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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K. Rama Gangi Reddy K.S. Thirunavukkarasu Department of Computer Science Vels Institute of Science Technology and Advanced Studies(VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The application of deep learning(DL approaches to the problem of identifying diseases of the sweet lemon leaf is a recent promising outcomes in many artificial intelligence (A) problems. Numerous techniques are used in machine learning (ML) models for classification and detection; however, given the ongoing progress in computer vision, it seems that deep learning (DL) research has a lot of promise for improving accuracy. The presence of diseases affecting sweet lemon leaves has a significant influence on Rayalaseema's economy, leading to lower-quality fruit production and a higher incidence of canker, greening, black spot, and other diseases. There is a shortage of research datasets and a restricted variety of pests, making it challenging to adapt research findings to agriculture in the area of sweet lemon leaf pest disease. The recommended strategy is assessed on the combined sweet lemon leaf disease picture datasets in the proposed study, which offers a method for identifying and categorizing diseases in sweet lemon leaf disease plants using deep learning and image processing. CNN, ResNet, MobileNet, and VGG16 are the models used to identify sweet lemon leaf disease. With an accuracy rate of $98.6 \%$, the suggested MobileNetV2 model outperforms the current CNN technique.

关键词： Deep learning Accuracy Biological system modeling Transfer learning Production Agriculture Topology Convolutional neural networks Diseases Resilience

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Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks 57

Human-machine Hierarchical Networks for Decision Making unde...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Quan, Chen Geng, Baocheng Han, Yunghsiang S. Varshney, Pramod K. Syracuse University Department of Electrical Engineering and Computer Science Syracuse United States University of Alabama at Birmingham Department of Computer Science Alabama United States Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9781665451819

This paper proposes a belief-updating scheme in a human-machine collaborative decision-making network to com-bat Byzantine attacks. A hierarchical framework is used to realize the network where local decisions from physical sensors act as reference decisions to improve the quality of human sensor decisions. During the decision-making process, the belief that each physical sensor is malicious is updated. The case when humans have side information available is investigated, and its impact is analyzed. Simulation results substantiate that the proposed scheme can significantly improve the quality of human sensor decisions, even when most physical sensors are malicious. Moreover, the performance of the proposed method does not necessarily depend on the knowledge of the actual fraction of malicious physical sensors. Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced. © 2023 IEEE.

关键词： Decision making

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Social Media as the Most Effective Means of Business Promotion Today with Using Social Media Advertising Technology 7th

Social Media as the Most Effective Means of Business Promoti...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Saputra, Aldo Tannaris, Ingwen Hubert, Kelby Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

Indonesia has the largest number of people that are active in social media and Indonesia is a country with the most social media users in the world. Social media in general is used to socialize (relate, both, personally, in groups and so on) between users. The users of social media in Indonesia already has reached 160 million in January 2020. and this number of social media users in Indonesia increased up to 12 million (8.1%) from April 2019 to January 2020. Firstly, social media was used by users to connect with each other through community. However, now social media is not only used as a means of virtual community for interaction but is also used as a channel for doing business. There are still many people who do not realize that social media has the advantage of being a very efficient and effective business, and many company that still confused about to choose which social media platform that suitable with their business promotion. Therefore, we want to show and tell them one of the advantages of social media in the field of promotion. Based on the surveys, there are four most popular social media in Indonesia such as YouTube, WhatsApp, Facebook, and Instagram. However, there still very few studies can be found to understand that social media is effective or not for business promotion. For this reason, this article will examine the advantages that social media have that can support business promotion. This article will utilize the questioners method to assess the important factors of social media users such as user satisfaction factors in using social media and what social media are often used by user to promote their business. The research result is expected to provide valuable guidance for user to understand about function social media for business promotion. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Economic and social effects

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Towards Mental stress Detection in University Students Based on RBF and Extreme Learning Based Approach 1

Towards Mental stress Detection in University Students Based...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Anand, Taruna Alam, Mohammad Shabbir Akila, D. Yadav, Manjushri Janardan Mavliya, Anil Kumar Saran Kumar, A. Graphic Era Deemed to be University Department of Humanities and Social Sciences Dehradun India College of Computer Science and Information Technology Jazan university Department of Computer science Jizan Saudi Arabia Saveetha College of Liberal Arts and Sciences SIMATS Department of Computer Applications Tamil Nadu Chennai India International Institute of Business Studies Department of Management Bengluru India COER College of Nursing and Paramedical Sciences Uttarkhand Roorkee India Bannari Amman Institute of Technology Computer Science and Engineering Erode India

ISBN: (纸本)9798350371802

A lot of individuals associate stressful situations with bad experiences. It is concerning that mental health issues are so common, particularly among younger people. The once carefree generation is now under a great deal of stress. Modern society's elevated stress levels are associated with an array of serious health issues, such as an increased risk of suicide, heart disease, depression, and stroke. Within the scope of our research, we are attempting to quantify the degree to which students experience internet-related anxiety in the seven days leading up to an exam. Model training, feature selection, and preprocessing are all tremendously dependent on sequencing. Images with consistent size and intensity can be achieved by removing noise and blinks during this preprocessing step. Classifier performance improvement via feature selection aims to normalize and increase accuracy by utilizing favorable attributes. Feature selection is crucial during the training of a StressNet-C-BiLSTM model. When compared to two state-of-the-art algorithms, BiLSTM and CNN, the suggested technique outperforms them. The results demonstrated a significant improvement, with an accuracy rate of 97.58%. © 2024 IEEE.

关键词： Health risks

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A Reliable Clinical Decision Support System for Posttraumatic Stress Disorder Using Functional Magnetic Resonance Imaging Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5605-5615页

作者： Bhattacharya, J. Gupta, A. Dretsch, M.N. Denney, T.S. Deshpande, G. TIET Department of Computer Science and Engineering Patiala147004 India Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Walter Reed Army Institute of Research U.S. Army Medical Research Directorate-West Joint Base Lewis McChordWA98433 United States Auburn University Auburn University Neuroimaging Center Department of Electrical and Computer Engineering AuburnAL36849 United States Alabama Advanced Imaging Consortium BirminghamAL36849 United States National Institute of Mental Health and Neurosciences Department of Psychiatry Bangalore560030 India Indian Institute of Technology Department of Heritage Science and Technology Hyderabad502284 India

In recent years, there has been an upsurge in artificial intelligence (AI) systems. These systems, along with efficient performance and predictability, also need to incorporate the power of explainability and interpretability. This can significantly aid clinical decision support by providing explainable predictions to assist clinicians. Explainability generally involves uncovering key input features important for classification. However, characterizing the uncertainty underlying the decisions of the AI system is an important aspect needed for interpreting the decisions. This is especially important in clinical decision support systems, given considerations of medical ethics such as nonmaleficence and beneficence. In this study, we develop methods for characterizing the decision certainty of machine learning (ML)-based clinical decision support systems. As an illustrative example, we introduce a framework for ML-based posttraumatic stress disorder (PTSD) diagnostic classification that classifies the subjects into pure and mixed classes. Accordingly, a clinician can have very high confidence (95% probability) about the diagnosis of a subject in a pure PTSD or combat control class. Remaining sample points for which the AI classification tool does not have very high confidence ( © 2024 IEEE.

关键词： Decision support systems

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Intelligent Energy Management Energy Storage Systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Towards Interpretable, Attention-Based Crime Forecasting

Towards Interpretable, Attention-Based Crime Forecasting

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Ma, Yujunrong Qi, Xiaowen Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering College Park United States University of Maryland Department of Criminology and Criminal Justice College Park United States Institute for Advanced Computer Studies University of Maryland Department of Electrical and Computer Engineering College Park United States

ISBN: (纸本)9798350337020

While the use of machine learning techniques in high stake fields, such as medical diagnosis and criminal justice, has been increasing in recent years, concerns have been raised regarding the lack of transparency and interpretability of the algorithms used. In this paper, we propose the use of interpretable attention-based ConvLSTM models for crime forecasting application. This approach combines the power of ConvLSTM models in capturing spatio-temporal patterns with the interpretability of attention mechanisms, allowing for the identification of key geographic areas in the input data that contribute to the prediction. We demonstrate the effectiveness of this approach through experiments on real-world crime data, showing that our model demonstrates high accuracy in crime predictions while providing insightful visualization that enhances the interpretability of prediction results. © 2023 IEEE.

关键词： ConvLSTM Interpretable machine learning Self attention

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Evaluating Patient Satisfaction Through Validated Predictive Classification Models 16

Evaluating Patient Satisfaction Through Validated Predictive...

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16th IIAI International Congress on advanced Applied Informatics, IIAI-AAI 2024

作者： Chemkomnerd, Nittaya Pannakkong, Warut Huynh, Van Nam Tanantong, Tanatorn School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa Japan Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Thammasat University Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798350377903

Patient satisfaction plays a crucial role in evaluating healthcare service quality. This study explores the field of predicting patient satisfaction in healthcare settings. The research aims to develop a predictive and validated model for patient satisfaction scores to improve patient satisfaction through simulation-optimization. Factors related to patient satisfaction were identified, including length of stay (LOS) and patient sociodemographic characteristics. A survey was conducted to collect relevant data. Several predictive modeling techniques were employed, i.e., ordinal logistic regression, decision trees, gradient boosted trees, and neural networks. The models were trained on the survey data to predict patient satisfaction scores. A simulation was then used to generate synthetic data, which was fed into the predictive models to obtain predicted satisfaction scores. The predicted scores were validated against the actual survey scores using the Mann-Whitney U test to evaluate each model's ability to represent the simulated system accurately. Findings suggest that ordinal logistic regression provides the best performance for predicting satisfaction across various LOS. The validated predictive model can be integrated into a simulation-optimization framework to identify strategies for improving patient satisfaction in healthcare systems, facilitating informed decision-makina and the allocation of resources. © 2024 IEEE.

关键词： Logistic regression

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Reconstructing the Medical Image by Autoencoder with Stochastic Processing in Neural Network 3

Reconstructing the Medical Image by Autoencoder with Stochas...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Senthil Kumar, V. Jayalakshmi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9780738146270

The working on image to perform different operation is known as image processing. Digital Image is a collection of a defined number of pixels. Each feature has a particular pixel value at a specific position. Image restoration can be considered as an image enrichment where the final image is printed with more important features that create more appealing for the spectator but it is not essential to create genuine data from the point of a scientific vision. Autoencoder is one of the techniques in artificial neural network to analyze and predict a huge volume of data with different many features. This technique has three stages namely the first one is encoding, second one is compression and third one is decoding. Denoising autoencoder is an enhanced technique for image processing to reconstruct the damaged image to original image. Denoising autoencoder techniques are applying on the medical image to get the high quality image in digital image processing. In this paper, stochastic gradient descent algorithm is applied to predict the damaged pixel and replaced by new pixel values. The result is better than previous result in medical images. © 2021 IEEE.

关键词： Restoration

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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