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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mahaveerakannan, R. Nair, Sreerengan V R Rohini, M.S. Vinoth Kumar, M. Rajakumar, B. Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Tamil Nadu Chennai India Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India JNN Institute of Engineering Chennai Periyapalayam Highway Department of AI & DS Tamil Nadu Chennai601102 India

ISBN: (纸本)9798331530013

The potential of digital examinations and advanced tele-dentistry applications remains limited due to the lack of universal access to dental care, particularly in remote regions with scarce financial resources. This study aims to standardize the use of digital dental cavity evaluation and progression tracking through image-based techniques. The primary objective of this research was to design an innovative, cost-effective virtual tool capable of predicting dental cavitation from non-standardized images with clinically acceptable reliability. A dataset comprising 1,903 photographs of flattened tooth surfaces, captured using an iPhone 7 by 696 participants, was utilized. The study evaluated four deep learning strategies: YOLOv3, Faster R-CNN, RetinaNet, and Single-Shot Multi-Box Detector (SSD), to detect early cavities and dental infections. The benchmark was set against dentists' evaluations based on image inspections aligned with the Global Cavities Classification and Oversight Method. Among the models, YOLOv3 and Faster R-CNN demonstrated the highest sensitivity for cavitated caries, achieving 88.5% and 72.5%, respectively. However, their sensitivity for visually non-cavitated lesions was lower, at 37.9% and 27%, respectively. All four models achieved specificity levels exceeding 86% for cavitated cavities and over 72% for visually non-cavitated lesions. This research integrates model-based categorization, test-time enhancement, and portable deep learning techniques into a virtual ai-driven system capable of predicting dental cavities from irregular images with adequate clinical reliability. The proposed framework has the potential to enhance access to oral healthcare in underserved and resource-constrained areas, supporting automated diagnoses and advancing tele-dentistry application. © 2024 IEEE.

关键词： Cavitation

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An Efficient Approach to Skin Color Segmentation Using Perceptually Uniform CIELab Color Model 11th

An Efficient Approach to Skin Color Segmentation Using Perce...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Sathish, B.S. Jerry Alexander, T. Padmavathy, B. Leo Joseph, L.M.I. Ganesan, P. Sajiv, G. Murugesan, R. Department of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Department of ECE Swarnandhra College of Engineering and Technology Andhra Pradesh Narsapur India Department of Computer Science Engineering Sri Venkateshwara College of Engineering Bengaluru India Department of ECE SR University Telangana Warangal India Department of ECE Vidya Jyothi Institute of Technology Hyderabad India Saveetha School of Engineering SIMATS Chennai India Department of ECE Narshima Reddy Engineering College Secunderabad India

ISBN: (纸本)9789819717231

The skin segmentation is one of the most crucial steps in many applications such as face detection, human identification, and video conferencing. However, it is not straightforward to classify the skin pixels because the skin tone is extensively changing from darkest to lightest due to the divergence in the quantity of melanin (pigmentation). The proposed work focused on the segmentation of skin pixels using the device independent and perceptually uniform CIELab color model. In the proposed method, all the three components of the CIELab model are independently applied for skin segmentation. The outcome of the CIELab-based skin segmentation is compared to the result of RGB-based segmentation to exhibit the preeminence of the proposed method. The investigational outcome clearly exemplified that the chrominance components ‘a’ and ‘b’ precisely classified skin pixels in all test images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Overview of Energy Harvesting and Sustainability in Smart Cities 3

Overview of Energy Harvesting and Sustainability in Smart Ci...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Sustainability is a key factor in the context of smart cities. In smart cities, promoting sustainable energy is one strategy for achieving sustainability and lowering greenhouse gas emissions. Different energy sources are needed to meet the need for affordable and dependable electrical energy, where the environmental impact typically outweighs the cost of generation. In order to increase sustainability in the electrical industry, smart cities are examined in this article. The methodological strategy for this involved conducting a systematic literature review utilizing the PRISMA procedure. 154 journal articles were examined in-depth for this review. Energy efficiency, renewable energy, and energy and urban planning were the themes that the results were organized into. The results of the study showed that: (a) there is a global academic publication landscape for research on smart cities and energy sustainability;(b) publications are unbalanced when comparing the results of studies on the energy use intensity of different continents to those on the energy sustain ability of smart cities;and (c) there is a strong focus on technology-related issues related to energy sustainability, efficiency, and renewables topics in the literature, but much less attention is given to these issues. Urban and energy authorities are informed by the insights produced, and scholars are given ideas for future research directions. © 2023 IEEE.

关键词： Smart city

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (ai) and natural language processing (NLP) has significantly advanced stock market forecasting. ai's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where ai has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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On The Persona-based Summarization of Domain-Specific Documents

arXiv

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arXiv 2024年

作者： Mullick, Ankan Bose, Sombit Saha, Rounak Bhowmick, Ayan Kumar Goyal, Pawan Ganguly, Niloy Dey, Prasenjit Kokku, Ravi Computer Science and Engineering Department IIT Kharagpur India Emergence AI

In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large information repositories. However, every persona of a domain has different requirements of information and hence their summarization. For example, in the healthcare domain, a persona-based (such as Doctor, Nurse, Patient etc.) approach is imperative to deliver targeted medical information efficiently. Persona-based summarization of domain-specific information by humans is a high cognitive load task and is generally not preferred. The summaries generated by two different humans have high variability and do not scale in cost and subject matter expertise as domains and personas grow. Further, ai-generated summaries using generic Large Language Models (LLMs) may not necessarily offer satisfactory accuracy for different domains unless they have been specifically trained on domain-specific data and can also be very expensive to use in day-to-day operations. Our contribution in this paper is two-fold: 1) We present an approach to efficiently fine-tune a domain-specific small foundation LLM using a healthcare corpus and also show that we can effectively evaluate the summarization quality using ai-based critiquing. 2) We further show that ai-based critiquing has good concordance with Human-based critiquing of the summaries. Hence, such ai-based pipelines to generate domain-specific persona-based summaries can be easily scaled to other domains such as legal, enterprise documents, education etc. in a very efficient and cost-effective manner. © 2024, CC0.

关键词： Cost effectiveness

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Heterogeneous Wireless Access Networks for IoT and E-Health-A Survey 3

Heterogeneous Wireless Access Networks for IoT and E-Health-...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

ISBN: (纸本)9798350330724

In a heterogeneous wireless environment, there are many different radio access technologies (RATs), such as Wi-Fi and 2G, 3G, 4G, and 5G, which each have a different degree of coverage and processing power to meet various service requirements. A mobile user may have access to various access networks in such a situation. On the PC of each user, numerous programs with varied Quality of Service (QoS) requirements can operate simultaneously. It would be advantageous for a multi-interface terminal to use two or more interfaces simultaneously in order to improve performance. However, using multiple networks at once could use up more energy than just using one interface. Therefore, energy use must to be taken into account when analyzing the Flow/Interface Relationship (FIA). This paper presents a novel method for choosing the ideal FIA that achieves the best trade-off between all the characteristics taken into account, dubbed Smart Tabu Search (STS). STS considers user preferences, network circumstances, network expenses, application QoS specifications, and battery life of mobile devices. We use simulations and testbed experiments to validate our concept. Professionals are now able to diagnose and monitor patients remotely because to the growing use of healthcare monitoring devices. One of the most frequent occurrences that affects the reliability of information transmission in any network is congestion, which is defined as the unchecked increase in traffic relative to network capacity. With the use of a mesh network, wireless sensors, and some of the most significant models for vital signs, the article aims to provide a realistic simulation environment for a healthcare system. The simulator environment is a helpful tool for assessing the dependability and effectiveness of the healthcare system in a realistic setting. However, the system is not appropriate for real-time applications due to the sluggish network adaption caused by the end-to-end based traffic regulation deci

关键词： Electrocardiograms

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Mental Health and Suicide Risk among College Students: A Detailed Exploration of Contributing Factors 15

Mental Health and Suicide Risk among College Students: A Det...

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

作者： Meena, R. Gunasundari, B. Omana, J. Sripriya, N. Yamini, G. Revathi, K.P. Easwari Engineering College Department of AI&DS Chennai India Prathyusha Engineering College Department of CSE Chennai India VIT University School of Computer Science and Engineering Chennai India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of English Chennai India

ISBN: (纸本)9798350370249

This study aims to identify critical predictors of suicide and inform targeted interventions using data from 27,014 college students. It examines a comprehensive range of mental health indicators, including psychiatric symptoms, suicidal intentions, impulsivity, sleep disturbances, internet addiction, aggression, self-injury behaviors, and eating problems, alongside demographic and socioeconomic variables. Analytical techniques, including exploratory data analysis, cross-tabulation, and correlation analysis, uncover the complex interplay between these factors and suicide risk. The study evaluates several machine learning algorithms. The weighted Random Forest uses feature weighting based on point-biserial correlation coefficients to enhance prediction accuracy and prioritize relevant features. The findings reveal significant associations between higher scores in mental health indicators and increased suicidal behavior, emphasizing the need for comprehensive mental health support systems. By identifying key risk factors through machine learning models, the study provides actionable insights for developing targeted interventions aimed at reducing suicide risk among college students. © 2024 IEEE.

关键词： Feature weighting Fuzzy evaluation method Machine learning models Point-biserial correlation coefficients

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PVT v2:Improved baselines with Pyramid Vision Transformer

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Computational Visual Media 2022年第3期8卷 415-424页

作者： Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao Shanghai AI Laboratory Shanghai 200232China Department of Computer Science and Technology NanjingUniversityNanjing 210023China Department of Computer Science the University ofHong KongHong Kong 999077China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210014China Computer Vision Lab ETH ZurichZurich 8092Switzerland SenseTime Beijing 100080China Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Transformers have recently lead to encouraging progress in computer *** this work,we present new baselines by improving the original Pyramid Vision Transformer(PVT v1)by adding three designs:(i)a linear complexity attention layer,(ii)an overlapping patch embedding,and(iii)a convolutional feed-forward *** these modifications,PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification,detection,and *** particular,PVT v2 achieves comparable or better performance than recent work such as the Swin *** hope this work will facilitate state-ofthe-art transformer research in computer *** is available at https://***/whai362/PVT.

关键词： transformers dense prediction image classification object detection semantic segmentation

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Cash Flow Forecasting Based on Wavelet Transform and Neural Networks 8

Cash Flow Forecasting Based on Wavelet Transform and Neural ...

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8th International Conference on computer science and engineering, UBMK 2023

作者： Soylen, Zumra Mammadova, Farida Fasounaki, Mandana Ozmen, Ayse Irem Ince, Gokhan Istanbul Technical University Department of Computer Engineering İstanbul Turkey Mdp Group R&d Department İstanbul Turkey Istanbul Technical University Department of Computer Engineering & Ai and Data Science Application and Research Center İstanbul Turkey

ISBN: (纸本)9798350340815

Cash flow forecasting is a critical task for businesses and financial institutions to ensure effective financial planning and decision-making. However, limited data availability poses a significant challenge when developing accurate and robust cash flow prediction models. In this paper, we investigate the performance of various forecasting methods and propose an approach based on wavelet transform for improving the forecasting accuracy. We demonstrate the effectiveness of the proposed approach with the best combination of wavelet functions and methods for forecasting future values in a univariate time series. We investigate the impact of wavelet transform on forecasting techniques based on open-source datasets. Our methodology includes data collection, preprocessing, feature engineering, model selection, and experimentation using different performance evaluation metrics. © 2023 IEEE.

关键词： Forecasting

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Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization

arXiv

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arXiv 2024年

作者： Mullick, Ankan Bose, Sombit Saha, Rounak Bhowmick, Ayan Kumar Vempaty, Aditya Goyal, Pawan Ganguly, Niloy Dey, Prasenjit Kokku, Ravi Computer Science and Engineering Department IIT Kharagpur India Emergence AI

The ever-increasing volume of digital information necessitates efficient methods for users to extract key insights from lengthy documents. Aspect-based summarization offers a targeted approach, generating summaries focused on specific aspects within a document. Despite advancements in aspect-based summarization research, there is a continuous quest for improved model performance. Given that large language models (LLMs) have demonstrated the potential to revolutionize diverse tasks within natural language processing, particularly in the problem of summarization, this paper explores the potential of fine-tuning LLMs for the aspect-based summarization task. We evaluate the impact of fine-tuning open-source foundation LLMs, including Llama2, Mistral, Gemma and Aya, on a publicly available domain-specific aspect based summary dataset. We hypothesize that this approach will enable these models to effectively identify and extract aspect-related information, leading to superior quality aspect-based summaries compared to the state-of-the-art. We establish a comprehensive evaluation framework to compare the performance of fine-tuned LLMs against competing aspect-based summarization methods and vanilla counterparts of the fine-tuned LLMs. Our work contributes to the field of aspect-based summarization by demonstrating the efficacy of fine-tuning LLMs for generating high-quality aspect-based summaries. Furthermore, it opens doors for further exploration of using LLMs for targeted information extraction tasks across various NLP domains. © 2024, CC0.

关键词： Information retrieval

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