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Unraveling Customer Sentiment in Indian Fashion E-Commerce: A BERT-Based Approach

Unraveling Customer Sentiment in Indian Fashion E-Commerce: ...

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

作者： Oneeka Taneja Kuppili Raja Satya Alpana Deepa C Rathod Anaum Fathima M R Vishalakshi Prabhu H Computer Science (Data Science) R. V. College of Engineering Bangalore India Dept. of Computer Science R. V. College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

With the surge of e-commerce growth in India, it has become very necessary for brands to understand the sentiments of customers so that they can improve their offerings and remain in competition. In this study, the transformer-based model, namely BERT is used for conducting sentiment analysis of customer reviews related to famous Indian clothing brands such as Myntra, Ajio and Tata Cliq. Our research utilizes BERT contextual embeddings for the capture of opinions and emotions in customer feedback. In this paper, we will look to prove the efficiency of BERT in sentiment classification regarding Indian fashion e-commerce by analyzing a large corpus of reviews from these popular platforms, as well as compare its accuracy to DistilBERT. The study will identify how this approach extracts meaningful insights from unstructured text data that turn very useful to the brands for understanding customer satisfaction and improvement areas. Sentiment analysis of online cloth industry is one of the rarely explored field and our paper introduces this idea, specifically for the Indian demographic. Our results contribute to the developing literature of sentiment analysis in the Indian e-commerce domain and illustrate some of the many ways in which state-of-the-art natural language processing techniques are put to work in retail.

关键词： Industries Sentiment analysis Reviews Customer satisfaction Clothing Transformers Electronic commerce data mining Surges Information technology

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IoT-Enabled Dust Cleaning Mechanism for Enhanced Performance of Solar Panel Arrays 23

IoT-Enabled Dust Cleaning Mechanism for Enhanced Performance...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Udit Mamodiya Budesh Kanwer Indra Kishor Poornima University India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology India Dept. of Computer Science Poornima Institute of Engineering and Technology India

ISBN: (纸本)9798400709418

Over the past two decades, solar energy sources have secured a remarkable position among alternative energy options, demonstrating the feasibility of harnessing green energy to meet a significant portion of the world's electrical power needs. The development of technology to control solar energy generation has proven essential for lowering costs and increasing reliability. As a renewable energy source, solar panels provide power without releasing any pollutants. However, dirt has a negative impact on solar panels and is a significant environmental issue that affects energy generation. When dirt builds up on the surface of the solar panel, the intensity of the light falling on it decreases. As a result, the solar panel produces less electrical energy overall. As cleaning the solar panel is essential, these procedures must be continuously observed and assessed in order to be optimized. This indicates the importance of using intelligent systems to keep an eye out for dirt and clean solar panels in order to maximize their efficiency. We have developed a 1 kW solar tracking system. This research paper aims to illustrate the idea of an innovative, intelligent IoT-based dust cleaning mechanism for overall performance improvement for the solar tracker system. In a month, if the modules are not cleaned, efficiency could drop by 20% to 30%. The tracking efficiency of a dirty solar panel is measured at 41.27%, and this efficiency significantly improves to 59.89% when the panel is cleaned and combined with a tracking system

关键词： Dust cleaning system

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UniFaaS: Programming across Distributed Cyberinfrastructure with Federated Function Serving

UniFaaS: Programming across Distributed Cyberinfrastructure ...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Yifei Li Ryan Chard Yadu Babuji Kyle Chard Ian Foster Zhuozhao Li Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science and Learning Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Chicago Chicago IL USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster.

关键词： Scheduling algorithms Parallel programming Heuristic algorithms Parallel processing Elasticity Dynamic scheduling Supercomputers

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Multidimensional Impacts of Generative AI and an In-dept. Analysis of LLMs with Their Expanding Horizons in Technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ramu, K. Nijhawan, Ginni Lalitha, G. Praveen Asha, V. Fakher, Ahmed J. Velammal Institute of Technology Dept. of Cse TN Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India New Horizon College of Engineering Department of Computer Applications Bangalore India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous. © 2024 IEEE.

关键词： Differential privacy

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Exploring Television White Space as an Alternative for Wireless Broadband Connectivity 2

Exploring Television White Space as an Alternative for Wirel...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Ifijeh, Ayodele H. Adetiba, Emmanuel Adewale, Adeyinka Thakur, Surrendra Moyo, Sibusiso Emmanuel, Dare O. Ukpong, Udeme C. Dept. of Electrical and Information Engineering Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Dept. of Electrical and Information Engineering Ota Canaanland Nigeria Durban University of Technology KZN E-skills CoLab Durban4001 South Africa School for Data Science and Computational Thinking Stellenbosch University Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358834

Advancement in wireless transmission has enabled the communication among multiple devices all over the world, as well as expanding the frontiers of wireless Internet access. As a result of these advancement, numerous devices are questing for wireless connectivity thereby congesting the existing spectrum. If new measures are not taken to reduce depletion of the reserved radio resource, new allocations of wireless Internet access will be impossible, thereby risking the development of the entire wireless ecosystem. The demands for resourceful spectrum consumption have continually driven researchers and stakeholders in the wireless industry to search for spare radio spectrum to offer affordable and reliable wireless connectivity. The TV broadcasting is not fully utilizing the spectrum in some geographical zone. These TV unused spaces called TV White Spaces (TVWS), are well-thought-out to be the probable future resolutions for unavailability of spectrum. The low frequency band (470 to 694 MHz) of the unutilized TV channel has exceptional broadcast features over extensive distances and enables transmissions through high-rise buildings, trees, and mountains. These features allow the applications of TVWS in Internet of Things (IoT), wireless sensor network, wireless connectivity, wireless switching, smart grid and cities, automated transportation, and logistics as well as connectivity in cloud infrastructure. This paper presents a study on the advantages and benefits of television white space as an alternative to wireless broadband connectivity. © 2023 IEEE.

关键词： Tall buildings

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Smoke Level Monitoring and Alerting in Automobiles using IoT

Smoke Level Monitoring and Alerting in Automobiles using IoT

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Vani Pujitha Teja Aravind Kumar Rayalavarapu Lal Basha Shaik Sashank Madhabushi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Vehicle emissions significantly exacerbate air pollution, releasing harmful gases such as carbon monoxide (CO), nitrogen dioxide (NO 2 ), and hydrocarbons (HC) from poorly maintained engines and inefficient combustion processes. The study presents an Internet of Things (IoT)-based system to monitor the emissions, which mainly includes key pollutants like CO, NO 2 , and HC in real-time. Strategically positioned gas sensors near the vehicle's exhaust capture emission data, which an Arduino microcontroller processes with high accuracy. The system delivers continuous gas level updates via an LCD display and leverages an IoT framework to ensure seamless integration of sensors, microcontrollers, and mobile devices. This connectivity enables real-time data transmission and remote alerting, enhancing emission management. When pollutant levels surpass predefined safety thresholds, the system triggers immediate alerts through a Global System for Mobile Communications (GSM) module (SIM 900) via SMS to the vehicle owner's mobile, complemented by an audible buzzer. This alert notification encourages timely maintenance, reducing emissions and improving vehicle performance and contributing to cleaner air.

关键词： GSM Microcontrollers Real-time systems Sensor systems Mobile handsets Sensors Safety Nitrogen Gas detectors Monitoring

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Enhancing Protection Mechanisms in data and Application Security Across Diverse Platforms

Enhancing Protection Mechanisms in Data and Application Secu...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nijhawan, Ginni Dasari, Kavitha Chandrashekar, Rakesh Vadivukrassi, M. Kalra, Ravi Abdullah, Sajid Abd Al Khidhir Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India St. Martin's Engineering College AP Dept. of Cse Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Secure data protection is more vital than ever as internet dangers proliferate. Five key approaches comprise this study's comprehensive security design. They are the Advanced Encryption Algorithm (AEA), DIDS, BBA, BEDI, and AAC. Each algorithm strengthens the security design, defending against several attacks. The system relies on the AEA technique to securely encrypt and decode data. It uses complex cryptography algorithms that focus on key management and rapid modular math to protect secret communication. The DIDS algorithm detects unusual system activity in real time to prevent attacks. It can adapt to changing conditions, leverage topic knowledge, and provide extensive intrusion reports, making it stronger at battling security concerns. Fingerprint data improves user identification, making BBA safer and more customizable. Multiple biometric sources and dynamic template changes make the software more biometric trait resistant. BEDI uses blockchain technology to verify data, creating a permanent, public record. The smart contract application and decentralized agreement procedure make data changes tougher. AAC increases access control by altering the barrier based on user behavior and environment. AAC is usercentered and context-aware because it constantly learns and changes. The study found that the proposed framework outperforms competitors in encryption strength, userfriendliness, detection accuracy, integration complexity, flexibility, and regulatory compliance. The powerful and versatile security solution may be utilized in many contexts. © 2024 IEEE.

关键词： Encryption algorithms

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The Interpretability of Invariant Neural Networks in Deep Learning

The Interpretability of Invariant Neural Networks in Deep Le...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Irfan Praveena, K. Manjunatha Dutt, Amit Kiran, D. Sai Fadaam, Taghreed Kadhim Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology College of Pharmacy Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Neural networks and deep learning allow complicated models to be taught to comprehend and accomplish difficult tasks. This study calls into question the model's understandability and invariance. If the AI meets these two standards, you will feel more confident. To address these issues, we recommend the Integrated Invariance Interpretation (I3) paradigm. This strategy assists in the understanding of decision-making in neural networks. The most current I3 system includes the following algorithms: FIA, PIA, and CAM. Each has a unique approach to interpretability and invariance. If principal component analysis (PIA) is employed, a model may stay unchanged while the data changes. Feature impact analysis (FIA) may demonstrate how changes to features affect the model's output. The method known as 'concept-based invariance' allows CAM to recognise complex patterns. Our findings were based on a real-world dataset and extensive testing. This evaluated the feasibility and effectiveness of the I3 framework. In comparison to methodologies that just evaluate certain components, the I3 framework provides a more comprehensive view of model interpretability. Using the I3 architecture, professionals and researchers may create successful AI systems and gain better knowledge of neural network decision-making. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Driver Drowsiness Detection Using Mobile Net for Road Safety Enhancement

Real-Time Driver Drowsiness Detection Using Mobile Net for R...

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Disruptive Technologies (ICDT), International Conference on

作者： Chengamma Chitteti Mallavarapu Sravika Shaik Ayesha Dungavath Sridhar Naik Vangipuram Anil Kumar Muthyamaina Pavan kumar Dept. of Data Science Mohan Babu University (Erst While Sree Vidyaniketan Engineering College) Tirupati A.P India Dept. of CSSE Sree Vidyanikethan Engineering College Tirupati A.P India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Research shows that fatigue ranks within the three main road accident causes worldwide. This paper develops a driver sleep detection system in real time with MobileNet as the light weight convolutional neural network architecture built specifically for indoor and mobile environments. Real-time system facial scans seek driver indicators of drowsiness which include lengthy eye closures and slow yawn activity leading to sleepiness. MobileNet operates at a low cost which makes it both rapid and functional for vehicle implementation without accuracy compromises. The system helps prevent driver fatigue which leads to better vehicle safety because drivers receive instant feedback through this alert model. This solution presents several advantages such as affordability as well as mobility through mobile devices which makes it ideal for current vehicle safety requirements. Fatigue signs to avoid accidents. When drowsiness accelerates the slowdown of reaction times but increases alertness it causes higher risks of vehicle crashes. System implementations that depend on physical signals need drivers to maintain close positions with the equipment which results in driver distraction. We evaluate the driver eye movement behavior to measure driver behavior through imaging-based dataset measurements in this paper. MobileNet eliminates optical images successfully at a low cost thus speeding up the visual fatigue relief process. MobileNet model demonstrated a performance of 94 % in the evaluation results. The system requires 0.00829 seconds to recognize tiredness. The system delivers exceptional real-time functionality thus making it an extraordinary example. Better performance was achieved through the implementation of hyperparameter optimization in connection with k fold validation. The study matters greatly because its implementation would assist in protecting lives by presenting an effective strategy to stop road fatalities arising from unpredictable seizures.

关键词： Visualization Accuracy Road accidents Costs Sleep Vehicle safety Fatigue Real-time systems Road safety Vehicles

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AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

AutoMediVision:A Deep Learning System for Accurate Pharmaceu...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： G Rajeswarappa B. Venkata Sivaiah M. Navya Y. Lasya Shaik Mohammad Sameer V. Varun Kumar Department of CSE G Pulla Reddy Engineering College Kurnool Dept. of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept. of Computer Science and Systems Engineering(CSSE) Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

关键词： Deep learning Accuracy Computational modeling Medical services Mobile handsets Safety Standards Research and development Biomedical imaging Pharmaceuticals

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