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TFT -DQN -A Dual Framework Model for Stock Market Forecasting

TFT -DQN -A Dual Framework Model for Stock Market Forecastin...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： A. Kalaivani V. Asha J. Sathya Rahul B Kabbin Rahul Ravi Tej Department of MCA New Horizon College of Engineering Karnataka India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The financial and economic outlook of any economy is effectively conveyed through the stock markets which in turn plays a very critical part in the global economy. Investors buy and sale stock to grow financially and earn investment returns and on the other hand, companies sell stock to gather funds. But then again, the share market is very volatile as the risk is high and there is uncertainty in it. Trends in the stock market can be accurately described as difficult tasks as the methods used in today's market are not successful enough in understanding the different aspects of how consistently changing markets work. This research seeks to build on the existing literature on stock market prediction and decision making in terms of trading by presenting a new model, Dynamic Temporal Fusion Transformers (D-TFT). The model integrates Temporal Fusion Transformers (TFT), Deep-Q- Networks (DQN) to enhance the speed and performance. Multi-step prediction is applicable due to the strong forecasting capabilities. Such strong forecasting capabilities are ideal because they enable the DQN, which is a reinforcement learning algorithm, with real time trading decision making capabilities that are influenced by the ongoing volatility of the financial market. Our findings suggest that while the TFT component exhibits strong proficiency in predicting various market trends, the DQN is able to utilize these predictions to optimize decision-making within real-time trading activities. The hybrid DTFT model achieves a Mean Absolute Percentage Error (MAPE) of 0.88%, significantly outperforming existing methods. This paper suggests a novel way of addressing additional challenges such as imprecision of predictive models, high uncertainties in stock market and improving the overall decision-making process.

关键词： Uncertainty Computational modeling Heuristic algorithms Decision making Reinforcement learning Predictive models Transformers Thin film transistors Forecasting Stock markets

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Monitoring and Leak Detection of Hazardous Gas Using Internet of Things

Monitoring and Leak Detection of Hazardous Gas Using Interne...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Adwaith.A. Nair Chidella Naga Sai Niteesha Gaurav Sharma Shankar Nalinakshan Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The compounding of smart sensors with wireless communication technologies has been yielding highly effective Internet-of-Things (IoT) solutions for monitoring hazardous gases and leak detection in industrial facilities. In this research, the authors present the limitations in traditional gas detection methods, which fail to provide real-time monitoring and scalability, further failing to provide seamless integration with safety systems. These IoT-based systems continuously monitor gas concentrations to assess the environment in real-time. The IoT solutions deploy networked sensors, allowing for the precise, large-scale tracking of gas levels with full coverage and reduced risk of undetected leaks. In addition, the integration of GPS and advanced communication technologies allows these systems to pinpoint the location of gaseous emissions accurately. This means that onsite workers can have access to pertinent data to effectively perform rectification activities. automated alarm responses and counteraction measures, such as halting production or initiating ventilation systems, further enhance the speed and efficiency of threat mitigation, reducing the impact on workers, infrastructure, and the environment. This paper explores the state-of-the-art techniques in IoT-based gas monitoring and leak detection, including the accuracy of sensors, wireless connectivity, and interfaces with cloud-based analytics and remote controls. It also addresses challenges such as sensor stability, data protection, and connection reliability. Despite these challenges, IoT-based gas monitoring remains a promising field for industries facing high risks in their production lines, with increasing demands for compliance and operational efficiency. Finally, this review offers opportunities for using machine learning to predict maintenance needs and blockchain for secure data exchange, which contributes to the growth of innovative technologies. Thus, IoT-based gas monitoring systems present a sca

关键词： Wireless communication Industries Accuracy Sensor systems Real-time systems Internet of Things Leak detection Monitoring intelligent sensors Gas detectors

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Real-Time Underwater Trash Object Detection Using Enhanced YOLOv8

Real-Time Underwater Trash Object Detection Using Enhanced Y...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Adimulam Raghuvira Pratap Thumu Rampraveen Reddy Lanke Nikhila Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The accumulation of trash in aquatic and marine environments has severe and significant consequences on marine ecosystems, resulting in a persistent environmental hazard. While volunteer trash pickup efforts have grown in recent years, they still demand considerable time, effort, and resources. To effectively address these challenges, the implementation of automated solutions is essential. This project aims to develop an efficient object detection deep learning model which helps in effectively identifying the underwater trash objects. The key challenges of underwater image-based localization come from object detection under low light conditions, also the presence of numerous small and overlapping targets, which significantly increase the complexity. In this project we have used improved Yolov8n object detection model achieving an impressive accuracy rate of mAP of 0.951. We have utilized diverse datasets covering a range of environmental conditions, including challenging low-light scenarios. Also, the integration of advanced preprocessing techniques and data augmentation has further enhanced our model’s performance, ensuring reliable predictions in various underwater environments, thereby improving detection performance without introducing additional computational complexity.

关键词： Accuracy Object detection Pressing Predictive models data augmentation Prediction algorithms Real-time systems Reliability Object recognition Environmental management

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A Deep learning Technique for Smart Gender Classification System

A Deep learning Technique for Smart Gender Classification Sy...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Vijaya Krishna Sonthi Rishi Sikka Anusha R P. Bala Shanmuga Devi Sachin K Kamble Bhasker Pant Department Of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department Of Electronics Sanskriti University Mathura Uttar Pradesh India English A. P. C. Mahalaxmi College for Women Thoothukudi India Design Terna Engineering College Navi Mumbai CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India

The examination of the possibility of employing deep learning techniques for transportation planning and modelling is the basis of this essay. The writers look at the transportation issues that have developed for urbanized populations. intelligent transportation systems will become increasingly crucial to satisfy people’s transportation demands as the urban population grows. automated data gathering devices demand the usage of analytics tools in order to give insights for actual decision-making and policy planning. This research has two objectives. This research begins with a review of academic literature on gender and age identification using Convolutional Neural Network-based approaches estimate, followed by a thorough examination of the present methodology. Then, using the classification techniques, we conduct a review of roughly 30 publications to draw valid conclusions and forecasts about how the various CNN approaches and transportation systems will develop in the *** to the existence of a complicated background, object occlusion, and varying lighting conditions, gender determination from facial photos is a difficult process. Face photos may be used for a variety of purposes, including tracking, identification, and expression analysis. Two deep learning-based approaches are examined in this research for facial image-based gender classification. Convolutional neural networks (CNN) and Alex Net are two examples of these techniques. The effectiveness of both models for identifying the male and female classes from face photos was tested through experiments. Results indicate that both approaches were successful in classifying people by gender. Additionally, a comparison between these two models and a few of the common techniques for gender categorization was done.

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Gotte Ranjith Kumar Shubham Deepak Banerjee School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns.

关键词： Hair Training Measurement Accuracy Dermatology Medical services Machine learning Predictive models Planning Reliability

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Enhancing English Language Assessment in Educational Settings using Natural Language Processing Techniques

Enhancing English Language Assessment in Educational Setting...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： V Moses Jayakumar R. Rajakumari Purnachandra Rao Alapati Santiago Otero-Potosi D. Naga Malleswari M. Karthik Department of English Saveetha School of Engineering SIMATS Chennai India Department of English Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of English Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh India Departament of Investigation Instituto Superior Tecnológico Liceo Aduanero Ibarra Ecuador Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Various approaches to testing the ENL learners in educational contexts have tended to experience some limitations concerning validity, reliability, practicality, and sustainability. These limitations can be well mitigated by NLP techniques especially in areas where automated assessment can be made and personalized feedback given to learners. This particular work will contribute to the improvement of methods for teaching and testing in the sphere of English language by employing narrow components of NLP to create the base for the automated assessment that matches the need of a learner or academical curriculum and offers accurate and comprehensive feedback. In contrast with more conventional forms of assessment that involve paper grading or multiple choice tests the proposed approach leverages NLP to ‘interpret’ the student texts for the purposes of assessment thus encompassing multiple aspects of languages than can simply be graded out of out 100. The type of data used in the present study includes datasets like the Twitter Sentiment Analysis dataset; this is a dataset that comprises labeled tweets that are used to train and test NLP models. An ideal approach, like BERT-CNN is used for feature extraction, preprocessing and training/validation to achieve higher levels of student outputs analysis. K-fold validation and parameter tuning strategies are used for effectiveness and overfitting avoidance of the proposed approaches. The proposed NLP-based assessment system will have a test accuracy of 0.99, and it will prove to be more accurate, efficient and scalable than traditional systems. The system shows its ability to fairly assess the language skills of the learners and offer the feedback to them; therefore, it underpins the potential for introducing changes in the language assessment in learning institutions. This is a major innovation in the area of English language assessment since this work provides a new approach of using NLP in education measurement. The propose

关键词： Training Sentiment analysis Accuracy Social networking (online) Feature extraction Natural language processing data models Tuning Testing Context modeling

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Real-Time Environmental Monitoring and Control for Ensuring Optimal Temperature and Humidity Conditions for Patient Comfort and Medical Equipment Integrity

Real-Time Environmental Monitoring and Control for Ensuring ...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Vinish Kumar Vishal Upmanu Rudra Pratap Singh Chauhan Neetu Sharma Bura Vijay Kumar Manish Zadoo Raj Kumar Goel Institute of Technology Ghaziabad India Meerut Institute of Engineering Technology Meerut India Shri Shankracharya Institute of Professionals Management and Technology Raipur India Galgotias University Greater Noida India SR University Warangal India ABES Engineering College Ghaziabad India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Ensuring precise environmental parameters, particularly in terms of temperature and humidity, is crucial for both patient safety and the longevity of essential medical equipment. This innovative solution utilizes IoT technologies, featuring the DHT11 sensor-equipped ESP32 micro controller, for continuous data collection and secure transmission to the Blynk platform's cloud server. The selection of the DHT11 sensor is based on its attributes, including accuracy, cost-effectiveness, precision, and reliability, for providing real-time temperature and humidity data. The intelligent control logic autonomously responds to deviations in environmental conditions, triggering corrective actions such as activating cooling, heating, or humidity control mechanisms. This real-time surveillance system empowers healthcare institutions to manage environmental conditions actively, ultimately enhancing patient comfort and safeguarding the integrity of medical equipment. The project's adaptability and scalability cater to diverse healthcare needs, contributing to an elevated standard of patient care and healthcare facility functionality. The ESP32's control logic maintains ideal conditions by continuously checking and initiating actions in response to deviations from predefined thresholds. By harnessing the potential of IoT and cloud technology, this initiative significantly improves patient well-being and enhances the overall quality of healthcare services.

关键词： Temperature sensors Biomedical equipment Cloud computing Temperature distribution Medical services Humidity Real-time systems

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automated Tooth Segmentation in X-ray Images using Attention Integrated U-Net++ Model

Automated Tooth Segmentation in X-ray Images using Attention...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Julakanti Sai Yaswanth Kanderi Johith Kumar Rimjhim Padam Singh Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Oral functioning, the ability to perform various oral functions, pain-free and without being ill, is one of the indicators of overall well-being and life satisfaction. Even though these diseases are mostly preventable, a majority of individuals all over the world suffer from periodontal disease, dental caries, and oral malignancies to an estimated of 5 billion people worldwide. However, there are still use of traditional radiographic techniques like periapical and panoramic radiography that assist in diagnosis. A better diagnosis is enhanced through the use of modern techniques in imaging such as MRI, ultrasonography and CBCT imaging. Deep learning-based solutions enhance diagnostic capabilities through dental segmentation in images since they learn from unmodified data in contrast to traditional machine learning and deep learning techniques which produce inferior results when dealing with massive datasets. Therefore, in an effort to solve the problem of efficient dental segmentation, anencoder-decoder basedtooth segmentation model based on U-Net++ architecture modified using Efficient Channel Attention Network is proposed that aims at enhancing the accuracy of dental diagnosis. The paper also focus of the performance of different state-of-art CNN techniques namely, U-Net, FCNs, ENet, U-Net++, U-Net 3+, SwiftNet on the standard Tuffs Dental dataset for model selection and comparisons. The proposed model outperforms other segmentation models and achieves the best mean average precision of 96.4% and a dice coefficient of 90%.

关键词： Deep learning Image segmentation Computational modeling Magnetic resonance imaging Ultrasonography Dentistry Convolutional neural networks Diagnostic radiography X-ray imaging Diseases

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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