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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani V.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning data preprocessing Reinforcement learning Feature extraction data models Predictive analytics Stress

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A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

SSRN

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SSRN 2023年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Czech Republic Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India Departament of Industrial Engineering University of Chile Santiago Chile Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India

Selecting outstanding tech stocks for investment is challenging. Specifically, studying the investment for academic purposes is not mature enough due to the disarray of the publications and the `over' informative experience from profit-making websites. Often some authors entitle stock price prediction subject as a stock selection problem;both prediction and selection are just sub-section of portfolio management. Moreover, stock websites provide tons of potential criteria showing their perspective evaluation to simulate and monitor the stock market professionally, which causes an extra problem for academic studies screening out the less informative criteria. The paper generalizes a novel framework with a user-interactive interface for stock selection problems based on multi-source data fusion and decision-level fusion to enhance reliability that is limited by the narrow criteria performance and to enhance the strength of a model overcoming the weakness of a single-performed model. This framework benefits the time-series prediction and decision-making study. Besides, we propose adopting dynamic time warping to assist a task-learning process by customizing a loss function that improves the accuracy of data prediction. The experiment shows that the proposed method reduces the prediction log error by 28\% in a best case of real-situation data and decreases the warping cost by 20\%. Finally, we illustrate the proposed framework by implementing it in a real-world stock data selection. The results are practical and effective, further justified through a detailed ablation study. © 2023, The Authors. All rights reserved.

关键词： Investments

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Mobile Application for Water Management and Monitoring System in Residential Building 1

Mobile Application for Water Management and Monitoring Syste...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Akhila, Nalla Sirisha, Aswadhati Dass, P. Dinesh, P.M. Sujatha, T. Gaikwad, Amit Kamalakar Aditya Engineering College Surampalem Computer Science and Engineering Jawaharlal Nehru Technological University Kakinada East Godavari district Kakinada India Department Duvvada India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of ECE Chennai India Sona College of Technology Department of ECE Salem India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India G.H.Raisoni University Department of Computer Science Maharashtra Amravati India

ISBN: (纸本)9781665450461

Water scarcity is one of the major threats to human society. Though there are many reasons for water scarcity like water pollution, drought and global warming, overuse of domestic water is one of the major causes of water scarcity. More than one billion people are deprived of clean drinking water and water for other purposes. One of the main solutions that were found to this problem is rainwater harvesting. Many countries encouraged the citizens to collect the rainwater for further uses. Though the rainwater is preserved, another issue named water over usage comes into existence. Most of the time, the water supplied for domestic reasons may undergo several preprocessing like filtration, osmosis, etc. to ensure the cleanliness of the water. When this water is wasted for petty reasons like washing dishes and watering plants, it may lead to the shortage of freshwater for necessary needs like cattle feeding, food production, farming, etc. Many researchers and scientists have found a lot of techniques and processes to minimize the usage of fresh water in households. This project aims the development of a mobile application that constantly monitors the water usage by a house in a community. There will be a prefixed value for the amount of water each house can use. If the water usage exceeds the prefixed value, the inmates of the house are asked to pay a certain fine amount which is calculated by the number of extra liters used and the prefixed fine amount for each liter. If the fine amount was not paid, the water supply to that particular house will be blocked. All of the above-mentioned processes are monitored by this application. Hardware components like multiple sensors, adaptors, motors are used for this purpose. The whole working of the hardware is controlled by a microcontroller namely Arduino Uno. This application helps in the control of the over usage of water in residential buildings. This can be equipped in apartments, community houses, quarters, etc. to constan

关键词： Potable water

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration

Exploring the Effectiveness of Deep Learning in Audio Compre...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： S. Pushparani K. Sashi Rekha V. M. Sivagami R. Usharani M. Jothi Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case.

关键词： Deep learning Training Filtering Bit rate Noise computer architecture Feature extraction

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Position: Explain to Question not to Justify

arXiv

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arXiv 2024年

作者： Biecek, Przemyslaw Samek, Wojciech MI2.AI University of Warsaw Poland MI2.AI Warsaw University of Technology Poland Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area. Copyright © 2024, The Authors. All rights reserved.

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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Calculation of Signal Information Delay in Intelligent Communication networks 3

Calculation of Signal Information Delay in Intelligent Commu...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Kuchuk, Nina Mozhaiev, Mykhailo Kalinin, Yevhen Davydov, Viacheslav Mozhaev, Oleksandr Kuchuk, Heorhii National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine Computer Technology and Telecommunications Researches Sector National Scientific Center «hon. Prof Bokarius Forensic Science Institute» Kharkiv Ukraine National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine Kharkiv National University of Internal Affairs Department of Cyber Security and Data Technologies Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

An analysis of the signaling systems used in the Intelligent Communication Network has been carried out. The main probabilistic characteristics of signal information delay are determined. A method for calculating these characteristics for such error correction methods is proposed: basic error control method and preventive cyclical control method. The analysis of the delay of signal units from loading the signal network is carried out. Simple formulas are proposed for approximating the load applied to the signaling link. © 2022 IEEE.

关键词： Error correction

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Mechanical characterization of extruded sisal-polypropylene composites with optimized compounding parameters

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Interactions 2024年第1期245卷 177-177页

作者： Khawale, Vijay Ramrao Jadhav, Varsha D. Satishkumar, P. Vijayan, D.S. Saminathan, Rajasekaran Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Wanadongri India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

This study aims to optimize the mechanical characteristics of Sisal-polypropylene composites by investigating the impact of varying the compounding factors on these qualities. These parameters include fiber feeding location, barrel temperature, and fiber content. A capillary rheometer was used to track the changes in viscosity of the mixed compounds in relation to shear rate and temperature. Blends were tested at predetermined shear rates using a Couette shear rate theory–based extruder screw speed chart. In order to determine how each processing variable affected the mechanical characteristics of the composites, an analysis of variance (ANOVA) was conducted, and experimental designs were based on Taguchi’s optimization method. The optimization study revealed that, in addition to barrel temperature, the mechanical characteristics of the specimens were highly influenced by fiber content and feeding position inside the extruder. Compounds containing 40 wt % fiber, when treated at 180 ºC and fed into the extruder’s melt zone, had an average modulus value of 7 GPa and a strength value of 48 MPa. The viscosity measurements show minimal variations at shear speeds above 1850 s− 1, making it suitable for analyzing these substances. The scanning electron micrographs (SEM) reveal that the fibers were coated uniformly with polymer, and there was some seepage through the core, resulting in twofold shear effect. The optimal screw speeds for the blends studied here ranged from 75 to 220 rpm when using the present extruder (Do/Di = 1.3 and shear rates 1850 s− 1 to 4750 s− 1). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Polypropylenes

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Random Forests for Predicting Diabetes Progression and Complications

Random Forests for Predicting Diabetes Progression and Compl...

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Advancement in Technology (ICONAT), International Conference for

作者： Chitra Sabapathy Ranganathan R Nandhini K Nisha G. Sumathi G. Sudhakar C. Srinivasan Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Computer Science and Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to $130 / 85 \mathrm{~mm}$ HgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg.

关键词： Body mass index Accuracy Retinopathy databases Blood pressure Diabetes Glucose Biomedical monitoring Insulin Random forests

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Transforming Shopping with IoT: Exploring the Intelligent Way to Design the Smart Trolley using IoT Principles

Transforming Shopping with IoT: Exploring the Intelligent Wa...

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Big data & Machine Learning (ICBDML), IEEE International Conference on

作者： M. Jayaprakash A. Hency Juliet A. Jaya Mabel Rani N.T. Velusudha S. Lakshmisridevi V. Samuthira Pandi Department of Information Technology R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Data Analytics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

The interconnection of commonplace items is revolutionizing people's daily lives, thanks to the Internet of Things (IoT). A smart shopping system, consisting of interconnected products, might one day be a reality in supermarkets. In this Internet of Things system, products may be individually identified using low-cost RFID tags, which, when added to an RFID-enabled smart shopping cart, can be read automatically. This means shoppers won't have to endure the checkout line at all—billing can be handled right in the cart. The integration of RFID-equipped smart shelving into this system allows for the monitoring of inventory and, perhaps, the update of a central server. The suggested shopping system, which is dubbed IoT Assisted Smart Trolley (IoTAST), is cross-validated with the standard Smart Trolley (ST) model to clearly evaluate its performance. Since an RFID reader can automatically read all objects instead of a labourer physically scanning them, inventory management becomes considerably easier with this sort of technology. We construct a working system to test its functioning, determine the design criteria of a sophisticated purchasing system, and then create a reliable communication mechanism that renders the system feasible. This study validates the viability of the system. This is the first smart shopping system that takes security into account that we are aware of.

关键词： Protocols Prototypes Machine learning Reliability engineering Security Internet of Things Servers

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