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Decoding AI: Experimental Analysis of Artificial Intelligence based Wine Quality Prediction Logic using Convoluted Deep Classification Strategy 9

Decoding AI: Experimental Analysis of Artificial Intelligenc...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Geetha, C. Arunsundar, B. Vasumathi, G. Thamizhazhakan, K. Sudhakar, C. Venkata R.MK. Engineering College Department of Cse Chennai Kavarapettai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Department of Electronics And Communication Engineering Tirupati517102 India

ISBN: (纸本)9798350365092

Wine quality prediction is essential for optimizing production and ensuring consumer satisfaction. This study introduces a novel Convoluted Deep Classification (CDC) approach for accurate wine quality assessment. Leveraging convolutional neural networks (CNNs), our method effectively extracts hierarchical features from diverse wine data. The process begins with dataset preprocessing, encompassing chemical properties and sensory attributes. Subsequently, a hierarchical CNN architecture is tailored to capture intricate features such as acidity levels and volatile compounds. Techniques like data augmentation and transfer learning are employed to bolster model generalization. Evaluation on a benchmark dataset demonstrates outstanding performance, achieving a remarkable accuracy of 97%. This surpasses traditional methods and baseline CNN architectures, affirming the efficacy of our approach. Additionally, interpretability analyses unveil the key features influencing wine quality predictions, providing invaluable insights for producers and connoisseurs alike. Our CDC framework offers a robust and interpretable solution for wine quality prediction, empowering stakeholders with actionable insights for production optimization and quality assurance in the wine industry. © 2024 IEEE.

关键词： Quality assurance

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Analyzing Job Interview Performance Prediction using Graph Features and Convolutional Neural Networks

Analyzing Job Interview Performance Prediction using Graph F...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rath, Manoj Kumar Karthi, R. Roopalatha, N. Rao, Attru Hanumantha Thulasimani, T. Ali Khan, P.M.D. Kiit Deemed to Be University Bhubaneswar India E.G.S. Pillay Engineering College Department of Management Studies Nagapattinam India University Gitam School of Business Hyderabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India Mohan Babu University School of Computing Tirupati India

ISBN: (纸本)9798350375442

The psychological reactions of candidates to personnel selection are becoming more important since they significantly impact the outcome of the selection process. In this study, we created and evaluated the Multi-dimensional Job Interview Self-Efficacy Scale (MJISE). Five components of interviewees' self-efficacy form the basis of our proposed MJISE: self-promotion, dealing with difficult interviewers, interview-related anxiety, and content and practical interview *** proposed approach consists of three stages, which are preprocessing data, training the model, and extracting features. When it comes to text classification, preprocessing is king. In order to make text data useable by a classifier, it is necessary to extract a set of words from the text data and convert them into a feature set. Throughout the training process, it employed a GCNN. With a 91.30% average accuracy, our innovative approach surpasses CNN and ANN. © 2024 IEEE.

关键词： Convolutional neural networks

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A Double Diagonal Ratio Metric for Tiny Object Detection in Aerial Images

A Double Diagonal Ratio Metric for Tiny Object Detection in ...

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2023 International Conference on Computer engineering and Distance Learning, CEDL 2023

作者： Liu, Haoguang Tong, Qiang Qi, Lin Hao, Yanyan Liu, Xiulei Beijing Information Science and Technology University School of Computing Science Beijing China Beijing Information Science and Technology University School of Economics and Management Beijing China Henan Vocational College of Industry and Trade School of Information Engineering Zhengzhou China

ISBN: (纸本)9798350329032

In recent years, with the development of deep learning and the maturity of object detection technology, unprecedented progress has been made in aerial image object detection tasks. But for detecting tiny objects with little pixel information and easily confused with the background, the general-purpose detectors still do not achieve good performance. Through analysis, we found that, in addition to the defects of the tiny objects themselves, the sensitivity of the most commonly used IoU-based metric to tiny objects is one of the main reasons for the poor detection performance. Therefore, we design a new metric, Double Diagonal Ratio (DDR), which alleviates the poor performance by using a first power line segment ratio instead of a second power area ratio. Note that, DDR is simple in design and can be easily embedded into almost all detectors assignment, non-maximal suppression, and loss function modules to replace IoU. In addition, DDR does not introduce additional hyper-parameters and has stronger robustness. We evaluate our metric on the AI-TOD dataset, and extensive experimental results show that DDR significantly affects the detector performance improvement, especially on tiny objects. © 2023 IEEE.

关键词： Object detection

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Algorithmic Approaches to Securing Cloud Environments in the Realm of Cybersecurity 10

Algorithmic Approaches to Securing Cloud Environments in the...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Devipriya, A. Rosaline, R. Anto Arockia Prabhu, M. Ramkumar Nancy, P. Karthick, V. Kadumbadi, Vijayakumar Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Chengalpattu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai Thandalam India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This paper introduces a systematic methodology for enhancing cloud security in information systems by applying mathematical algorithms in cybersecurity. Using the most sophisticated computational methods, our approach strives to boost the security posture of cloud-based platforms by not only identifying but also blocking, and rectifying various cyber threats. We start with a detailed review and analysis of various algorithms, which contribute to the identification of gaps in the current research landscape and solutions already existing. We proceed by performing the requirement analysis during which we set up security targets for the cloud environment to be optimized. The second step consists of deliberation on what the best algorithms are and is involved in a process that will meet the identified requirements. We did the development work for a prototype of the security solutions described beforehand. It was integrated and tested on a cloud environment for the best KPI result. Performance metrics, including detection accuracy (98.5%), false positive rate (0.2%), response time (25 milliseconds), and resource utilization (CPU: The JSON-LD, Microformats, and MAP-trees (10%, 30%, and 40%) algorithms were assessed to determine the functionality of the algorithms. Thus, the security effectiveness values will be obtained in the following way: malware detection (99%) - intrusion detection (97%) - data breach prevention (95%) and insider threat detection (98%) will be determined through comprehensive testing with numerous cyber threats. One more thing I would like to mention is the scalability parameter of the security solutions they function both for 1,000 concurrent users and handle 1TB of data per day and 1,000 requests per second. The team calculated the scalability which was found out to be 95% which is over 90% thus highly scalable to meet the growing demand. Besides, user satisfaction ratings were obtained which were;(4.5), (4.3), (4.7) and (4.6) for usability, reliabi

关键词： Cybersecurity

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U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate TB Lesion Segmentation and Classification 8

U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthikeyan, M. Saravana Raj, J. Relin Francis Parvathi, R. Anushya, S. Thanga Krishnan, R. Santhana Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India SaveethaInstitute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Department of Smart Computing Vellore India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350376425

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. This Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including DenseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. The attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. This progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis. © 2024 IEEE.

关键词： Medical imaging

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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Sum Rate Maximization in Cooperative Non-Orthogonal Multiple Access Based 5G Wireless Networks 3

Sum Rate Maximization in Cooperative Non-Orthogonal Multiple...

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3rd IEEE International Conference on Recent Advances in Systems Science and engineering, RASSE 2023

作者： Aswathi, V.A. Rahul Manohar, O. Ranjan, Priyesh School of Electronics Engineering Vellore Institute of Technology Vellore India Christ College of Engineering Department of Electronics and Communication Engineering Kerala Irinjalakuda India Health Informatics Centre for Development of Advanced Computing Noida India

ISBN: (纸本)9798350341676

This paper addresses a coordinated direct and relay transmission (CDRT) network with a downlink non-orthogonal multiple access (NOMA) over Rayleigh fading channels. A Coordinated Direct and Relay Transmission network consists of a base station (BS), two downlink users (one near user and one far user or cell edge user), and a relay. The near user will receive its intended message directly from the BS, whereas the far user or cell edge user will access and be supported by a full-duplex (FD) decode and forward (DF) relay to receive its message. Since the paper considers a NOMA network, the near user will be allocated with a lower power and the far user will be allocated with a higher power to ensure user fairness. The first section of this study discusses the system model, signal models and associated signal-To-interference plus noise ratios (SINRs). The joint optimisation problem for maximising the minimum sum rate is defined in the second half. Mathematical analysis is used to generate the joint power allocation factor and the results of both optimal and non-optimal conditions are plotted using Monte-Carlo simulations. Through various analyses, this paper establishes that using optimal power allocation factors can significantly improve the system's performance. © 2023 IEEE.

关键词： Wireless networks

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Leveraging NLP and Quantum computing for Advanced Agricultural Solutions 8

Leveraging NLP and Quantum Computing for Advanced Agricultur...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Bhargavi, G. Das, Manisha Banumathi, S. Malarvizhi, E. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Andhrapradesh Rajam India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Avadi Chennai India St.Joseph's College of Engineering Department of Ece Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Quantum computing and natural language processing (NLP) could transform smart farming. Decision-making and resource management improve with convergence, enabling new agricultural solutions. This technique solves traditional computing's problems with large datasets and complex language processing, making it crucial for real-time decision-making. Problems include complex quantum algorithms, NLP models in quantum frameworks, and quantum hardware. This study presents Smart Agriculture computing based on Quantum Natural Language Processing (SAC-QNLP) to overcome the aforesaid challenges. Agricultural data systems use quantum computing, data flow, and quantum-enhanced NLP algorithms. Quantum computing assesses large, complicated datasets faster than standard methods. Thus, smart agricultural strategy apps learn more and forecast better. Increased production projections from genetic and environmental factors SAC-QNLP could change farming. A simulation evaluated SAC-QNLP's performance and scalability. Older methods use more resources, produce less precise data, and are less efficient. The results show SAC-QNLP could boost farming production and sustainability. © 2024 IEEE.

关键词： Large datasets

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Design and Development of an Augmented Reality E-Commerce 3D models 3

Design and Development of an Augmented Reality E-Commerce 3D...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Nandhini, P. Hariprabha, S. Abirami, S. Jaseenash, R. Kaviyaraj, R. Sri Shanmugha College of Engineering and Technology Department of Computer Science and Engineering salem India Srm Institute of Science and Technology School of Computing Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The world is becoming more modernized with augmented reality, and this paper proposes an idea for a new way to improve the user experience of all e-commerce platforms. This strategy makes use of Transient Chaotic Neural Networks (TCNN) to develop a new marker-less visual tracking system that makes use of pictorial information. It is made up of point correspondences between image sequences. The purpose of this is to provide the easy visualization of the dimensional importance of an object in the perspective of the user's own near environment to facilitate the making of accurate purchase decisions. This is accomplished by using Vuforia to create a 3D representation of the target object, Unity3D and C# to serve as the application's framework, and Wikitude for tracking the surrounding environment. The outcomes of the experiments provide evidence of both the effectiveness and efficiency of the proposed model. © 2022 IEEE.

关键词： Augmented reality

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A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Husain, Saif O. Meghana, A. Soujanya, Rajireddy Sunil, G. Reddy Muvva, Vijaya Bhaskar The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman

ISBN: (纸本)9798350375442

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification. © 2024 IEEE.

关键词： Median filters

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