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intelligent Chatbot for Educational Institutions 7

Intelligent Chatbot for Educational Institutions

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Reddy, Nandigam Srikar Chaitanya, Naini Pranay Varshitha, Panthangi Sai Varma, Rudhraraju Chaitanya Reddy, Patlolla Karthik Dandu, Jagadeesh B V Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9798350372816

Our project introduces an 'intelligent Chatbot for Educational Institutions'. Responding to the dynamic landscape of educational technology, we unveil an advanced full-stack chatbot initiative Designed to overhaul academic administration and student information access. Our chatbot leverages sophisticated rule based systems and traditional programming paradigms to ensure optimal efficiency. This chatbot excels at processing natural language queries and furnishing real-time updates on students' academic progress and accomplishments. A notable application of its capabilities lies in swiftly identifying and presenting information about the top performing student in a given academic year, such as the college valedictorian of 2022. This functionality is made possible by a meticulously designed algorithm that systematically analyses academic records, discerns query context, and extracts pertinent data to deliver precise and valuable insights to users, including educators, students, and parents. With a user-friendly interface, the chatbot ensures accessibility for users of all technical proficiencies. Our approach places a premium on accuracy in information retrieval and continuous refinement through iterative enhancements, enabling the chatbot to adapt seamlessly to the evolving educational landscape. This initiative aims to establish a new standard in educational technology by providing an innovative solution that streamlines information dissemination and enriches the academic journey for stakeholders in the educational realm, all achieved with reliance on ML and DL technologies. © 2024 ieee.

关键词： Educational technology

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Fast Moving Consumer Goods Object Detection Using YOLOV8

Fast Moving Consumer Goods Object Detection Using YOLOV8

引用

2024 ieee international conference on Electronic systems and intelligent computing, ICESIC 2024

作者： Umamageswari, A. Mukund, P.U. Abhishek, V.B. Devi Vaishnavi, Y. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering With Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331522988

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management;together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability. © 2024 ieee.

关键词： Efficiency

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Time-Series Forecasting Using ARIMA and SARIMA Models for Solar NASA POWER data 3

Time-Series Forecasting Using ARIMA and SARIMA Models for So...

引用

3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Patil, Yashwant Shruti, Tanya Rajeswari, K. Pimpri-Chinchwad College of Engineering Pune Department of Computer Science Pune India

ISBN: (纸本)9798331523893

Solar energy forecasting is vital, particularly in energy planning for power utilities, as it provides insights for integrating renewable energy into existing power distribution networks. This study investigates the effectiveness of ARIMA and SARIMA models in forecasting solar radiation (ALLSKY_SFC_SW_DWN) using NASA POWER data from 2019 to 2024. The dataset includes solar irradiance and meteorological parameters critical for renewable energy research. Methodologically, both ARIMA and SARIMA models were implemented and evaluated through R2, MAE, and RMSE metrics, highlighting their capacity to predict temporal trends and seasonal variations. Results demonstrated ARIMA's superior accuracy with an R2 of 0.9343, while SARIMA, incorporating more seasonal factors, achieved an R2 of 0.8664. This work not only compares these models' practical applications but also contributes to solar energy forecasting research by offering insights into their strengths and limitations for decision-making in renewable energy systems. © 2025 ieee.

关键词： Solar irradiance

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Traffic Flow Prediction Using LSTM, ARIMA & SARIMA Model for intelligent Transportation System

Traffic Flow Prediction Using LSTM, ARIMA & SARIMA Model for...

引用

2024 international conference on intelligent computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Verma, Rupal Pandey, Sudhakar Rup, Suvendu NIT Raipur Information Technology Raipur India

ISBN: (纸本)9798350369175

Accurate and timely prediction of traffic data is an essential component in the planning, administration, and control of urban traffic in intelligent Transportation systems (ITS). Our study is dedicated to exploring three distinct models: LSTM (Long Short Term Memory), ARIMA (Autoregressive Integrated Moving Average) with parameters ($p, d, q$) that specify autoregression, differencing, and moving average orders, and SARIMA (Seasonal Autoregressive Integrated Moving Average), intended for analyzing seasonal time series data. ARIMA and SARIMA models are conventional statistical techniques employed for the study of time series data. They provide reliable methodologies for forecasting future traffic patterns through the analysis of historical data. To choose the most efficient model among the three options, we utilize performance measurement procedures to evaluate their predictive abilities. We utilize an external dataset obtained from the U.S. Government Open Repository. This dataset is classified as geographical data. Upon analysis, it was shown that all three SARIMA models had superior performance. © 2024 ieee.

关键词： Prediction models

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Impact of Artificial Intelligence (AI) in Talent acquisition Process: A study with reference to IT Industry

Impact of Artificial Intelligence (AI) in Talent Acquisition...

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2023 ieee international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Yadav, Pravin Vitthal Kollimath, Umesh S. Chavan, Tanaji Vitthal Pisal, Dnyaneshwar Tukaram Giramkar, Sudarshan Arjun Swamy, Shalini M. Dist-Pune Baramati413102 India SVPM's Institute of Management Malegaon BK Tal-Baramati Dist Pune413115 India Parikrama College of Management Dist-Ahmednagar Shrigonda413701 India JSPM's Imperial College of Engineering and Research Wagholi Pune India

ISBN: (纸本)9781665462631

This article identifies the impact of Artificial Intelligence (AI) on the Talent acquisition Process particularly in the IT industry of Pune city. Various aspects of AI were studied to suffice the core objective of this study namely, Screening, Video Interviews, Tech Screening, Sourcing, Scheduling, ATS Re-Engagement, Referrals, and Career Site Conversion. The relevant data of talent acquisition process before AI implementation and After AI implementation was gathered from HR professionals of the IT industry, using a standardized questionnaire. To analyze the demographic profile of the respondents, percentage analysis was performed, and Paired t-test was used to identify the difference in the Talent acquisition process before and after AI implementation. Further, this study revealed that there is a significant difference in the Talent acquisition process before and after AI implementation (p © 2023 ieee.

关键词： Artificial intelligence

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Generative Adversarial Network-Based Network Intrusion Detection System for Supervisory Control and data acquisition System

Generative Adversarial Network-Based Network Intrusion Detec...

引用

2024 ieee international conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Hong Nhung Lan-Phan, Thi Song, Chai-Jong School of Computing Gachon University Department of AI and Software Engineering Gyeonggi-do Seongnam-si1342 Korea Republic of Viet Nam - Korea Institute of Science and Technology Division of Information Technology Thach That Ha Noi Viet Nam Korea Electronics Technology Institute Information Media Research Center Seoul03924 Korea Republic of

ISBN: (纸本)9798331530839

In the context of burgeoning cyber threats targeting Supervisory Control and data acquisition (SCADA) systems, this paper presents a pioneering Intrusion Detection System (IDS) grounded in Generative Adversarial Networks (GANs). The ubiquity of information technology in critical industrial processes necessitates heightened security measures, and the proposed model addresses this imperative through the fusion of GANs and IDS tailored for SCADA environments. The study delves into the evolutionary trajectory of Intrusion Detection systems, tracing their progression from conventional signature-based methods to the adaptive capabilities afforded by artificial intelligence, particularly deep learning. Against this backdrop, the paper investigates the vulnerabilities of SCADA systems, elucidating the sophisticated attack vectors identified in recent studies. The distributed nature of SCADA systems, deployed across extensive industrial landscapes, accentuates the need for robust intrusion detection mechanisms. This research's core innovation lies in applying a GAN model to SCADA systems, leveraging the distributed network protocol version 3 (DNP3) for data access authorization. The model is trained on a dataset comprising 12 meticulously selected features from DNP3 packets, attuning it to the nuances of SCADA network traffic. Notably, the proposed GAN-based IDS demonstrates exceptional efficacy, achieving impressive accuracy. Beyond the empirical contributions, the paper envisions future directions for integrating explainable AI techniques and enhancing the interpretability of IDS models specific to SCADA environments. Moreover, collaboration between academia and industry is advocated to foster the development of comprehensive datasets that represent SCADA network traffic. © 2024 ieee.

关键词： Generative adversarial networks

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Monitoring Real-Time Fleet Emissions Through an intelligent Fleet Management System 27

Monitoring Real-Time Fleet Emissions Through an Intelligent ...

引用

27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Abediasl, Hamidreza Aliramezani, Masoud Koch, Charles Robert Shahbakhti, Mahdi University of Alberta Department of Mechanical Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Monitoring fleet vehicle operation is a crucial aspect of fleet management systems, typically involving the analysis of GPS and/or on-board diagnostics (OBD) data from fleet vehicles. Given the increasing emphasis on environmental responsibility within fleet management, there is a growing demand for monitoring vehicle emissions. This study aims to integrate machine learning models for emission estimation into an intelligent fleet management system (IFMS) designed for light-duty vehicle fleets. These emission models are trained using real-driving data from fleet vehicles and have the capability to provide real-time emission estimates based on the OBD data of each vehicle. The developed IFMS is hardware-agnostic, allowing for wireless data collection, and enables the real-time reporting of fleet emissions, including CO2, CO, HC, and NOx. © 2024 ieee.

关键词： Fleet operations

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Enhanced Multi-Layer Bat Optimization(EMBO) Approach for data acquisition and Security Management in Web Platform 2

Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Dat...

引用

2nd ieee international conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Siva, V.R. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Intrusion Detection systems (IDS) have grown to be essential to network and computer security. However, it complicates the training phase and makes it time-consuming to detect vulnerabilities in data sharing on the network. To solve this problem, we introduced a Multi-Layer Perceptron Neural Network (MLPNN) technique in data science to classify malicious and non-malicious network traffic. In addition, we preprocess the data by normalizing the mean original value using the Z-score Min-Max Scaling (ZSMMS) technique. In addition, we select the best feature by analyzing the weight and bias values with the Improved Bat Optimization (IBO) algorithm. After that, we propose the MLPNN technique to classify network traffic and improve accuracy. Finally, the Optimal Padding advanced Encryption Standard (OPAES) algorithm will be implemented to enhance the security of the website. Additionally, by producing an IDS model and using metrics such as accuracy, recall, time complexity, and security performance, the website's security performance can be improved to 95.7%. © 2024 ieee.

关键词： Network intrusion

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intelligent Monitoring Algorithm for Transmission Lines Based on Lightning Fusion Technology 3

Intelligent Monitoring Algorithm for Transmission Lines Base...

引用

3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Liu, Weigang Liu, Xinmin Chen, Dazhi Lv, Fubin Zhou, Hongze Liu, Yan State Grid Xinjiang Electric Power Co. Ltd. Changji Power Supply Company Changji Xinjiang831100 China

ISBN: (纸本)9798331519032

The traditional safety detection method for transmission lines heavily relies on manual inspection, which is not only inefficient but also limited by personnel experience, environmental factors, and inspection cycles. It is difficult to achieve real-time and accurate identification and warning of transmission line anomalies, and has significant limitations and safety hazards. In recent years, lightning fusion technology has emerged as an innovative solution that cleverly combines the high-precision ranging and speed measurement capabilities of advanced radar detection technology with the intuitive image recognition advantages of video surveillance technology. Through complex data processing algorithms, it achieves deep fusion and intelligent analysis of the two data. This article explores an intelligent monitoring algorithm for transmission lines based on lightning vision fusion technology, and proposes a monitoring system that combines deep learning (DL) algorithms to automatically capture and analyze environmental changes around transmission lines, significantly improving the timeliness and accuracy of transmission line monitoring. The experimental results show that this system not only significantly reduces manual intervention and operation costs, but also effectively avoids missed and false alarms, providing a more solid technical guarantee for the safe and stable operation of the power system (EPS). © 2024 ieee.

关键词： Alarm systems

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IoT Applications in Smart Building Energy Management

IoT Applications in Smart Building Energy Management

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Ravichandra, Thangjam Karumudi, Madhavi Maheswari, M. Uma Leela, S. Thamizhkani, B. Kanniyappan, S.P. Alliance School of Business Alliance University Karnataka Bengaluru562106 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Bowrampet Telangana Hyderabad500043 India Rajalakshmi Engineering College Department of Mechatronics Thandalam Tamil Nadu Chennai602105 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India R.M.K. Engineering College Department of Civil Engineering Tamil Nadu Kavaraipettai601206 India

ISBN: (纸本)9798331543617

The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monitoring, analysis, and management of energy consumption are the goals of this. data is gathered from various sources, such as appliances, lighting, HVAC (heating, ventilation, and air conditioning), and other systems, employing networked systems. This offers the chance for a more effective distribution of energy. The goal of the project is to foresee energy patterns and automatically adjust settings by focussing on the integration of cloud computing, big data analytics, and machine learning algorithms. Furthermore, it highlights how the Internet of Things can help cut down on energy waste and improve sustainability by giving consumers advice and information on how to use less energy. Providing consumers with insights and recommendations helps achieve this. By promoting the adoption of more ecologically friendly energy practices, the implementation of these technologies not only reduces operating costs but also decreases the environmental impact that buildings have. The study's conclusions indicate that the field of smart buildings has advanced significantly, which may help to promote sustainability and higher energy efficiency. © 2024 ieee.

关键词： Green buildings

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