology Bangalore560064 India Department of M. Tech Computer Science & Engineering Head Centre for Robotics Research Head Centre for Space Research Nitte Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811613418

The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on all the sectors of the world like public health, global economy and daily lives. COVID-19 positive cases and death due to COVID-19 are rapidly increasing day by day. It is crucial and essential for fast and accurate automatic detection of COVID-19 infection to make better decisions and to provide appropriate treatment for the patients that can hopefully save their lives. The current has employed the VGG19, RESNET50 and DesNet121 deep learning convolutional neural network with transfer learning to identify and classifies the X-ray images into COVID-19 and non-COVID-19 classes. This study has been extended to analyse the factors which distinguishes COVID-19 and non-COVID-19 images. To accomplish this task, we have employed GLCm features and determined that variance is the best feature for this purpose. GRAD-CAm algorithm has been used to interpret the decision of CNN architecture. In this study, VGG19 and DenseNet121 achieved the classification accuracy of 98.80%, and ResNet50 achieved the accuracy of 97.65% for binary classes (COVID-19 and non-COVID-19 classes). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Preetha, V. Sathishkumar, P. Janani, m. manohari, B. Ida, S. Jhansi Suganyadevi, S. Sri Ramakrishna Institute of Technology Department of CSE Coimbatore India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of CSE Chennai India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sri Krishna College of Engineering and Technology Department of M.Tech Computer Science and Engineering Coimbatore India R.M.K Engineering Colleg Department of Computer Science and Business Systems Kavaraipettai India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses carried out for this study led the researchers to the conclusion that hyperboles may be found in unbiased datasets, which helps improve sarcasm identification as well. © 202

关键词： Sentiment analysis

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Challenges with reinforcement learning in prosthesis

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materials Today: Proceedings 2022年 49卷 3133-3136页

作者： Deepali Salwan Shri Kant Himanshu Pareek Roopali Sharma Pt. Deen Dayal Upadhyaya National Institute for Physically Handicapped New Delhi India Research and Technology Development Centre Department of Computer Science School of Engineering and Technology Sharda University Greater Noida India M.Tech IIT Hyderabad India M.Tech Amity University India

Reinforcement Learning has work wonders in games like Atari and AlphaZero. Recent advancement in Deep Reinforcement Learning showcase it’s ability in the active Prosthesis as well. RL is being used widely to solve problems where Learning of the Agent in its own environment is as necessary as training the model beforehand. However, model developed, and successful in the gaming environment could still need to be tuned to be effective with Real Time devices such as Prosthetic Limb and other Real-World devices. In this article, main challenges are presented which we face while working on a model Based and model Free Reinforcement Learning in real world environment and suggesting an approach which would work uniformly on most of the Real Time scenarios. We observed the performance and noticed that there are couple of factors which needs to be taken care of in Real Time Applications which are not much though about in games and other online applications. We also compared the algorithms such as Policy Proximal Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG) vs model Based Policy search with Gaussian Processes and found out that a mix of model-Based and model-Free (mBmF) performed the best individually despite of all the challenges.

关键词： Reinforcement learning Artificial intelligence Deep learning Challenges model-based model-free Prosthesis OpenSim

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Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning 4

Intelligent Deep Learning Framework for Breast Cancer Predic...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Rao, Chintala Nageswara Chatrapathy, K. Jainul Fathima, A. Sathish, Goski mukherjee, Saptarshi Reddy, Pundru Chandra Shaker CSE-AI&ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing&Information Technology REVA University Karnataka Bengaluru India Information Technology Francis Xavier Engineering College TamilNadu Tirunelveli India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology Uttarakhand India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305258

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (mL) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer's neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard mL strategies: decision-tree (DT), support-vector-machine (SVm), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard mL models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate. © 2023 IEEE.

关键词： Breast cancer Carcinoma Deep Learning KNN machine learning RNN SVm

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Intrusion detection based on phishing detection with machine learning

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measurement: Sensors 2024年 31卷

作者： Jayaraj, R. Pushpalatha, A. Sangeetha, K. Kamaleshwar, T. Udhaya Shree, S. Damodaran, Deepa Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur TN Chennai India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology TN Coimbatore India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology TN Chennai India Department of Computer Science and Engineering Alpha College of Engineering and Technology Puducherry India VITBS Vellore Institute of Technology Chennai Campus TN India

machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative Distribution Function gradient (CDF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method. © 2023

关键词： Intrusion detection

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Utilizing Artificial Neural Networks and multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, mCV, mCH, mCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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International Conference on Computation System and Information technology for Sustainable Solutions (CSITSS)

作者： K. Chokkanathan S.m. Karpagavalli G. Priyanka K. Vanitha K. Anitha P. Shenbagavalli Department of Artificial Intelligence Madanapalle Institute of Technology & Science Department of M.Tech Computer Science and Engineering Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology CSI College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings.

关键词： Data privacy Accuracy Training data computer architecture Transforms Zero Trust Time factors computer security Artificial intelligence Resilience

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