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Quantum computing for Climate Change: A Comprehensive Review of Current Applications, Challenges, and Future Directions 15

Quantum Computing for Climate Change: A Comprehensive Review...

引用

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

作者： Ashwani, Siddhant Tripathy, Aayushi Jeeban Karna, Siwani Reddy Jahanve, Poluru Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The need for creative ideas to address the growing challenges posed by climate change has become critical. This paper explores the various uses of quantum computing for mitigating climate change, with an emphasis on energy optimization, improved climate modeling, and weather forecasting. Environmental modeling, chemistry, material science, carbon capture techniques, and other significant applications have also been discussed. Finding important approaches, results, and associated technological constraints is the current state of study. Examining relevant case studies, the review suggests strategic directions for future research on climate change enabled by quantum computing. It serves as an essential tool for scientists, policymakers, and researchers, enabling a deeper comprehension of the potential of quantum computing in environmental science and encouraging multidisciplinary cooperation for the long-term mitigation of climate change. © 2024 IEEE.

关键词： and energy optimization carbon capture climate change climate modeling energy optimization quantum computing

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Deep Learning for Autism Detection Using Eye Tracking Scanpaths 2

Deep Learning for Autism Detection Using Eye Tracking Scanpa...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Supritha, R. Bharathi Mohan, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by difficulties in social interaction, communication, and limited repetitive behaviors, with symptoms varying significantly among individuals. Eye tracking holds promise in autism detection due to its unique ability to capture and analyze visual attention patterns, providing insights into the cognitive processes and atypical visual behaviors associated with ASD. Eye-tracking technology offers a unique perspective, allowing the observation and quantification of visual attention patterns, which may reveal distinctive features associated with ASD. These visualizations represent how individuals, particularly those with ASD, explore and engage with stimuli. In this research, we propose a novel approach using deep learning models, specifically DenseNet-201, EfficientNet B7, ResNet-50, and MobileNetV2, to analyze eye-tracking scan paths for ASD detection. The dataset focuses on visualizations of eye-tracking scan paths, primarily involving individuals with ASD. The study yielded promising results, with the deep learning models achieving accuracies of 94.97%, 94.74%, 84.21%, and 92.45%, respectively. DenseNet-201 demonstrated the highest accuracy at 94.97%. The research contributes to advancing early diagnosis and intervention strategies for individuals with ASD. © 2024 IEEE.

关键词： Eye tracking

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Tri-Model Synergy: Redefining Depression Detection through the Convergence of ANN, CNN, and SVM 5

Tri-Model Synergy: Redefining Depression Detection through t...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： George, Angelina Baniya, Achal Jose, Alphonsa Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331504403

Depression being a universal mental health challenge that affects millions globally, necessitating reliable detection methods in order to aid in timely intervention. Like physical health, mental health is not to be ignored. This study presents a novel hybrid model that combines Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) to analyze and detect depression in individuals. The hybrid model achieved a training accuracy of 95.6% and a test accuracy of 91.2%, demonstrating its robustness and reliability. The total training time was recorded at 3.01 seconds, with ANN contributing 1.11 seconds and CNN contributing 1.9 seconds. Furthermore, the model's performance metrics included an F1-Score of 0.91 and 0.91 for detecting the classes of absence and presence of depression respectively, highlighting its effectiveness in correctly identifying depressive states. This hybrid approach provides a comprehensive analysis by leveraging the strengths of each individual model, paving the way for more reliable and scalable mental health assessment tools. © 2024 IEEE.

关键词： Convolutional neural networks

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Sudoku Solver Algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： Optimization algorithms

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Consumer Behaviour Analysis for Customer Segmentation and Purchase Prediction 10

Consumer Behaviour Analysis for Customer Segmentation and Pu...

引用

10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Gopakumar, Amritha Shine, Aathira Ajim, Amina Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350384369

E-commerce, short for electronic commerce, is the practice of placing orders online or negotiating a sale's terms and price. Although e-commerce is often associated with selling products and services to consumers, business-to-business (B2B) transactions also make use of it. To market their goods, many smaller consumer enterprises rely only on online sales. Nowadays, people are choosing to make purchases online as online transaction mechanisms and shopping platforms develop. Nevertheless, given that both clients and Merchants are unable to interact with clients face-to-face, there is a limited understanding of consumer demands. Due to this, businesses may struggle to gauge nuanced cues such as body language and personal preferences. Limited direct engagement can hinder the ability to address specific customer needs and preferences, potentially resulting in misinterpretation or overlooking crucial details. In turn, businesses may face difficulties in tailoring their products or services to meet the nuanced expectations of their customers, emphasizing the importance of alternative strategies such as detailed customer feedback mechanisms and data analytics to bridge this gap. The online system logs user activity and gathers data on consumer behaviour, allowing for the prediction of consumer purchasing patterns. Therefore, to improve the shopping experience of people, our study aims to use historical sales data to analyze consumer behaviour and do customer segmentation using RFM (Recency, Frequency, and Monetary) based K-means clustering and purchase prediction using various ML models to improve sales, out of which the combination of Logistic Regression and XGBoost performed the best. © 2024 IEEE.

关键词： Sales

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Smart Traffic Flow Optimization with IoT and Machine Learning 15

Smart Traffic Flow Optimization with IoT and Machine Learnin...

引用

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

作者： Poojitha, D. Darak, Tanmay Samaddar, Ushnish Khanna, Manju Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Traffic signal optimization is crucial for the effective traffic management within cities with the primary goals being the reduction of bottleneck occurrences, avoidance of the extension of travelling time, and improvement of overall city street network function. Historically used approaches can hardly be adjusted to fluctuating traffic situation. Hence, a new methodology involving the use of machine learning-based traffic signal control with the assistance of the IR (Infrared) /UV (Ultraviolet) sensors for traffic flow sensing in real-Time environment is introduced in this study. The UV sensors make congestion distance measurements in real time which are assisted by IR sensors for traffic information acquisition. Historic data with data gathered from the traffic sensors are used to train the machine learning model to predict traffic. The strategy employs the use of heuristic search methodologies in the determination of signal timings with reference to congestion information that has been forecasted as well as congestion information that is being experienced. This paper discusses the fact that machine learning-based approaches, supported by sensor technology, can be used for effective traffic management systems that modify with the help of data. The success of this traffic management system is proved by its ability to minimize the MSE (Mean Squared Error) of congestion predictions to an average of 0.69. © 2024 IEEE.

关键词： Information management

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Hand-Written Bramhi Script Recognition using Deep Learning

Hand-Written Bramhi Script Recognition using Deep Learning

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Swaroop, Bandi Vishnu Prasad, Angala Hari Reddy, Busa Thanuj Sathwik Vardhan, Puvvadi Harsha Singh, Rimjhim Padam Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350374933

This work involves the classification of handwritten script using deep learning with a special interest in the comparison of different pre- trained models. As one of the oldest writing systems in the world, derived from the Dravidian and southeast Semitic scripts that were developed 4,500 years ago in India and Central America, Brahmi script also has variability due to the hand-written accented forms. To address these obstacles, different pre-trained models viz., Vision Transformer (ViT), MobileNetV2, VGG16, ResNet50, DenseNet, and EfficientNet with the CNN are implemented and compared. The result of the implementation infers that all of these pre- trained models yield a higher accuracy than the custom CNN traditional CNN approach, with a peak improvement in performance shown by MobileNetV2. This lays the ground for future utilization of deep learning architectures which have the capability of preserving and understanding handwritten symbolic scripts. © 2024 IEEE.

关键词： Deep learning

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MediLocker: Centralized Electronic Health Records System 2

MediLocker: Centralized Electronic Health Records System

引用

2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Harini, N. Karishma, R.S. Pranav, M. Duvvuri, Ananya Akhil, Marrivada Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The transition from traditional paper-based patient records to advanced digital formats has not been without its challenges. While Electronic Health Records (EHRs) offer instantaneous access to detailed patient health information, there are drawbacks too. One significant drawback that was identified from the literature survey was the lack of interoperability and uniformity in patients' medical information across different healthcare facilities. This fragmentation hampers communication and collaboration among healthcare providers, leading to inefficiencies and gaps in patient care. Additionally, concerns about the security and privacy of patient data in EHRs persist, with instances of data breaches and unauthorized access raising alarm within the healthcare community. In response to these challenges, this paper proposes the development of a secure, privacy-ensuring Medilocker system aimed at investigating electronic health management systems comprehensively by assessing the status of EHR systems and efficiently managing electronic health records while safeguarding patient data. The implementation of role-based access control, coupled with data anonymization for preserving confidentiality, results in the development of a cohesive solution that facilitates the systemization, integration, and fortification of the healthcare system, while ensuring prompt access to information in compliance with privacy and security standards. © 2024 IEEE.

关键词： Authentication

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Inference of Missing Attributes in Multilevel Complex Network 5

Inference of Missing Attributes in Multilevel Complex Networ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nettem, Nikhil Chowdary Nukala, Aashish Gopal Kaushal, Siruguppa Vishnu Sai Deepthi, L.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

Social networks exhibit a hierarchical structure where users are grouped at multiple levels based on communities, interests, or behaviors. The attributes associated with users provide additional information for various social network analysis tasks. However, the attributes may be missing for multiple reasons: incomplete data collection, or privacy concerns. Inferring missing attribute values within a hierarchical social network poses a challenge for accurate analysis and modeling. The state-of-the-art algorithm infers the attributes in a single level and less focus is given to infer the attributes at multiple levels. In this paper, the missing attributes are inferred by employing hierarchical clustering followed by CNN and Hierarchical Graph Neural Network. Our method's accuracy of attribute inference is compared with traditional machine learning models, including Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes. The results demonstrate that the hierarchical clustering with the CNN approach consistently outperforms traditional machine learning models in terms of attribute inference accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Adaptive Real-Time Malware Detection for IoT Traffic Streams: A Comparative Study of Concept Drift Detection Techniques 5

Adaptive Real-Time Malware Detection for IoT Traffic Streams...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bharani, D. Lakshmi Priya, V. Saravanan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

As IoT expands dynamically, cybersecurity threats are evolving with time;thus, classical malware detection systems are no more appropriate, due to the phenomenon of concept drift and the necessity of handling large amount of data while detecting malware. Concept drift is used to refer to the statistical properties of malware that change over time as the malware behavior changes. We henceforth propose a real-time system to detect malware within a large-scale network traffic streams in the presence of concept drift. The system is capable of processing continuous large volumes of IoT traffic in real-time as it is implemented using Apache Spark and Apache Kafka. The proposed system also compares the performance of six different concept drift detection techniques: KS test, CUSUM, ADWIN, KSWIN, DDM, Page Hinkley on NSL-KDD and IoT -23 datasets. The comparison results show that KS test correctly identified concept drift in both the datasets. The proposed system makes an IoT environment with better malware detection capabilities by integrating the adaptive mechanism in real-time data processing tools apart from achieving high performance and scalability. © 2024 IEEE.

关键词： Data integration

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