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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prabagaran, A. Balamurugan, N.M. Kalavathi, S. Padmanabhan, P. Angelin Blessy, J. Gupshup Technology India Pvt Ltd Bengaluru India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Information Technology Chennai India Noida Institute of Engineering & Technology Department of CSE Uttar Pradesh Greater Noida201306 India School of Computing Science and Engineering Galgotias Univeristy Dept of Computer Application India

ISBN: (纸本)9798350327533

The world's most serious chronic liver condition is Non-Alcoholic Fatty Liver Disease (NAFLD). The early stage prediction saved people's lives. Recent machine and deep learning, help to predict and classify diseases. The main goal of this work is to make an advanced prediction system that uses plasma lipid data and MRI images to predict the stage-wise classification of NAFLD images. The four main stages of liver disease are normal, steatosis, steatohepatitis, and cirrhosis. The machine and deep learning techniques to identify and classify cirrhosis disease in NAFLD. This research study proposes a machine and deep learning techniques with features that classify and predict the NAFLD. The random forest algorithm is used to predict the disease images into binary classifications, and a deep neural network is used for stage-wise classification. Initially, different features are used to classify the liver. This work uses 52 features, such as GLGCM and GLCM to predict and classify the fatty liver. The proposed method combined the initial prediction features using the random forest method and deep neural network to classify different stages of livers images. The experimental work is done with the help of MRI images and various iterations for evaluation. The experimental results were evaluated using three metrics, such as specificity, sensitivity, and accuracy. The predicted values of this methodology were 96.26%, 95.7%, and 97.6%. © 2024 IEEE.

关键词： Cirrhosis Hybrid Method Random Forest-Deep Neural Network Stage Wise Classification

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Automation in Monitoring Temperature, Vibration of Industry and Preventing Accident Using IoT 3

Automation in Monitoring Temperature, Vibration of Industry ...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Uma Maheswari, Sabari L Teotia, Shashiraj Kumaravel, R. Pandey, Himani Dhinakaran, K. Kumar, Ashok R.M.K. Engineering College Department of Electrical and Electronics Engineering Kavaraipettai India Swami Vivekanand Subharti University Department of Computer Application Meerut India Vivekanandha College of Engineering for Women Department of Computer Science and Technology Elayampalam India Baroda University Department of Applied Science and Humanities Vadodara India Misrimal Navajee Munoth Jain Engineering College Department of Computer Science and Engineering Chennai India Banasthali Vidyapith Department of Computer Science Rajasthan India

ISBN: (纸本)9798350336238

Industrial safety is more important in both safety of human being and machines. To ensure this the temperature of the machine is monitored continuously and if any abnormal situation arises, the machine will be isolated from the supply using IoT. Similarly, the sound and vibration inside the factory is monitored using vibration sensor and intimated to the control room if needed. The control room may be located in remote place the information will be sent to them using IoT. Vibration may induce due sudden fall of any heavy materials, braking of machine shaft, fall of objects from Crane, etc. The vibration caused by the unexpected incident will be identified by the vibration sensor provide in the factory/ Industry in various places. The sensor will alarm by siren and intimate to the operator or control room. SMS (Small Message Service) also send to the operator through GSM. © 2023 IEEE.

关键词： Accident prevention

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Distributed Machine Learning System for Intrusion Detection 2

Distributed Machine Learning System for Intrusion Detection

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Patil, Harshal Lohumi, Yogesh Tejaswini, K.M. Sudheendramouli, H.C. Parikh, Swapnil Maranan, Ramya Sri Balaji University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Sri Siddhartha Institute of Technology Department of Mathematics Tumakuru India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science and Application Mysuru India Parul University Department of Computer Science and Engineering Parul Institute of Engineering and Technology Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

As network systems expand in complexity, the necessity for effective intrusion detection grows. This study compares two distributed machine learning systems utilizing different communication topologies: Parameter Server and Point-to-Point. The former employs centralized parameter management for efficient communication, while the latter facilitates direct node-to-node communication without a central server. Using extensive network traffic data, the models are assessed on performance metrics like accuracy, scalability, and false positive rates. Both systems show promising results, with median accuracy and F1 scores around 93%. However, limitations include potential data homogeneity issues, scalability challenges particularly with the Parameter Server becoming a bottleneck, concerns over model overfitting, and high computational overhead in maintaining models across numerous nodes. Addressing these drawbacks is crucial for improving the reliability of intrusion detection systems in complex network environments. © 2024 IEEE.

关键词： Network intrusion

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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Neural Networks for Natural Language Processing

Neural Networks for Natural Language Processing

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Deshpande, Sudhindra B. Velrani, K. Sundara Bhagyalakshmi, A. Vani, M. Sowmya Ananthi, S.N. Sahu, Devika Department of Information Science and Engineering KLS Gogte Institute of Technology Udyambag Karnataka Belagavi590006 India Department of Information Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Application Mohan Babu University Andhra Pradesh Tirupati517501 India Department of Computer Science and Engineering S.A Engineering College Tamil Nadu Chennai600077 India Department of Computer Science and Engineering Government Engineering College Chhattisgarh Raipur492001 India

ISBN: (纸本)9798350374933

Artificial neural networks have made Natural Language Processing (NLP) a lot different because they can encode language data more efficiently. A lot of different natural language processing tasks, like mood analysis, translation, and text production, have been done very well with these networks are based on the neuronal structure of the human brain. This abstract delves into the fundamental principles of neural networks in natural language processing, with a particular emphasis on notable structures, including the more recent Transformer models, CNNs, and RNNs. The investigation investigates the potential future trajectories, challenges, and applications of improving language comprehension and developing assignments. © 2024 IEEE.

关键词： Natural language processing neural networks neural networks NLP recurrent neural networks RNNs

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Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics 7

Utilizing Random Forests for High-Accuracy Classification in...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Master of Computer Application Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results. © 2024 IEEE.

关键词： Random forests

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Blockchain-IoT Layered Architecture, Current Trends, Challenges, and applications 7

Blockchain-IoT Layered Architecture, Current Trends, Challen...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Kumar, Mukesh Nidhi Tiwari, Shweta Kaur, Surinder School of Computer Application Lovely Professional University Phagwara India Maharaja Agrasen University Kallujhanda Himachal Pradesh Solan India Computer Science and Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350397284

The term 'Internet of Things' (IoT) refers to the potential global connectivity of billions of individually networked devices that can collect and share data. This connectivity might occur everywhere on the globe. By combining IoT with Blockchain (BC), a robust and secure system might be created to store information gathered by intelligent gadgets. Internet-connected IoT devices can upload transaction data to private BC networks. As a result, all transactions in such networks are recorded in immutable ledgers. In order to comprehend how devices may connect with one another, this study examines the interplay between BC technology and IoTs. In this article, a BC layered architecture as a practical framework for integrating BC technology with IoT by making use of state-of-the-art tools and methods is proposed. Current trends and difficulties, as well as potential application domains, are discussed which is in relation to the layered architecture of Blockchain technology. © 2023 IEEE.

关键词： Internet of things

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A human-in-the-loop method for pulmonary nodule detection in CT scans

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Visual Intelligence 2024年第1期2卷 227-239页

作者： Qingjie Zeng Yutong Xie Zilin Lu Yong Xia National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Australian Institute for Machine Learning The University of AdelaideAdelaide SA 5000Australia

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung *** extensive well-performed methods have been proposed for this task,they suffer from the domain shift issue between training and test *** domain adaptation(UDA)methods provide a promising means to mitigate the domain variance;however,their performance is still limited since no target domain supervision is *** make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains,we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target ***,we first train a detector model on source domain,and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme(USSS)to find a few target domain samples worth annotating most and obtain their human ***,the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint(ZZC)*** evaluate our method on the Nodule Analysis 2016(LUNA16)and TianChi *** results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain.

关键词： Pulmonary nodule detection Human-in-the-loop Semi-supervised learning Domain adaptation

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Supervised and Unsupervised Coupled Generative Adversarial Networks in Multivariate Time Series 4

Supervised and Unsupervised Coupled Generative Adversarial N...

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4th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2023

作者： Wang, Qinfen Li, Yifei Xia, Yong School of Computer Science and Engineering Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China

ISBN: (纸本)9798350337181

Generative adversarial networks (GAN) have been successful in demonstrating the efficiency of generative time series. In this paper, we proposed a novel end-to-end supervised and unsupervised coupled generative adversarial networks (SUC-GAN) framework that consists of a gated recurrent unit (GRU) for generating multivariate time series. We test SUC-GAN on three datasets (i.e. Sines, Energy, Stocks) to investigate the efficacy and properties. The preliminary results show the potential power of supervised, unsupervised, and coupled learning for data generation. © 2023 IEEE.

关键词： Generative adversarial networks

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