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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Agarwal, Vivek Kumar, Sanjay Shukla, Anudita Singh, Kaustubh Siddiqui, Hamza Galgotias College Of Engineering & Technology Information Technology Department Greater Noida India Galgotias College Of Engineering & Technology Computer Science Engineering Department Greater Noida India Galgotias College Of Engineering And Technology Department Of Information Technology Greater Noida India

ISBN: (纸本)9798350330861

Diabetes is now a frequent condition among people worldwide. The prevalence of diabetes is increasing daily across various age groups due to numerous factors. Factors contributing to the development of diabetes include infections caused by bacteria or viruses, existence of chemical contaminants in food, autoimmune responses, obesity, unhealthy dietary patterns, lifestyle changes, alterations in eating habits, and environmental pollution. Therefore, diagnosis of diabetes in elementary stage is essential for preserving an individual's life. Diabetes mellitus, commonly referred to as diabetes, is a significant metabolic condition that significantly affects a person's overall health and wellness. Diabetes causes elevated blood sugar levels. Improper management of diabetes increases the likelihood of heart attack, retinopathy leading to vision loss, skin problems, and other ailments. Presently, there are over a million individuals affected by this condition. Early diabetes detection can help to take necessary measures. In this research, we applied the Support Vector Machine algorithm, a machine learning method, to detect patterns and risk factors in a dataset related to diabetes. This approach aids in effectively categorizing patients as either diabetic or non-diabetic. © 2023 IEEE.

关键词： Support vector machines

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Audiovisual speech recognition based on a deep convolutional neural network

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Data science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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Sentimental Analysis of COVID-19 Twitter Data Using Machine Learning 8th

Sentimental Analysis of COVID-19 Twitter Data Using Machine ...

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8th International Conference on Emerging Research in Computing, information, Communication and Applications, ERCICA 2023

作者： Likhith, S.R. Pooja Ahuja, S. Prathibha, B.N. Uma Shankari, B. Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India

ISBN: (纸本)9789819976218

Corona virus is considered as a scourge all over world by which most of the people got infected. Since the virus was spreading very quickly governments had to bring in very tough rules such as lock down. Lock down disrupted almost all sector like education, business etc.… During lock down and after the lock down people have used social media as their platform to express the opining regarding the pandemic. Tweeter is one such social media where people had shared their thoughts on the pandemic. In this study tweets which are made by the public is considered to analyses the sentiments. And in this research machine learning models are used to classify the data into five different classes namely positive, negative, neutral, extremely positive, and extremely negative. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes are the ML models used to classify the sentiment and are achieving on accuracy of 99%, 99%, 97%, 94%, and 73%, respectively. Further precision, recall, F1-score, and world clod are calculated for each and every class. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 Decision tree Logistic regression ML Naïve Bayes Random forest Sentiment analysis SVM Tweets

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A Usability Management Framework for Securing Healthcare information System

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computer Systems science & engineering 2022年第9期42卷 1015-1030页

作者： Hosam Alhakami Abdullah Baz Wajdi Alhakami Abhishek Kumar Pandey Alka Agrawal Raees Ahmad Khan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India

Transformation from conventional business management systems tosmart digital systems is a recurrent trend in the current era. This has led to digitalrevolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remainsa serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses andcompromising software usability as well. Most of the data breaches are financiallymotivated, especially in the healthcare sector. The cyber invaders continuouslypenetrate the E- Health data because of the high cost of the data on the darkweb. Therefore, security assessment of healthcare web-based applicationsdemands immediate intervention mechanisms to weed out the threats of cyberattacks for the sake of software usability. The proposed disclosure is a unique process of three phases that are combined by researchers in order to produce andmanage usability management framework for healthcare information system. Inthis most threatened time of digital era where, Healthcare data industry has bornethe brunt of the highest number of data breach episodes in the last few years. Thekey reason for this is attributed to the sensitivity of healthcare data and the highcosts entailed in trading the data over the dark web. Hence, usability managementof healthcare information systems is the need of hour as to identify the vulnerabilities and provide preventive measures as a shield against the breaches. The proposed unique developed model of usability management workflow is preparedby associating steps like learn;analyze and manage. All these steps gives an allin one package for the healthcare information management industry because thereis no systematic model available which associate identification to implementationsteps with d

关键词： Smart healthcare healthcare industry usability security

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Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1265-1282页

作者： Raftopoulou, Maria Da Silva, Jose Mairton B. Litjens, Remco Vincent Poor, H. Van Mieghem, Piet Uppsala University Department of Information Technology Uppsala751 05 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands Department of Networks The Hague2595 DA Netherlands Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. However, the limited communication bandwidth, the hardware of the agents and a potential application-specific latency requirement impact how many and which agents can participate in the learning process at each communication round. In this paper, we propose a selection metric characterizing each agent's importance with respect to both the learning process and the resource efficiency of its wireless communication channel. Leveraging this importance metric, we formulate a general agent selection optimization problem, which can be adapted to different environments with latency or resource-oriented constraints. Considering an example wireless environment with latency constraints, the agent selection problem reduces to the 0/1 Knapsack problem, which we solve with a fully polynomial approximation. We then evaluate the agent selection policy in different scenarios, using extensive simulations for an example task of object classification of European traffic signs. The results indicate that agent selection policies which consider both learning and channel aspects provide benefits in terms of the attainable global model accuracy and/or the time needed to achieve a targeted accuracy level. However, in scenarios where agents have a limited number of data samples or where the latency requirement is very stringent, a pure learning-based agent selection policy is shown to be more beneficial during the early or late stages of the learning process. © 2023 CCBY.

关键词： Federated learning

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Data Analytics Technique of Different IP Address Utilized for Cloud Services 9

Data Analytics Technique of Different IP Address Utilized fo...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Wani, Arpita Mohite, Shahuraj Gonge, Sudhanshu Joshi, Rahul Vora, Deepali Kotecha, Ketan Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India Department of Information Technology Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350383201

The utilization of different IP addresses for cloud services has become a critical aspect of modern data analytics. As cloud computing continues to revolutionize the way organizations store, process, and manage data, the need to optimize data analytics techniques for diverse IP address utilization has grown exponentially. This research delves into the various methods and strategies employed to effectively harness the power of multiple IP addresses in the cloud environment. The abstracted study begins by exploring the significance of IP addresses in cloud services and how their dynamic allocation can impact data analysis. It delves into the challenges faced by data analysts in handling varying IP addresses, such as security concerns, network complexities, and data consistency. Subsequently, the research highlights innovative techniques and tools designed to efficiently manage and analyze data from different IP sources. These include load balancing algorithms, virtual IP mapping, and geo-distributed data processing. Moreover, the research assesses the benefits and limitations of each approach, considering factors like performance, scalability, and cost-effectiveness. It also examines real-world case studies and success stories of companies that have leveraged these techniques to gain a competitive edge in the cloud-based data analytics landscape. In conclusion, the study advocates for the adoption of optimized data analytics techniques that incorporate diverse IP address utilization to maximize the potential of cloud services and facilitate data-driven decision-making in modern enterprises. © 2023 IEEE.

关键词： Data Analytics

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Deep Learning Model for Human Detection-Enhanced YOLO Comparative Analysis 4

Deep Learning Model for Human Detection-Enhanced YOLO Compar...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, P. Aswathy, R.H. Soms, Nisha Niranjani, V. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri eshwar college of engineering Department of Computer science and engineering Coimbatore India

ISBN: (纸本)9798331540364

Video surveillance systems employing closed-circuit television (CCTV) are crucial for public safety. Accurately detecting humans within video frames is a core component of this technology. This knowledge is crucial for numerous uses, including detecting possible dangers, observing crowd dynamics, and examining patterns of human behavior. This research introduces a new method called YOLOv8, designed to enhance human detection capabilities within CCTV footage. The proposed algorithm, YOLOv8, is built upon the popular object recognition framework, YOLO, but with significant enhancements to improve its accuracy and efficiency in detecting humans. YOLOv8 utilizes deep convolutional neural networks (CNNs) and advanced object recognition techniques to achieve high accuracy in human detection, even in challenging environments such as low light conditions, occlusions, and varying scales of human poses. To assess the effectiveness of YOLOv8, various tests were performed using standard datasets and real-world CCTV surveillance footage. The results demonstrate that YOLOv8 outperforms existing human detection algorithms regarding accuracy, robustness, and real-time processing capability. YOLOv8 achieved an average precision of over 95% in human detection, making it highly suitable for everyday CCTV surveillance. The proposed YOLOv8 algorithm has the potential to significantly improve human detection in security camera footage and enhance public safety. This research contributes to advancing human detection technology and can benefit various domains, including law enforcement, public safety, and video analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging Convolutional Neural Networks for Enhanced Educational Question Classification

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SN computer science 2024年第8期5卷 1052页

作者： Deena, G. Ancy Breen, W. Raja, K. Shanmuganathan, C. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Ramapuram Chennai India

Question classification is a critical task in natural language processing, as well as applications in question-answering systems and intelligent personal assistants. This study presents novel machine learning approach for automatic question classification based on Bloom’s taxonomy, a hierarchical structure of cognitive levels. Traditional methods rely on feature engineering and classic machine learning models, but these methods often lack the ability to understand complex patterns and connections in natural language data. This study proposes a novel approach using a convolutional neural network (CNN) model, which can automatically learn relevant features from input data without extensive feature engineering. The model classifies questions into pre-established categories or labels using an embedding layer, convolutional and pooling layers, and a fully linked layer for classification. We assess the model’s efficacy on a publicly accessible dataset generated by the subject expert and compare it to conventional machine learning methods like logistic regression and support vector machines. The CNN-based model outperforms conventional techniques, achieving greater recall, accuracy, precision, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bloom’s Taxonomy Convolutional Neural Network Machine Learning Word embedding

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Lung Tumor Volumetric Estimation and Segmentation using Adaptive Multiple Resolution Contour Model

Lung Tumor Volumetric Estimation and Segmentation using Adap...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： SenthilPandi, S. Kalpana, B. Kumar, Vinodh S. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Information Technology RMD Engineering College Chennai India

ISBN: (纸本)9798350305708

The need to identify pathology is strongly felt in image guided analysis. Tumor localization is essential for targeting the tumor. Tumor boundaries are often hard to delineate due to high variation in shape and size of the tumor. The tumor boundary is usually defined using the thresholding technique which may have high FP rate due to partial volume effects. Hence, this AMRC technique has to be repeated several times in order to get a more accurate result. The multiple thresholding technique often leads to an undesirable situation where the segmentation boundary contains holes which are hard to fill. The AMRC is a dynamic contour model that is able to adaptively adjust the contour to better fit the contour of the tumor. Segmentation of SVT involves two steps viz. identification of peak intensity pixels with the HECS model and definition of an adaptive contour with AMRC. © 2023 IEEE.

关键词： AMRC Segmentation Tumor Localization

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The Indian Search Algorithm 2nd

The Indian Search Algorithm

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2nd International Conference on Cybersecurity and Evolutionary Data engineering, ICCEDE 2022

作者： Dutta, Anurag Kanti Kumar, Pijush Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Information Technology Government College of Engineering and Textile Technology Serampore India

ISBN: (纸本)9789819950799

Searching algorithms deal with searching for key $$\mathcal {K}$$ in a heap of data. In this work, an indigenous Searching Algorithm—"The Indian Search Algorithm" has been proposed that will be efficient enough to search for a key in the order $$\begin{aligned} \textrm{log}_{\left(k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{\ddots }}}}}\right) }{\left(n-1\right) }+\textrm{log}_2{\left(n\right) } \end{aligned}$$ where $$\left(k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{k+\frac{1}{\ddots }}}}}\right) $$ is the kth metallic ratio. The Search Algorithm would be an efficient one at least for an Ordered List. In the paper, we have tried to incorporate two children from the same hierarchy—with the parent being the Generic Search with kth metallic ratio, which are Indo-Pellian Search, following the Pell Series, and Indo-Fibonaccian Search following the Fibonacci Series. The Computational Complexity of both have been evaluated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Divide and conquer Fibonacci’s numbers Metallic ratio Pell’s numbers Searching

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