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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Swetha, Kora Kumari, E.N. Vijaya Reddy, V. Adinarayana Gupta, K.Gurnadha Mlr Institute of Technology Computer Science and Engineering India Ece Ksrm College of Engineering Kadapa India Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350325799

Image classification is a procedure that makes an effort to understand an image in its entirety. The objective is to label the picture so that it may be filed away in the appropriate category. Image data that has been collected typically suffers from degradation due to opaque means such as haze, smoke, fog, rain, or snow. De-weathering is the procedure that removes the effects of weathering from an image so that it can be used again. This is done so that similar issues do not arise. As a result, the preprocessing phase is required to concurrently evaluate the quality and other characteristics of an input image, and then additionally execute de-weathering (for example, removals of haze, rain, or snow). These checks and removals must take place in sequence. In this research, an idea for a deep learning-based weather image recognition model is presented. The model takes into account 11 various weather situations, including dew, fog/smog, frost, glazing, hail, lightning, rain, rainbow, rime, snow, and sandstorm. In addition, the paper gives an idea for a deep learning-based weather image recognition model. © 2023 IEEE.

关键词： Weather forecasting

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Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry 4

Advancing Image Security Through Deep Learning and Cryptogra...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumar, Prajwal Laroia, Ankur Kumar, Mehul Laroia, Avi Upreti, Kamal Parashar, Jyoti Maharshi Dayanand University Computer Science Engineering Haryana India University of Texas at Dallas Ms in Business Analytics TX United States Amity University Computer Science Engineering Uttar Pradesh India Birla Institute of Technology Civil Engineering Pilani India Christ University Dept. of Computer Science Delhi NCR Campus India Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350353266

Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. © 2024 IEEE.

关键词： Electronic health record

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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N-gram Features and Machine Learning Models for Recognizing Human Enzyme Protein Sequences 4

N-gram Features and Machine Learning Models for Recognizing ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mehta, Disha M. Prajapati, Rashmin B. Upadhyay, Keyur N. Valani, Nisha S. Soni, Neha R. Patel, Shrina Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering Gujarat Vasad India

ISBN: (纸本)9798350300093

The identification of enzyme proteins is crucial for understanding the underlying mechanisms of various biological processes. In recent years, the development of machine learning techniques has made it possible to automate this task with high accuracy. In this study, we propose a method that uses 3-gram features and machine learning models for recognizing human enzyme protein sequences. First, the 3-gram features are extracted from the amino acid sequences of enzymes and use these features as input to several machine learning models, including naive bayes, decision tree, random forest, and Extra tree (ET). We compare the performance of these models using metrics such as accuracy, precision, recall, and F1 score. The results obtained show that the ET model performs the best, achieving an accuracy of 96%, precision of 93%, recall of 93%, and F1 score of 93%. Overall, the proposed approach demonstrates the potential of using machine learning models and 3-gram features for accurate identification of human enzyme protein sequences. © 2023 IEEE.

关键词： Decision trees

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A Deep Learning based Bangladeshi Vehicle Classification using Fine-Tuned Multi-class Vehicle Image Network (MVINet) Model

A Deep Learning based Bangladeshi Vehicle Classification usi...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Ahmed, Tanvir Sharmin Mou, Farzana Samad, Abu Anas Ibn. Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Dhaka Bangladesh University of Birmingham Dept. of Computer Science Birmingham United Kingdom Computer Vision Engineering Lab Sigmind.ai Limited Dhaka Bangladesh

ISBN: (纸本)9798350316001

The classification of vehicles is an important task in many applications, including traffic management, surveillance, and law enforcement. In Bangladesh, vehicle classification has become increasingly challenging due to the rapid growth in the number of vehicles on the roads. In this study, a novel deep learning-based Bangladeshi vehicle classification model using fine-tuned Multi-class Vehicle Image Network (MVINet) based on DenseNet201 with four added layers has been proposed. Our approach uses convolutional neural networks (CNNs) to extract features from images of vehicles. To make our dataset, we employ a YOLO (You Only Look Once) model for initial vehicle detection, followed by manual filtering and augmentation of the dataset to improve classification accuracy. The proposed method demonstrates superior performance compared to manual data collection methods. We evaluated our approach using standard metrics such as accuracy, precision, recall, and F1 score. Our model achieved an accuracy of 97.06%, precision of 97.17%, recall of 97.24%, and F1-score of 97.12% indicating high performance in classifying vehicles. We also compared our approach with state-of-the-art techniques and found that the proposed deep learning-based approach outperformed them significantly. The proposed approach has the potential to improve traffic management and law enforcement in Bangladesh. It can be used to monitor traffic flow, detect traffic violations, and identify stolen vehicles. Overall, this study demonstrates the effectiveness of deep learning in Bangladeshi vehicle detection and classification and highlights the importance of using advanced technologies to address the challenges of a rapidly changing transportation landscape. © 2023 IEEE.

关键词： Deep learning

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CrimeVerse: Exploring Crime Scene through Virtual Reality 6

CrimeVerse: Exploring Crime Scene through Virtual Reality

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Deshmukh, Jay Shetty, Srishti Waingankar, Mrunmayee Mahajan, Gauri Joseph, Richard Dept. of Computer Engineering Vivekanand Education Society's Institute of Technology Mumbai India

ISBN: (纸本)9798350398496

Modern technologies are applied in order to enhance the caliber of crime investigations. The documentation of crime scenes plays a crucial role in the investigation and prosecution of criminal cases. The challenges faced by traditional methods of crime scene documentation such as photographs, sketches, and written notes have their limitations in terms of accuracy, completeness, limited accessibility, and the risk of losing crucial evidence. The precise scene recording offered by 3D data capture techniques is free from the spatial distortion effects present in 2D photographic recordings. This research study explores the use of LIDAR scanning and virtual reality in enhancing crime scene documentation. The paper focuses on evaluating the effectiveness of LIDAR scanning and VR walkthroughs and also focuses on evidence tagging for enhancing crime scene documentation. It also helps in investigating the usability of the VR system for law enforcement professionals. The proposed model faces several challenges during its development, including the high cost and technical complexity of LIDAR scanning, the need for efficient data processing and visualization, and the limited accessibility of the VR system due to hardware and software requirements. Through user testing and analysis, the project demonstrated significant improvements in the accuracy and completeness of crime scene documentation, providing a more detailed and immersive experience for investigators. The VR system was found to be user-friendly and effective in aiding decision-making and communication among law enforcement professionals. Overall, the project provides valuable insights which let you see through the eyes of a witness by documenting their perspective, which aids in the investigation and comprehension of the incident. They can also be used as proof in court. © 2023 IEEE.

关键词： Object detection

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P2P-assisted distributed-server content delivery of 8K media : a DLT-based approach

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Multimedia Tools and Applications 2024年 1-38页

作者： Barlas, Gerassimos El-Fakih, Khaled Hiromori, Akihito Yamaguchi, Hirozumi Department of Electrical and Computer Engineering American University in Dubai Dubai Dubai United Arab Emirates Computer Science & amp Engineering Dept American University of Sharjah Sharjah Sharjah26666 United Arab Emirates Graduate School of Information Science and Technology Osaka University Osaka Japan

In this paper we study the problem of multi-source, P2P-assisted, multimedia content delivery of 8K content. Such media constitute a big challenge for contemporary VoD services, as their media rate can surpass the clients’ upload/download speeds. Our proposed methods tackle this shortcoming while also allowing a reduction in the server resource consumption. Two methods are presented and evaluated, that include global and local multi-installment schemes, that are shown to surpass server-only or non-DLT-based content distribution by a fair margin, in all the significant metrics. Our methods can either offer a significant improvement in access time or a boost in the number of clients served, while at the same time reducing the consumed server resources. The performance of the proposed methods is evaluated with a rigorous simulation study based on the current state of the Internet on two geographic locations that represent best-case and average-case scenarios. Our findings suggest that the methodology used to assign the content parts to individual sources, has a big influence of the consumption of server resources, the system’s scalability and service quality. One of the proposed methods (LMI) achieves a maximum of 70.5% improvement on the average client access time, while saving up to 23% of server resources. Based on the system settings the reduced access-time/faster access to content can be balanced against an increase in the served clients by up to 25%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Multimedia systems

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3D AttU-NET for Brain Tumor Segmentation with a Novel Loss Function 6

3D AttU-NET for Brain Tumor Segmentation with a Novel Loss F...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Roy, Rahul Annappa, B. Dodia, Shubham National Institute of Technology Karnataka Surathkal Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350346961

In the United States of America (USA), every year 150,000 patients are registered with a secondary brain tumor that is not generated in the brain. This necessitates the need for early brain tumor detection, which in turn will help patients to live longer. For clinical evaluation and treatment, precise segmentation of brain tumors in MRI images is required. This process can be aided by machine learning and efficient image processing, but manual imaging can be time-consuming. In this study, we aim to develop an 3D automated segmentation algorithm with a novel loss function. A 3D attention UNET CNN model was trained using the novel loss function, which was calculated by taking the weighted average of dice loss and focal loss to overcome the class imbalance. Results show the enhancement in the segmentation performance of attention UNET model with an average increase of 5% in the Dice coefficient for all three classes. However, the model's performance was not as strong for enhanced and core tumors. Further research may be needed to optimize performance in these areas.. © 2023 IEEE.

关键词： Magnetic resonance imaging

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An IoT-Based Sustainable Automation System for the Garments Industry in Bangladesh 3

An IoT-Based Sustainable Automation System for the Garments ...

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3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Ahmed, Khandakar Rabbi Salam, Towfika Rabbu, Mohammad Mahmudul Haque, F.M. Hasan, Nazmul Miraz, Shafiun Rana, Md. Masud Mansur, Maliha Tabassum, Humayra Shayer, Khandokar Shadman Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307399

The Readymade Garments sector accelerates Bangladesh's development. The 'Made in Bangladesh' brand has also helped to boost Bangladesh's reputation and brand recognition on a global scale. Despite its limited resources, the country has maintained a 6% annual GDP growth rate while attaining extraordinary social and human development. On the other hand, the phrase 'Internet of Things' (IoT) refers to a broad concept that can be summarized as an ecosystem of intelligent gadgets equipped with networks, sensors, and processing tools. IoT is a new technological platform that enables the many objects in our environment to connect via sensors and the Internet to improve our quality of life. Manufacturing and the industrial sector, agriculture, the health sector, smart cities, security, and emergency services, to name a few. IoT implementation in the garment sector would open a new door to growth in production and worker safety. Implementing home automation and safety systems in the garment sector will help in production and ensure the safety of the workers. The suggested IoT -based sustainable automation system for the Bangladeshi garments sector is efficient, eco-friendly, secure, reliable, and user-friendly. The system's user-friendly interface makes it straightforward to use and manage. The Bangladeshi garments industry's IoT -based sustainable automation system is a comprehensive and integrated solution that addresses the industry's primary difficulties and provides a sustainable and viable future. © 2023 IEEE.

关键词： Internet of things

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection Dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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