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

作者： Bhanujyothi, H.C. Jacob, I Jeena GITAM Deemed to be University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356816

Stock market prediction has become a challenging task in today's world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model. © 2024 IEEE.

关键词： Convolutional neural networks

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chugh, Bharti Kumar, Pushepndra Mishra, Upendra Chaudhary, Himanshi Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

This research paper introduces an innovative approach to optimize livestock surveillance by employing YOLOv8 for cattle body segmentation. The study attained a mAP(50), mean Average Precision of 0.598 and 0.464 and mAP(50-95) on a dataset comprising 704 images. Preceding model training, extensive preprocessing and image augmentation techniques were implemented, resulting in a dataset of 1476 training, 171 validation, and 71 testing images. The developed model demonstrates considerable promise in enabling real-time mon-itoring of cattle and their health status. Additionally, to enhance its suitability for edge computing environments, quantization-aware training (QAT) abd weight compression techniques were employed, ensuring efficient deployment on resource-constrained devices. The results highlight the effectiveness and practicality of employing YOLOv8-based segmentation for livestock surveillance, with notable implications for precision agriculture and animal welfare. However, further enhancements are warranted, particularly in achieving higher mAP scores across a broader range of Intersection over Union (IoU) thresholds, to fully address the segmentation challenges in this domain. © 2024 IEEE.

关键词： computer vision

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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Personalized Video Prefetching Mechanism Based on User Viewing History in Edge Computing Environment

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Transactions of the Korean Institute of Electrical Engineers 2023年第5期72卷 620-627页

作者： Moon, YangChan Lim, Mingyu Dept. of Smart ICT Convergence Konkuk University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

The personalized video service collects and analyzes user information to provide differentiated services for each user. Additionally, it is impossible to avoid delays when sending a video from a server to a client. However, if prefetching is performed at the edge node, transmission delay can be reduced, and personalized service can be provided by utilizing user information at each edge node. In this paper, we propose a personalized video service based on edge nodes. The proposed method operates so that the edge node performs personalized prefetching in which the number of prefetching is adjusted by calculating the weights of five selection criteria based on the user's viewing record. We found that the proposed method has the highest prefetching accuracy through implementation and experiments using the simulation of user behavior of selecting and viewing videos. © 2023 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Edge computing

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PassChain: A Blockchain-Based Decentralized Password Manager 3

PassChain: A Blockchain-Based Decentralized Password Manager

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Anik, Nur Adnan Chowdhury Hossain, Md. Sabbir Rahman, Habibur Rumpa, Ummay Khadiza Datta, Joy Nawaz, Rifat Karmoker, Sagar Khan, Md. Adib Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

A Nobel approach to the password management system is introduced in this paper, which is through a decentralized system named blockchain. Our goal is to secure people's passwords by providing them with a secure and distributed password management system named "PassChain."In today's digital world, a secure password management system is crucial, but it is still challenging. The traditional system lacks security and is a centralized system, so trust issues also arise. Our proposed blockchain-based solution offers full control over users' passwords. Because of the decentralized method, they are not stored on a single server. Decentralized servers are also more secure than databases. This approach will ensure user security and privacy and will also reduce dependency on third-party services that are prone to vulnerabilities. © 2024 IEEE.

关键词： Decentralized systems

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'Liver Disease Risk Prediction Using Regularized Deep Learning: A Novel Approach' 2

'Liver Disease Risk Prediction Using Regularized Deep Learni...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350390179

Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy-Liver, Low-Risk, Moderate-Risk, High-Risk, and Severe-Liver-Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model's scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings. © 2024 IEEE.

关键词： Prediction models

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Enhancing Agricultural Quality Control: Freshness Detection in Root Vegetables Through Deep Neural Networks 15

Enhancing Agricultural Quality Control: Freshness Detection ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shorna, Aklema Akther Ave, Abida Sultana Mahi, Hasibul Hasan Chowdhury Shital, Sabrina Bari Sowrna, Nourin Jahan Nahid, Aynul Hasan Daffodil International University Dept. computer science and engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

In monarchy agricultural technology, this research pioneers the classification of freshness in root vegetables, a field that has been abruptly neglected in earlier research on a variety of vegetables. This study addresses this research gap and employs a transfer learning-based system to swiftly determine the freshness of root vegetables through images. To conduct this research, we create our own dataset which contains six different vegetables, 'Potato', 'Radish', 'Carrot', 'Onion', 'Taro', and 'Turnip'. Vegetables were collected from the local market and clicked manually by personal mobile phone, giving the dataset authenticity and real-world application. In total we collect 6384 images, divided into 3 classes: 'Fresh', 'Aged' and 'Rotten' based on their physical attributes and texture. Different types of transfer learning model VGG19, ResNet50, InceptionResNetV2 and DenseNet201 used to classify freshness of root vegetables. Although every model achieved decent accuracy, DenseNet201 achieved a remarkable result. DenseNet201achived highest accuracy 98.75% where data loss was 0.02%. Conversely, though ResNet50 achieved lowest accuracy 60.97% among all models. The variety of algorithms used seeks to determine the best course of action for this particular agricultural setting in addition to precisely classifying freshness. This research not only defines a novel benchmark but also sets the stage for future explorations in agricultural technology, ushering in a new era of precision farming for root vegetables © 2024 IEEE.

关键词： classification deep neural network freshness Root vegetable transfer learning

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Enhancing Supply Chain Efficiency through IoT Integration: A Detailed Examination of the Chances and Difficulties 2

Enhancing Supply Chain Efficiency through IoT Integration: A...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Mehra, Chaitanya Sharma, Shaifali Arora, Jayant Sharma, Aakanksha Mahajan, Pranaya Manhotra, Aryan Dept. of Computer Science Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798331541217

The adoption of Internet of Things (IoT) technology in supply chain management has brought about noteworthy transformations, boosting productivity, visibility, and evidence- based decision-making. This research investigates the primary reasons for implementing IoT in contemporary supply chains, tackling issues such as limited real-time monitoring, suboptimal inventory control, and the necessity for preemptive risk manage- ment. The investigation delves into the capabilities of IoT-driven solutions, including sensor technology, RFID systems, and data analysis tools, to confront these obstacles by enabling constant surveillance and anticipatory maintenance, taking into account the impact of innovative technologies such as blockchain and artificial intelligence. The results underscore the considerable effect of IoT in establishing robust, flexible, and eco-friendly supply chains, providing valuable insights for businesses aiming to improve their operational effectiveness in an increasingly digital landscape. © 2024 IEEE.

关键词： Inventory control

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Image Captioning: Analyzing CNN-LSTM and Vision-GPT Models 9

Image Captioning: Analyzing CNN-LSTM and Vision-GPT Models

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Karthik, Abburi Sai Karthik, Maddala H. S. M. Krishna Yashwanth, Samudrala Anjali, T. Amrita Vishwa Vidyapeetham Dept. of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350394474

Image captioning, which exists at the point of intersection of computer vision and natural language processing, is essential for enhancing image comprehension, allowing applications like content discovery, visual aid for the blind, and more. The hunt for more precise and reliable picture captioning models continues to be an important research goal as technology develops quickly. The two prominent image captioning techniques used in this study Image Captioning Using LSTM+CNN and Image Captioning Using VisionGPT2 are thoroughly compared. We examine these models' internal workings, assess their effectiveness, and offer insights into their advantages and disadvantages for diverse application *** neural networks (CNNs) for extracting visual features and long short-term memory (LSTM) networks for producing sequential language are combined in the LSTM+CNN model, a tried-and-true methodology. It has shown adept.in creating insightful descriptions for a variety of photographs. On the other hand, VisionGPT2, a GPT-2 architectural extension, makes use of transformers and pretrained language models to provide cutting-edge outcomes in a range of natural language processing applications. We analyze the viability of each technique by taking into account elements like model complexity, training data needs, and deployment simplicity. This comprehensive comparison enlightens academics, programmers, and businesses on the ideal picture captioning solution for their particular requirements, fostering development in this area and its numerous uses. © 2024 IEEE.

关键词： Image enhancement

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Enhanced Security Measures for Image Privacy: Encryption Perspectives 2

Enhanced Security Measures for Image Privacy: Encryption Per...

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

作者： Omshith, R. Balaji, Vemisetti Pavan Rahul, Rayapudi Venkata Kumaran, U. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. Using a variety of cryptographic techniques and algorithms, the original image is transformed into an encrypted format throughout this process, making it more difficult for unauthorized users to view or understand the content. Secure communication, picture sharing, medical imaging, and many more fields requiring the protection of sensitive visual data find widespread use in image encryption. A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. It also discusses the difficulties and potential paths for picture encryption, highlighting the rising demand for reliable, effective, and secure image encryption solutions in a world that is becoming more and more networked and data-driven. Advanced Encryption Standard (AES) is more secure than (Exclusive OR) XOR, Fernet. © 2024 IEEE.

关键词： Advanced Encryption Standard Confidentiality Encryption Exclusive OR (XOR) Fernet Integrity

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