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Addressing the Cold Start Challenge through the Implementation of Content-based Filtering and Hybrid Filtering for Movie Recommendation

Addressing the Cold Start Challenge through the Implementati...

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International Conference on computing and Networking Technology (ICCNT)

作者： Mahi Kolli L D Mukil N Kartthikeyan M Pranavkrishnan Nalini Sampath Priyanka C Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Recommender systems are of great significance for the difficulty in increasing speed as well as the amount of online information of the users to sort out the relevant content as they offer personalized suggestions. Content-based filtering and collaborative filtering are the two very common types of recommender algorithms, but they are still facing some issues, e.g. sparsity and cold-start problems. To cope with these limitations and increase recommendation accuracy, this study proposes a new form of recommendation system that combines collaborative filtering and content-based filtering to form a hybrid recommender system. The system proposed integrates the advantages of both methods and, therefore, offers more precise and individualized recommendations. As an illustration, the hybrid method shows superiority compared to the traditional methods, which proves that deep learning might be the key to advancing recommendation systems.

关键词： Deep learning Accuracy Filtering Social networking (online) Collaborative filtering System performance Soft sensors Hybrid power systems Real-time systems Recommender systems

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An Ensemble Approach to Tuberculosis Prediction using Shenzhen and Montgomery Datasets

An Ensemble Approach to Tuberculosis Prediction using Shenzh...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tummala Varshith Thanush S Koneri Tetali Syam Kumar Reddy Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Chest X-rays are critical for detecting a variety of lung disorders, with tuberculosis (TB) being a particular priority due to its global impact on public health. TB must be identified quickly and accurately using medical imaging to begin treatment and stop the spread of the illness. This work examines the importance of chest X -ray analysis and its role in TB identification. A novel convolutional neural network (CNN) based ensemble approach is proposed, thoroughly investigating the performance of state-of-the-art sophisticated deep learning models, such as Xception, InceptionV3, DenseNet201, ResNet50, EfficientNetB2, ResNet50-InceptionV3, EfficientNetB2-Xception, DenseNet201-Xception, and DenseNet201-InceptionV3, for differentiating between normal and tuberculosis-affected chest X-rays. The standard Shenzhen and Montgomery datasets were used for training and testing the efficacy of the proposed model. The findings of this study promote the development of accurate and effective TB detection technologies, ultimately enhancing global efforts to combat this deadly illness. Notably, the DenseNet201InceptionV3 ensemble produced the best results for TB detection, achieving a precision of 0.91, a recall of 0.85, an F score of 0.89, and an accuracy of 95.14%.

关键词： Training Accuracy Tuberculosis Ensemble learning Convolutional neural networks X-ray imaging Standards Biomedical imaging Testing Residual neural networks

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Road Accident Detection and Alert System at Real-Time Using Deep Learning Techniques

Road Accident Detection and Alert System at Real-Time Using ...

引用

Communication, computing and Industry 4.0 (C2I4), International Conference on

作者： C Mithul D Mohammad Abdulla M Hari Virinchi M Sathvik Suja Palaniswamy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

This paper presents a real-time road accident detection and alert system that leverages a custom sequential CNN model optimized for rapid accident identification. Unlike existing approaches, this method integrates an automated alert system through Telegram’s API, ensuring immediate notification to emergency responders. This enhancement significantly reduces response time, allowing for faster intervention and potentially saving lives. A convolutional neural network (CNN) model has been developed using frames extracted from CCTV video footage of accidents and non-accidents. The frames are processed and classified into an accident and non-accident categories for training the model to assess the incoming frames of a video in real-time. The data preprocessing, model selection, training, model validation, and performance assessment against benchmarks resulted in an efficient and accurate CNN-based accident detection system. It analyses live or recorded videos of the scene and determines whether an accident is present in the frame. In case of high confidence in an accident across multiple frames in succession, the system takes a picture in that frame, logs the time and location details, and then sends an alert message containing the time, location coordinates, and details of the accident to a specific Telegram channel through the Telegram Bot API. This monitoring method makes it easier to analyze the occurrence of an accident and report it within a short period.

关键词： Training Road accidents Accuracy Streaming media Cameras Real-time systems Data models Convolutional neural networks Time factors Monitoring

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Implementation and Analysis of TCP CUBIC Congestion Control System in Mininet

Implementation and Analysis of TCP CUBIC Congestion Control ...

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International Conference for Convergence of Technology (I2CT)

作者： S Navin Sunder Ve Ram Akathya Samarth Seshadri Shinu M Rajagopal Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the realm of data transfer dynamics, an intricate comprehension of congestion control is imperative for the seamless optimization of network performance. This study rigorously delves into the complexities, unveiling discernible disparities between sending rates and throughput. Emphasizing the critical role of congestion control, the research highlights escalating retransmissions and congestion as pronounced inefficiencies. The proposed methodology encompasses a thorough examination of packet loss, network utilization, and queuing delays, paired with the implementation of effective congestion control strategies. Post-implementation, the results demonstrate a significant enhancement in network performance, characterized by adaptive sending rates and minimized inefficiencies. This research underscores congestion control’s pivotal role in optimizing performance, offering valuable insights for achieving an efficient and responsive network.

关键词： Analytical models Protocols Packet loss Throughput Data transfer Control systems Data models

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Revealing distribution discrepancy by sampling transfer in unlabeled data 24

Revealing distribution discrepancy by sampling transfer in u...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhilin Zhao Longbing Cao Xuhui Fan Wei-Shi Zheng School of Computing Macquarie University Australia and School of Computer Science and Engineering Sun Yat-sen University China School of Computing Macquarie University Australia School of Computer Science and Engineering Sun Yat-sen University China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9798331314385

There are increasing cases where the class labels of test samples are unavailable, creating a significant need and challenge in measuring the discrepancy between training and test distributions. This distribution discrepancy complicates the assessment of whether the hypothesis selected by an algorithm on training samples remains applicable to test samples. We present a novel approach called Importance Divergence (I-Div) to address the challenge of test label unavailability, enabling distribution discrepancy evaluation using only training samples. I-Div transfers the sampling patterns from the test distribution to the training distribution by estimating density and likelihood ratios. Specifically, the density ratio, informed by the selected hypothesis, is obtained by minimizing the Kullback-Leibler divergence between the actual and estimated input distributions. Simultaneously, the likelihood ratio is adjusted according to the density ratio by reducing the generalization error of the distribution discrepancy as transformed through the two ratios. Experimentally, I-Div accurately quantifies the distribution discrepancy, as evidenced by a wide range of complex data scenarios and tasks.

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Generation of Photorealistic Face Images Using Deep Convolutional Generative Adversarial Networks

Generation of Photorealistic Face Images Using Deep Convolut...

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Conference on Information and Communication Technology (CICT)

作者： Sagala Sai Anvitha Sagam Prashamsa Reddy Yerramachu Sunaini Nidhin Prabhakar T V Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

In present time, the area of generating photorealistic images using Generative Adversarial Networks (GAN s) has seen considerable interest due to their capability to make high-quality, high-efficient and diverse visual content. This project mainly aims on proceeding the generation of photorealistic face images by addressing existing vulnerabilities in already existing face generation methods. The main objective is to examine and enhance the detection of generated faces by discriminator in GAN sand customizing the generator of a Deep Convolutional GAN model and the evaluation of its efficiency in producing less vulnerable to naked eye, visually clear, and realistic images. By utilizing diverse datasets such as CelebA, this study also aims to develop powerful methodologies that can reliably generate photorealistic facial images. Here the approach involves a thorough examination of the strengths and weaknesses of existing GAN models in generating realistic facial images. Through experimentation with diversified Deep Convolutional GAN architecture and optimization techniques, this study aims to attain significant improvements in image fidelity and realism. By utilizing the large and diverse datasets available, the work intends to traverse various strategies to mitigate common artifacts and inconsistencies observed in GAN -generated faces. Here using different variants of DCGAN s, significant results were obtained indistinguishable from real photographs.

关键词： Visualization Generative adversarial networks Generators Information and communication technology Reliability Faces Optimization Photorealistic images

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Adaptability of Denoising Models for Low Dose CT Images

Adaptability of Denoising Models for Low Dose CT Images

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International Conference on computing and Networking Technology (ICCNT)

作者： Bharathi D Tarun Ramaswamy Anjali Chullimadam Ganesan Bijjala Lohith Chandra Kolli Ajay Paul Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Computed Tomography (CT) is a fundamental diagnostic imaging modality that utilizes various technologies. A CT scan involves capturing multiple X -ray images from various angles around the body and then utilizing computer algorithms to generate detailed cross-sectional images, or slices, depicting the blood vessels, tissues and bones of the patient. The imperative for enhanced imaging often necessitates exposing individuals to substantial doses of ionizing radiation, particularly in instances involving repeated CT scans or elevated radiation doses. In response to radiation-related concerns, Low Dose CT (LDCT) has emerged as a key focus of research, reducing patient exposure to merely 25 percent of the radiation associated with conventional Full Dose CT (FDCT) scans. Despite its evident advantages in radiation reduction, LDCT images tends to yield images characterized by heightened random noise and artifacts. The intrinsic challenges in LDCT images compromise their clinical utility, affecting diagnostic accuracy. Present denoising methodologies often prioritize quantitative metrics, potentially resulting in over-denoised and blurred CT images, neglecting realistic image preservation. This study evaluates contemporary general image denoising techniques alongside deep learning denoising methods specifically tailored for low-dose computed tomography, focusing on their ability to preserve high-frequency details and the realism of the resulting denoised images. The objective is highlight the state of current deep learning denoising models and find models that are able to achieve both improved metrics and produce realistic denoised image outputs.

关键词： Measurement Deep learning Adaptation models Visualization Analytical models PSNR Computed tomography Computational modeling Noise reduction Medical diagnostic imaging

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A Next-Gen Approach to Streamlined Canteen Management Systems using Mesa Framework

A Next-Gen Approach to Streamlined Canteen Management System...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Advik Narendran Anantha Hothri Inuguri Manyam Yashaswini Srinidhi Sundaram Manju Khanna Department of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

A next-generation canteen management system is proposed that employs intelligent software agents to streamline various tasks, such as order processing and customer service, within canteens. The system automates verification through ID card scanning and OTP authentication, ensuring secure and efficient operations. By integrating agents to manage orders and provide real-time updates, the system enhances the dining experience by reducing manual effort and improving service speed. This smart solution, designed to cater to institutional needs, provides seamless control over canteen processes while enhancing user satisfaction through a modern, technology-driven approach.

关键词： Hands Customer services Authentication Process control Manuals Software agents Real-time systems Computational efficiency Information technology Next generation networking

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Detecting AI-generated images with CNN and Interpretation using Explainable AI

Detecting AI-generated images with CNN and Interpretation us...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Bharathi Mohan G Prasanna Kumar Rangarajan Akilesh rao S Mandava Sukesh Abinandhini D M Jaikanth Y Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

The rapid proliferation of digitally manipulated images in various media outlets has necessitated the development of robust and reliable methods for distinguishing between authentic and synthetic visual content. In response to this imperative, this research leverages state-of-the-art deep learning techniques and an extensive dataset of ‘real’ and ‘fake’ images to create an AI model for image identification. The study encompasses a meticulous data collection process, preprocessing steps, the selection of the DenseNet121 model architecture, model training, and evaluation of the model's performance through a range of metrics, including accuracy (93.42), precision, recall, and the Area Under the ROC Curve (AUC). To enhance the transparency and interpretability of the model, employed the Gradient-weighted Class Activation Mapping (Grad-CAM) technique, enabling to visualize the regions within images that influenced the model's decision-making. The research provides valuable insights into the development and evaluation of AI systems for image identification, particularly in the context of distinguishing between real and fake content. Furthermore, the interpretability techniques shed light on the model's decision-making process. This research contributes to the field of image authentication and paves the way for enhanced image classification in the era of synthetic media.

关键词： Heating systems Training Visualization Accuracy Decision making Data models Stability analysis

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Cloud-Native CDN Monitoring Using CI/CD

Cloud-Native CDN Monitoring Using CI/CD

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International Conference on computing and Networking Technology (ICCNT)

作者： Richa Vivek Savant S Navin Sunder Samarth Seshadri Niharika Panda Shinu M Rajagopal Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In a time where digital content consumption reigns, it is essential to efficiently deliver multimedia resources. This research seeks to transform how content is delivered by implementing and overseeing a secure, monitored Content Delivery Network (CDN) on a scalable Kubernetes cluster, managed through a secure CI/CD pipeline built on Jenkins, using the Amazon Web Services (AWS) and essential DevOps tools like Docker, SonarQube, Trivy, Prometheus, Grafana, Argo CD and Helm. The results obtained in this study are derived from an emulation of a CDN. The SonarQube analysis confirms whether the system meets all code quality gates, Trivy identifies and resolves critical container vulnerabilities and OWASP vulnerability checks identify and mitigate security risks in software dependencies. The system is monitored by analyzing the performance metrics visualized in Grafana dashboards, which offer detailed insights into metrics including and not limited to: active jobs, job queue duration, queued rate, memory usage, CPU usage, executor health metrics, system load, RAM usage, root filesystem usage, disk space usage, disk I/O activities, time synchronized drift and time synchronized status. Additionally, SMTP e-mail notifications are configured to improve responsiveness to anomalies identified via monitoring. This study emphasizes making content delivery mechanisms more robust, providing advanced tools for developers, and enabling easy access to digital resources for the community.

关键词： Chaos Codes Content distribution networks Web services Pipelines Random access memory Security Synchronization Monitoring Queueing analysis

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