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IntelliFarmAssist – A Novel Machine Learning integrated Genetic Algorithm Based Optimal Crop Recommendation System 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Mala, D. Jeya Basak, Swetaswa Reynold, A. Pradeep School of Computer Science and Engineering Vellore Institute of Technology Chennai India ASET College of Science and Technology Chennai India

ISBN: (数字)9783031648502

ISBN: (纸本)9783031648496

Crop recommendation is a critical task for agricultural productivity, and weather and soil conditions play a significant role in determining the suitable crop to grow. In this research, an intelligence assisted approach is used to develop a crop recommendation system that takes into account the weather and soil content of a particular region. The system uses weather and soil data to create objects that represent the conditions, and then applies a set of rules used as the fitness criteria in the proposed Genetic Algorithm to determine the optimal crop for the given conditions. The approach allows for flexible and modular design, making it easy to modify and extend the system. The results demonstrate the effectiveness of the proposed system in providing accurate and timely crop recommendations, which can help farmers to make informed decisions and improve their yield. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart city

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Vehicle Accident Detection and Reporting System using Internet of Things 5

Vehicle Accident Detection and Reporting System using Intern...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Goyal, Atul Gupta, Priyansh Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350379945

The rising number of vehicles on the road has led to a concerning increase in accidents, as reported by the Indian Government's Ministry of Road Transport and Highways. In many cases, prompt medical assistance can save lives. While numerous solutions exist to address this issue, most are limited to two-wheelers. In contrast, this research study presents a vehicle-independent solution that employs sensors to detect accidents in any vehicle type, making it highly effective and cost-efficient. The proposed model automatically identifies and report accidents in real-time, enhancing the road safety. It utilizes advanced sensors and communication technology to promptly notify emergency services and relevant authorities for timely response and assistance. © 2024 IEEE.

关键词： Vehicle detection

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User Interface and User Experience Design Methodologies and Evaluation Techniques: A Systematic Literature Review 1

User Interface and User Experience Design Methodologies and ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Gaurav Chakraverti, Ashish Kumar Mehta, Shruti N. Sharda University Department of Computer Science & Engineering School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350356816

Effective User Interface (UI) and User Experience (UX) design are essential for digital applications, crucial for enhancing user interactions and engagement. Developing a high-quality UI is now a key aspect of software development. This study presents a comprehensive literature review of recent research on UI and UX design methodologies for digital products. It serves as the preliminary findings for an ongoing research project aimed at using this knowledge to develop a mobile game. The qualitative analysis insights are expected to significantly improve user interaction and experience in digital applications. © 2024 IEEE.

关键词： User centered design

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Enhanced ELMAN Neural Network Learning Using Sine Cosine Optimization 21

Enhanced ELMAN Neural Network Learning Using Sine Cosine Opt...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Iqtidar, Ali Jianping, Li Pervez, Fatima Narejo, Attaullah Luo, Jiaheng Yeasin, Arafat Saboor, Abdus Yulu, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan611 731 China Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9798331519254

This paper explores an enhanced learning model of the Elman Neural Network with a view of addressing problems of local minima and slow convergence time by using the sine cosine algorithm. Elman Neural Network that is a sort of Artificial Neural Networks is also widely applied to most classification tasks due to its simplicity and effectiveness. However, it faces challenges like;it is slower in learning and when performing an optimization process it gets trapped in local Optima. The research presents a SCA-ElmanNN model which incorporates of exploration and exploitation aspect of sine cosine algorithm to improve Elman Neural Network performance. Using Thyroid dataset, the study examines the effectiveness of the proposed hybrid model with other models for the comparison purpose, including SCA-BPNN and SCA-ANN. The results clarify that upgraded SCA-ElmanNN model enhances the accuracy criteria and minimizes mean square error in both training and testing sets. The training set of Thyroid dataset had an accuracy of 99.9587% and mean square error of 0.000473411 with SCA-ElmanNN, outperforming all the assessed models. Thus, some amount of enhancement has been visible in the Thyroid dataset. This study shows that an integration of SCA with ElmanNN results to improved classification performance and highlights the applicability of meta-heuristic algorithms like Sine Cosine in handling of optimization problems within neural networks. The findings inform the future research on the integration of intelligent optimization algorithms for training artificial neural networks. © 2024 IEEE.

关键词： Elman neural networks

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REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction 4th

REED-NET: Residual Enhanced Encoder-Decoder Network for Low...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Jotwani, Phalit Gothwal, Ritu Tiwari, Shailendra Computer Science and Engineering Department Thapar Institute of Engineering & Technology Punjab Patiala147004 India

ISBN: (纸本)9789819769919

Increased radiation exposure to the patient is one of the most significant disadvantages of high-dose CT. The ionizing radiation used in CT scans entails the potential risk of damaging DNA and increasing the risk of cancer, especially in the patients those who undergo multiple CT scans. Deep learning (DL) is a useful method for reducing dosage in medical imaging. This is accomplished by optimizing image quality while employing reduced radiation doses. Deep learning models can successfully denoise images, remove artifacts, and improve spatial resolution, allowing images to be acquired at lower dosages without sacrificing diagnostic quality. In the proposed method encoders, decoder networks are combined together and shortcut connections are used to construct the Residual Enhanced Encoder-Decoder CNN (REED-NET) for low-dose CT imaging. We have used the Gaussian Error Linear Unit (GeLU) as the activation function. Training has been performed by using the patch-based approach, our proposed REED-NET has outperformed when compared with the traditional techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computerized tomography

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Multimodal Sentimental Analysis For Real-Time Monitoring of Work Stress Levels 6

Multimodal Sentimental Analysis For Real-Time Monitoring of ...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Singh, Arpit Kumar Shukla, Anubhav Aggarwal, Gunjan Upadhyay, Prashant Department of Computer Science Engineering School of Engineering & Technology Sharda University Greater Noida India

ISBN: (纸本)9798350374629

Stress at work is a widespread issue that affects both employee well-being and organizational productivity. Conventional techniques for evaluating stress frequently depend on subjective self-reporting, which provides little information on changes in stress levels in real-time. As a result, the incorporation of multimodal sentiment analysis has become a viable strategy for the ongoing, impartial tracking of stress at work. This survey study offers a thorough overview and comparison of previous studies on multimodal sentiment analysis for measuring job stress in real time. This literature review delves into the elements of multimodal sentiment analysis, comparing and contrasting it with conventional stress assessment techniques and scrutinizes the many methodologies utilized in recent *** in-depth examination of the techniques and performance measures used in these studies provides valuable information on the advantages and disadvantages of various strategies. Research future directions are discussed in light of identified obstacles, such as sample size restrictions and applicability in varied work situations. The study closes with a summary of the most important discoveries, a discussion of the practical ramifications, and suggestions for furthering the topic. This comparative assessment paves the way for future research in this crucial area while also deepening our grasp of the state-of-the-art in multimodal sentiment analysis for real-time job stress monitoring. ©2024 IEEE.

关键词： Sentiment analysis

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Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signals

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International Journal of Information technology (Singapore) 2024年 1-13页

作者： Misgar, Muzafar Mehraj Bhatia, M.P.S. Department of Computer Science & amp Engineering Netaji Subhas University of Technology New Delhi India

Mental health is an integral component of overall well-being, profoundly influencing the lives of individuals, families, and communities worldwide. As our understanding of mental health has evolved, so too has our awareness of the myriad conditions that impact it. Attention-Deficit Hyperactivity Disorder (ADHD) emerges as a substantial and intricate mental health concern, classified as a neurodevelopmental disorder with a pronounced impact on an individual’s capacity to concentrate, manage impulses, and modulate their activity levels. That is why the actigraphy measurements from wrist actigraphy provide a non-invasive monitoring method and diagnosis too. This paper proposes a methodology employing dimensionality reduction for feature extraction from time series actigraphy data signals and Machine learning (ML) classifiers for classifying control subjects from ADHD patients. Three dimensionality reduction techniques UMAP (Uniform Manifold Approximation and Projection), t-SNE (t-Distributed Stochastic Neighbour Embedding), and PCA (Principal Component Analysis) were applied to the actigraphy sample of length 1440 (24-h recordings). A Set of 10 ML classifiers (Medium Tree, Efficient Logistic Regression, Gaussian Naïve Bayes, Quadratic SVM, Cubic SVM, Fine kNN, Ensemble Boosting, Ensemble bagging, Medium NN, and Kernel SVM) were trained on three reduced feature sets. On UMAP features Cubic SVM outshines with remarkable accuracy of 99.2%, on t-SNE features Medium Tree classifier achieves an impressive accuracy of 98.3%, and on PCA. The Medium Tree classifier stands out with exceptional accuracy at 99.7%. Quadratic SVM shows Consistent Performance by maintaining the highest accuracy across UMAP (97.5), t-SNE (89.9), and PCA (98.8). This research contributes to advancing our understanding of ADHD through objective, data-driven methodologies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ADHD Dimensionality Reduction Machine Learning Motor Activity

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The Vulnerabilities and Risks of Implementing Internet of Things (IoT) in Cyber Security 11

The Vulnerabilities and Risks of Implementing Internet of Th...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Cambosuela, Luis Kaur, Mandeep Astya, Rani School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350350357

The Internet of Things (IoT) refers to the growing network of connected physical objects embedded with sensors, software and connectivity. While IoT has potential benefits, it also introduces new cyber security risks. This paper provides an overview of IoT security issues, vulnerabilities, threats, and mitigation strategies. The key vulnerabilities arising from IoT's scale, ubiquity and connectivity include inadequate authentication, lack of encryption, poor software security, and privacy concerns. Common attacks against IoT devices and networks include denial of service, ransom-ware, man-in-the-middle, and spoofing. An analysis of recent literature highlights emerging attack trends like swarm-based DDoS, IoT botnets, and automated large-scale exploits. Recommended techniques to secure IoT include building security into architecture and design, access control, cryptography, regular patching and upgrades, activity monitoring, incident response plans, and end-user education. Future technologies like blockchain, AI-enabled defense, and post-quantum cryptography can help strengthen IoT security. Additional focus areas include shared threat intelligence, security testing, certification programs, international standards and collaboration between industry, government and academia. A robust multilayered defense combining preventive and detective controls is required to combat rising IoT threats. This paper provides a comprehensive overview of the IoT security landscape and identifies areas for continued research and development. © 2024 IEEE.

关键词： Cybersecurity

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Navigating the Landscape of Intelligent Systems, Smart Technologies, and User-Centric Evolution 2

Navigating the Landscape of Intelligent Systems, Smart Techn...

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

作者： Komatireddy, Sanjay Reddy Krishna Bhargavi, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

Human-computer Interaction (HCI) is a dynamic field pi votal in shaping human-technology relationships. HCI is a multidisciplinary field, essential in understanding and enhancing the interaction between humans and technology. Challenges within HCI encompass design, usability, accessibility, and emotional resonance. The aim of this research study is to highlight the multifaceted nature of HCI, offering insights into its transformative and complementing potential and its continuous influence on the digital landscape. By delving into the challenges faced and the current technologies (Multimodal Affect Recognition, Artificial Intelligence, etc.), this review serves as a foundation for further research and development in this vital domain, offering valuable insights into the dynamic HCI landscape. © 2024 IEEE.

关键词： Affect Recognition Human-computer Interaction (HCI) Multimodal Interaction Personalization Psychology Usability Challenges

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Towards Privacy Protection: A Non-Invasive Human Detection Method via Edge-Cloud Collaboration 10

Towards Privacy Protection: A Non-Invasive Human Detection M...

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Yin, Luxiu Huang, Qihui Wang, Xuanya Liang, Wei Wang, Chun Hunan University of Science and Technology College of Computer Science and Engineering China Hunan Normal University College of Information Science and Engineering China

ISBN: (纸本)9798350389524

Traditional human detection technologies, such as those based on computer vision or wearable devices, often require the storage and analysis of vast amounts of personal data in the cloud, which increases the risk of data breaches. Therefore, the rapid processing of large-scale detection data while protecting user privacy has become a critical issue that needs urgent resolution. The approaches based on wireless sensing will achieve privacy protection, non-contact perception, massively scalable, and long-range detection. In this paper, we propose an innovative solution that combines data collection from terminal devices, real-time processing by edge computing, and deep analysis capabilities of cloud computing, to realize efficient and privacy-friendly human detection. First, we propose a non-invasive human detection method based on edge-cloud collaboration, integrating wireless sensing, data transmission, and processing within edge nodes, and utilizing drones equipped with edge nodes to process a large amount of wireless sensing data in real-time. Secondly, we designed a human presence prediction module based on a lightweight convolutional neural network to analyze signal data and achieve non-invasive human detection. Finally, with commodity software defined radio devices, we implement a non-invasive human detection prototype that can accurately differentiate multiple states of targets while protecting their privacy. Experimental results indicate that the proposed solution effectively reduces the risk of privacy leakage while maintaining a high detection accuracy. This provides strong support for the application of non-invasive personnel detection technology in the aspect of privacy protection. © 2024 IEEE.

关键词： Digital storage

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