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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sen, Muskan Chaturvedi, Kapil Sagar Institute of Research Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350313987

Sentiment Analysis is the need of the hour for every big or small companies, be it manufacturing or entertainment industries, educational institutions, media houses, political parties or even individual. All organizations want to identify churn and to get their customers retained with them by mean of sentiment analysis on social media. Increasing number of Internet and social media users contribute in petabytes of unstructured data generated every day. The continuous advancement in technology sector, in terms of high storage capability, computational processing power of the computers, cloud technology etc., has enabled processing of these high-volume unstructured data with ease, in a very less time, which otherwise would have taken even days to process. Many sentiment analysis systems are modelled by using deep learning, by using GRU (gate recurrent unit) architecture, has showed significant improvements with high tendency to reveal the underlying semantic meaning in the input text. GRU model seems to perform even better with lowest loss 0.0783, variable loss 0.4728, highest accuracy 97.20% and variable accuracy 88.19%. © 2023 IEEE.

关键词： Accuracy Deep Learning Gate Recurrent Unit Loss Sentiment Analysis (SA)

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Enhancing Healthcare Record Privacy Through the Integration of Visual Cryptography and Diverse Image Encryption Techniques 9

Enhancing Healthcare Record Privacy Through the Integration ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Sandeep, Dayina Ganesh, Addala Siva Krishna, Angalakurthi Jaya Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splitting sensitive healthcare information into multiple shares using visual cryptography, unauthorized access is prevented unless all shares are combined, adding an additional layer of security. Furthermore, image-based encryption adds another line of defense, ensuring that even if hackers gain access to data, it remains unusable without the correct decryption methods. Through the implementation of these techniques, we demonstrate a significant enhancement in the security of healthcare records. By combining diverse encryption methods, our approach not only strengthens the protection of personal health information but also prevents unauthorized data dissemination, as seen in the Star Health breach. The results indicate that our model is resilient to various cyber-attacks, reducing the likelihood of large-scale data leaks and improving the integrity of healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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TypeScript: An Open-Source Programming Language with Options for Robust Development and Large-Scale Applications

TypeScript: An Open-Source Programming Language with Options...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Shreyansh Kushwah Chanchal Bansal Shreyansh Rathore Vandana Kate Divyanshi Bargal Divesh Vishwakarma Dept. of Computer Science and Information Technology Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

JavaScript is widely used for web and application development but is criticized for its dynamic nature. TypeScript, an open-source language developed by Microsoft, addresses theseconcerns by introducing optional static typing. Released in 2012, TypeScript is a syntactical superset of JavaScript, offering features to enhance code quality and support large-scale applications. However, despite its benefits, the adoption of TypeScript is not without challenges. From a research perspective, further investigation is needed to understand the impact of TypeScript on software development practices, its integration with existing JavaScript codebases, and its effectiveness in improving overall software quality and developer productivity. Such research can provide valuable insights into the evolution of programming languages and their adaptation to changing technologies and industry demands.

关键词： Industries Productivity computer languages Codes Collaboration Software quality Software development management

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Data Augmentation for Medical Image Classification based on Gaussian Laplacian Pyramid Blending with a Similarity Measure

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IEEE Journal of Biomedical and Health Informatics 2023年第6期29卷 1-8页

作者： Kumar, Abhinav Sharma, Anshul Singh, Amit Kumar Singh, Sanjay Kumar Saxena, Sonal Varanasi India Department of Computer Science and Engineering National Institute of Technology Patna India Division of Veterinary Biotechnology Indian Veterinary Research Institute Izatnagar India

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis. IEEE

关键词： computer aided diagnosis

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Energy-efficient computation offloading assisted by RIS-based UAV

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The Journal of China Universities of Posts and Telecommunications 2024年第1期31卷 37-48页

作者： Li Linpei Zhao Chuan Su Yu Huo Jiahao Huang Yao Li Haojin School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Research Department of 5G and Drone China Mobile(Chengdu)Institute of Research and DevelopmentChengdu 610200China College of Communication Engineering Chengdu University of Information TechnologyChengdu 610225China Research and Development Center Sony(China)LimitedBeijing 100027China

The new applications surge with the rapid evolution of the mobile *** explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local *** is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading *** edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low *** this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC *** energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the *** that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based *** combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be ***,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.

关键词： mobile edge computing unmanned aerial vehicle reflecting intelligent surface

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Secure Student Record Management using Visual Cryptography in Educational Systems 9

Secure Student Record Management using Visual Cryptography i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： Cloud security

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End-to-end Optimized Lossy Compression for Neural-morphic Spiking Camera Captured Data

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6074-6086页

作者： Feng, Kexiang Jia, Chuanmin Pan, Jingshan Ma, Siwei Gao, Wen Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Peking University National Engineering Research Center of Visual Technology Beijing100871 China Peking University Wangxuan Institute of Computer Technology Beijing100080 China Shandong Computer Science Center National Supercomputer in Jinan Jinan250014 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China

Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we propose a novel Reconstruction-based Contextual Spike Compression (RCSC) framework, which contains scene reconstruction, contextual image compression and spike generation. To our knowledge, it is the first learning-based model for efficient and robust spike stream compression with informative fidelity. Extensive experimental results show that our model outperforms the state-of-the-art conventional codec VVC intra by 6.14% and surpasses the state-of-the-art learned codec by 2.53% in BD-rate reduction, establishing a strong baseline for spike compression. © 1991-2012 IEEE.

关键词： Image compression

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DriverSense: A Multi-Modal Framework for Advanced Driver Assistance System 16

DriverSense: A Multi-Modal Framework for Advanced Driver Ass...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Vyas, Daiwat Amit Chaturvedi, Manish Institute Infrastructure Technology Research and Management Gujarat India Institute of Technology Nirma University Ahmedabad India Institute of Infrastructure Technology Research and Management Department of Computer Science and Engineering Gujarat India

ISBN: (纸本)9798350383119

Driver distraction, lack of concentration, and increased stress levels are the primary reasons for road accidents. The Advanced Driver Assistance System (ADAS) is used in high-end cars to improve the safety of drivers. These systems use an in-vehicle camera to track the eye movement of a driver and analyze the facial expressions to classify the driver's attention level, mood and/or drowsiness. However, these computer vision-based solutions do not classify the stress and distraction level of a driver which are the major causes of fatality. Further, no ADAS is available to the best of our knowledge for two-wheeler (2W) and three-wheeler (3W) vehicle drivers who represent 80% of the driver population in India. These drivers are exposed to more distractions, stress, and risk due to an open environment. Accordingly, this paper proposes DriverSense, a wearable device based vehicle independent framework for ADAS that uses mobility sensors (e.g., gyroscope, accelerometer, GPS, etc.) and physiological sensors (e.g., EEG, PPG, ECG, SPO2, etc.) to detect aggressiveness and stress level of drivers in real time. The framework employs edge computing to reduce communication delays, and machine learning models for computing inferences related to driver behavior. The results of preliminary experiments conducted using the existing datasets and the data of mobility sensors collected in Ahmedabad city of India are encouraging. The aggressiveness of the drivers could be classified with 86.05% accuracy, whereas the stress level was identified with 88.24% accuracy using existing machine learning algorithms. © 2024 IEEE.

关键词： Advanced Driver Assistance System (ADAS) Driver Behavior Analysis (DBA) Intelligent Transportation Systems (ITS) Mobility Sensors Physiological Sensors

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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