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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Priyadharshini, B. Apoorva, N. Alekhya, Arvapalli Sagar, Gajula Sujan Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Recommendation systems are the subset of data filtering techniques and focus on providing personalized suggestions to the users. The systems rely on the data to provide insightful suggestions. Over the years, recommendation system has gained huge prominence in boosting e-commerce platforms, and online personalized services to users. Along with their popularity, these systems also face challenges such as data sparsity, time constraints, and scalability. As a result, various approaches are opted along with the traditional techniques to optimize the process. This paper reviews the various approaches followed in engineering the recommendation systems and analyzes their efficiencies. The approaches focus primarily on understanding the involvement of neural networks and evolutionary algorithms and their performance compared to other systems involved with approaches like clustering techniques, linear-based techniques, probabilistic models, etc. This paper also provides insights into the various hybrid approaches followed by the different recommender systems involving neural networks and evolutionary algorithms. © 2024 IEEE.

关键词： Recommender systems

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Object Detection in Shelf Image of the Retail store using Single Stage Detector 2

Object Detection in Shelf Image of the Retail store using Si...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Bagyammal, T. Mahesh Madhav, K. Dhiraj Kumar, Y. Arthi Sri, N. Siva Srinivas, V. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly tackles critical issues in retail industry, encompassing inventory management and optimized product placement. The ultimate goal is to boost operational efficiency and elevate customer satisfaction within the retail sector. The proposed methodology involves comprehensive steps, including data collection, annotation, pre-processing, and the deployment of advanced object detection models based on YOLOv5, with a particular emphasis on YOLO due to its real-time capabilities and being known as a single stage detector. By achieving these milestones, the proposed model promises to provide valuable solutions for the retail industry, reducing out-of-stock situations, minimizing manual labour, and delivering enhanced product organization. Thus, ensuring good performance compared to its other object detectors of the same use case. We have modified the architecture at different levels of the State-of-the-art YOLOv5 (Backbone, Head) and compared the models implemented for inference. We found out that Soft-IoU layer added in the head has given the best performance in detecting object with mean average precision of 79.7 © 2024 IEEE.

关键词： Customer satisfaction

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Hidden Emotion Detection using Speech, Text, Facial Expressions and Neural Networks 5

Hidden Emotion Detection using Speech, Text, Facial Expressi...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Reddy, Ravula Tarun Palaniswamy, Suja Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350379884

Understanding and recognition of human emotions are very crucial in various fields. This paper proposes a new approach to show the different feelings that are hidden using multi-modalities like video, audio, and textual data. A custom dataset AED-FST has been used in this work which has nearly 317 samples each consisting of some audio clip, some visual media, and a question-and-answer pair. From all these entirely diverse sources, the goal is to create a model that could possibly detect emotional cues. It found hidden emotions with a 67% accuracy, representing how well it could pick up on these weak cues from all different modalities. This method contributes immensely to emotion detection in extremely wide fields, including psychological diagnoses and human-computer interaction, to mention just a few. © 2024 IEEE.

关键词： Human computer interaction

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Deep Reinforcement Learning in Stock Trading: Evaluating DDPG and DQN Strategies 6

Deep Reinforcement Learning in Stock Trading: Evaluating DDP...

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6th IEEE International Conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Kodurupaka, Nithin Basavadeepthi, H.M. Pecheti, Shiva Teja Amudha, J. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306613

This study presents a comparative analysis of the Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms in the context of stock trading, focusing on historical stock price data of S&P 500 stock index. The primary objective is to evaluate the effectiveness of these RL algorithms in making trading decisions and achieving financial performance relative to a passive buy-And-hold strategy. The assessment revolves around key metrics, with a special emphasis on the buy, hold, and sell indications as essential signals for trading actions. Results indicate that DQN outperforms DDPG, showcasing a smaller total loss during the evaluation period. This suggests that DQN may offer superior capabilities in limiting financial losses and making effective trading decisions compared to DDPG, providing valuable insights into algorithm selection for dynamic trading environments. It is crucial to recognize the significance of buy, hold, and sell indications as key elements in assessing the performance of RL algorithms in stock trading scenarios. © 2024 IEEE.

关键词： Algorithmic Trading Buy-And-Hold Strategy DDPG Trading Agent Deep Neural Networks Deep Reinforcement Learning DQN Portfolio Performance Total Return

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Enhancing Code Insights through Semantic Change Impact Evaluation 15

Enhancing Code Insights through Semantic Change Impact Evalu...

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

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex code bases create problems. By evaluating the effect of reflection changes, this study contributes to the improvement of the comprehension of code, a critical success factor for quality and efficiency. This addresses issues by using semantic analysis, change detection, refactoring detection, and semantic change detection and diffing. Our approach documents the semantic differences between the original and changed source code and gives pragmatic conclusions on how to enhance code quality and maintainability. Specifically, we divide the code into AST files using simple python scripts for semantic analysis and compare them for differences to find implications. It executes the method through the focus on development workflows and major projects as it includes the past information while still simplifying argument parsing and file processing. Based on our study findings, a better understanding of code can help developers to improve their decision-making capacity and protect the performance during the enhancement activity of the program. © 2024 IEEE.

关键词： Abstract Syntax Trees (ASTs) change detection refactoring detection semantic analysis semantic changes

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MAESTRO-EP: Multi-Architecture Ensemble System for Temporal Reasoning and Outcome Prediction in Event Processing 8

MAESTRO-EP: Multi-Architecture Ensemble System for Temporal ...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Shekar, Vaibhav Devaraja, G. Mahadevan, Anbazhagan Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331531539

MAESTRO-EP (Multi-Architecture Ensemble System for Temporal Reasoning and Outcome Prediction in Event Processing) is an innovative deep learning framework designed to model and predict outcomes in complex event-driven systems. This paper introduces a novel multi-head ensemble architecture that leverages diverse neural network models, including RNNs, LSTMs, CNNs, Transformers, and GRUs, to capture intricate temporal patterns in event sequences. Our approach utilizes a minimal event schema, allowing the model to organically learn complex behaviors across various domains, from supply chain networks to game environments. We evaluate MAESTRO-EP on multiple datasets, including supply chain simulations, Tic-Tac-Toe, and pathfinding algorithms, demonstrating its superior performance in context output accuracy. The system's highly scalable and adaptable architecture provides a robust framework for future research in adaptive AI systems and digital twin simulations, with the potential to significantly advance event sequence modeling capabilities in dynamic multi-agent environments, particularly in complex supply chain networks. © 2024 IEEE.

关键词： Multi agent systems

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A Graph-Based Approach to Automatic Car Reliability System Prediction with Probabilistic Modeling and Machine Learning 3

A Graph-Based Approach to Automatic Car Reliability System P...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Varun, Bheema Sai Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

Reliability prediction in automotive systems undoubted represents a substantial part of safety and customer satisfaction. a new graph-based probabilistic method and machine learning algorithm for the automotive system reliability prognosis. By breaking complex dependency among vehicles' components into a hierarchy and using past failure data, the method improves on prediction error. Real-world automotive datasets show excellent results with better resolution and higher accuracy for the enhanced components compared to existing techniques while at the same time providing interpretability by visualizing the dependency of the components. © 2024 IEEE.

关键词： Prediction models

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DiffMat:Latent diffusion models for image-guided material generation

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Visual Informatics 2024年第1期8卷 6-14页

作者： Liang Yuan Dingkun Yan Suguru Saito Issei Fujishiro Graduate School of Science and Technology Keio UniversityKanagawaJapan School of Computing Tokyo Institute of TechnologyTokyoJapan Department of Information and Computer Science Keio UniversityKanagawaJapan

Creating realistic materials is essential in the construction of immersive virtual *** existing techniques for material capture and conditional generation rely on flash-lit photos,they often produce artifacts when the illumination mismatches the training *** this study,we introduce DiffMat,a novel diffusion model that integrates the CLIP image encoder and a multi-layer,crossattention denoising backbone to generate latent materials from images under various *** a pre-trained StyleGAN-based material generator,our method converts these latent materials into high-resolution SVBRDF textures,a process that enables a seamless fit into the standard physically based rendering pipeline,reducing the requirements for vast computational resources and expansive *** surpasses existing generative methods in terms of material quality and variety,and shows adaptability to a broader spectrum of lighting conditions in reference images.

关键词： SVBRDF Diffusion model Generative model Appearance modeling

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Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

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Leveraging Explainable AI for Improved Understanding and Prediction of Drug Responses in IGF1R Signaling Pathways

Leveraging Explainable AI for Improved Understanding and Pre...

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Janiel, J. Rajendran, P. Selvi School Of Computing Hindustan Institute Of Technology And Science Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Pharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their response to medications. A persons genetic composition can impact the likelihood of experiencing reactions or determining the effectiveness of a medication. By providing insights into the safety and effectiveness of drug therapies pharmacogenomics holds potential for significantly enhancing health outcomes. Through advancements in targeted therapies we can precisely target abnormalities that trigger tumor growth in patients. For instance IGF1R (Insulin like Growth Factor 1 Receptor) which belongs to the tyrosine kinase receptor family plays a crucial role in promoting cell growth, survival and proliferation across different types of cancers. The overexpression of IGF1R has been observed in cancer types indicating its involvement in fueling continuous growth and survival of cancer cells. Targeting IGF1R helps address the dysregulation of this receptor within cancer cells. Artificial Intelligence (AI) comes into play by enabling prediction of suitable drugs based on a patients genomic profile thereby reducing adverse effects and improving treatment effectiveness. Parallel, here has been growing concern regarding model explanation due, to the opaque nature of model predictions. This is particularly important when it comes to modeling drug responses. In our research paper we have employed AI to gain a clear understanding of the prediction model and the factors that affect its results. The findings show that lower valued counts of YAP-pS127-Caution protein tend to negatively impact the output. Similarly lower values of YAP-pS127-Caution protein and higher valued counts of YAP-pS127 -Caution protein, Xanthine, Tyrosine tends to positively impact the output. This helps as an aiding reference in knowing which feature of an unknown cell line should be focused to know

关键词： Proteins

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