2023 3rd International Conference on Intelligent Technologies (CONIT)最新文献_第8页

Creativity index calculation with question answering system using BERT model 基于BERT模型的问答系统创造力指数计算

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205798

Abhinav Nandgaonkar, S. Mane, V. Khatavkar

{"title":"Creativity index calculation with question answering system using BERT model","authors":"Abhinav Nandgaonkar, S. Mane, V. Khatavkar","doi":"10.1109/CONIT59222.2023.10205798","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205798","url":null,"abstract":"This system proposes a question-answer model to analyze answers given by the users to the open-ended question. For this system, we use a squad dataset. The squad dataset is helpful for open-ended questions. The model uses NLP and the BERT question-answer model for handling the answers. Problem-solving deals with the analysis of questions and answering them in a creative, feasible, and efficient way. The problem-solving the approach gives the way of thinking of the problem solver. Computational methods along with NLP are being developed to calculate these indices. With the help of question-answer systems in NLP, the users’ answers are analyzed and measured are being attempts to perform such analysis but the answers to the questions are varied from person to person which makes answer analysis challenging. Answers given by users and answer for the question present in the squad dataset is compared by using cosine similarity to calculate the creativity index. The creativity index is a linear combination of fluency, flexibility, uniqueness, and elaboration indices; calculated for every given answer by comparing given answers and the stored answers. The system is trained and tested for the general questions present in the squad dataset. This system is beneficial to predict and measure the creative thinking, problem-solving ability, and thinking ability of the user. It helps to build the work culture, societal culture, and behavior of individuals in the institution, community, and workplace. The CI is a tremendously useful tool for businesses, office employees, and students. Thinking and problem-solving skills are calculated using this technique.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132593946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Trading in the Internet of Energy using Ethereum Smart Contracts and Smart Energy Meters 使用以太坊智能合约和智能电表的能源互联网能源交易

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205731

Appasani Prem Sai, Aashish Kumar Bohre

{"title":"Energy Trading in the Internet of Energy using Ethereum Smart Contracts and Smart Energy Meters","authors":"Appasani Prem Sai, Aashish Kumar Bohre","doi":"10.1109/CONIT59222.2023.10205731","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205731","url":null,"abstract":"The emergence of the Internet of Energy (IoE) has paved the way for decentralized energy trading, which involves the exchange of energy among prosumers (both producers and consumers) in a peer-to-peer (P2P) fashion. Smart energy meters, which can measure the energy consumption and production of prosumers, play a critical role in IoE-based energy trading. However, the lack of trust and transparency among prosumers and energy traders is a major barrier to the widespread adoption of P2P energy trading. In this context, Ethereum smart contracts can provide a solution by enabling transparent and secure execution of energy trading agreements among prosumers. This paper proposes an energy trading system that leverages Ethereum smart contracts and smart energy meters to enable P2P energy trading in the IoE. This work describes the architecture of the system and the implementation details of the smart contracts used for energy trading. This paper has presented a case pseudo code for an efficient and secure approach to trade energy based on Ethereum. It moved most of the processing and storage off-chain (in opposition to several existing solutions) to minimize the cost of using Blockchain, in terms of gas paid to process and store data related to smart. This was done while keeping the trading process as secure as when all processing and storage are performed on-chain. It also made use of stable coins to overcome the exchange rate instability of cryptocurrencies.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132697840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis and Prediction of Liver Cirrhosis Using Machine Learning Algorithms 使用机器学习算法分析和预测肝硬化

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205700

Lalithesh D Sawant, Raghavendra Ritti, Harshith N, Ashwini Kodipalli, T. Rao, R. B R

引用次数: 0

From Clicks to Carts: Developing an Autonomous E-Grocery Shopping System 从点击到购物车:开发一个自主的电子杂货购物系统

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205810

Gaurisha R Srivastava, Pooja Gera, Nishtha Goyal, Arun Sharma, Ritu Rani

{"title":"From Clicks to Carts: Developing an Autonomous E-Grocery Shopping System","authors":"Gaurisha R Srivastava, Pooja Gera, Nishtha Goyal, Arun Sharma, Ritu Rani","doi":"10.1109/CONIT59222.2023.10205810","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205810","url":null,"abstract":"Our research initiative, the Autonomous E-Grocery Shopping System with Machine Learning, is centered on the creation of an autonomous grocery shopping experience. One of the main predicaments with online shopping is the lack of personalization and support that is typically offered during in-person shopping. In physical supermarkets, products that are often purchased together are grouped together, incentivizing consumers to buy more and consequently increasing sales. We have incorporated this concept into our e-grocery shopping system to develop a platform that recommends items to users that they may not have been aware they needed. Our research focuses on the crucial role of autonomous artificial intelligence, with an all-encompassing assessment of various state-of-the-art techniques employed in the development of autonomous AI. Through our work, we have created a robust recommendation model utilizing the Apriori Algorithm of association rule mining, and Collaborative Filtering with the Nearest Neighbor’s algorithm. We have identified four major use cases, which include recommending grocery items based on users’ past purchase history, purchase history of similar users, similar items in the users’ cart, and recommending the highest-rated grocery items. We have also created a customized dataset and supported our model using a web application. The average value of support, confidence and lift for all the association rules are 0.001580, 0.124178, and 4.220583 respectively.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities 使用智能城市图像处理技术对人类移动数据和绿色覆盖进行数据驱动的可视化分析

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205381

Sivasubramanian Ramanathan, Kulsoom Syed, Tejas Chavan

{"title":"Data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities","authors":"Sivasubramanian Ramanathan, Kulsoom Syed, Tejas Chavan","doi":"10.1109/CONIT59222.2023.10205381","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205381","url":null,"abstract":"This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115175942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Criminal Activities From CCTV by using Object Detection and machine Learning Algorithms 利用目标检测和机器学习算法从闭路电视中检测犯罪活动

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205699

Surbhi Singla, Raman Chadha

{"title":"Detecting Criminal Activities From CCTV by using Object Detection and machine Learning Algorithms","authors":"Surbhi Singla, Raman Chadha","doi":"10.1109/CONIT59222.2023.10205699","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205699","url":null,"abstract":"Now, a day’s Crime in every country is increasing day by day. Generally, Every day we listen to the news of different crimes of different categories like rape, assault, Kidnapping ,Robbery ,ATM Theft, Murders etc happening in different states, cities , countries. Almost all the newspapers, TV channels’, social media are filled with the news of Criminal activities happening all around the Whole World. In earlier times there is no method to detect Crime. After That the CCTV cameras were used to detect Crimes. But Watching these Videos manually by humans for detecting crimes is a very time Consuming process especially in today’s world of Artificial Intelligence and Machine learning .So this crime detection in CCTV surveillance becomes an important area of research in the field of machine learning. So, there is a very urgent need of the intelligent system which will detect the crimes from the real time CCTV Feed and classify them and provides an alert system to the nearest police stations and ambulances etc. So, that system will help in reducing the crime rate in any country. This paper reviews all prior research in this area, including approaches for object recognition and finding priority frames, techniques and algorithms like Yolo used to detect crimes , various datasets used and algorithms used to analyze crime data and train the dataset .It covers the various recent trends in researches in this field and analyzing the challenges faced and various research gaps and this paper also discuss how we can overcome these gaps in research so as to develop a better intelligence surveillance system in ml field.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114154329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emerging of Machine Learning and Deep Learning Technology: Addressing in Intelligent Wireless Network Optimization 机器学习和深度学习技术的兴起:智能无线网络优化中的寻址

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205650

Kaleab Hailemariam, Gurpreet Singh, Mariam Khamis Madata, Amemou Franck Elyse Yao, Jaspreet Singh

{"title":"Emerging of Machine Learning and Deep Learning Technology: Addressing in Intelligent Wireless Network Optimization","authors":"Kaleab Hailemariam, Gurpreet Singh, Mariam Khamis Madata, Amemou Franck Elyse Yao, Jaspreet Singh","doi":"10.1109/CONIT59222.2023.10205650","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205650","url":null,"abstract":"Wireless networks have grown into an essential component of modern life as a result of the proliferation of wireless technology over what has been a decade. The science underlying machine learning (ML) focuses on encouraging computers to act rather than additional programming. Throughout the prior ten years, machine learning has been helping us develop autonomous transportation systems, usable speech detection, effective web searches, and a much greater awareness of the genetic code of people. Deep learning (DL) is a breakthrough technology that makes automatically and self-sufficient managing networks possible. Incorporating DL creativity into wireless networks has the possibility of helping improve the efficiency of systems in instantaneously as well as replace the manually performed strategies currently required in engineers typically do the following-intensive network management obligations. This paper presents an essential understanding of during which exactly the superiority of ML based approach originates from compared to the traditional modeling based strategies by carefully reviewing recent attempts in employing DL for addressing wireless network optimization challenges. Along with discovering comparisons and difficulties, the fundamental research difficulties including a few prospective research areas for completely realizing the possibility of ML in wireless network optimization have also been highlighted. At last, learning-related comparisons between machine learning as well as deep learning are made.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117287675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Analysis of 6T SRAM Implementation upon Dual Gate Junctionless FET 基于双栅无结场效应晶体管的6T SRAM设计与分析

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205575

Akshaya Adlakha, Waqar Hussain, Leo Raj Solay, S. Intekhab Amin, S. Anand, Pradeep Kumar

引用次数: 0

Machine Learning based Classifier Models for Detection of Celestial Objects 基于机器学习的天体探测分类器模型

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205666

Vikrant Sharma, Sandeep Goel, A. Jain, Amit Vajpayee, Rahul Bhandari, R. Tiwari

引用次数: 0

LSTM Algorithm for the Detection of Mental Stress in EEG 脑电精神压力检测的LSTM算法

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205636

Dipali Dhake, Kunal Gaikwad, Shreyas Gunjal, Sanket Walunj

{"title":"LSTM Algorithm for the Detection of Mental Stress in EEG","authors":"Dipali Dhake, Kunal Gaikwad, Shreyas Gunjal, Sanket Walunj","doi":"10.1109/CONIT59222.2023.10205636","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205636","url":null,"abstract":"Stress is a prevalent mental health issue that can lead to severe consequences if not addressed properly. In recent years, electroencephalography (EEG) signals have gained attention for stress detection. However, most existing approaches rely on pre-processed features, which can be time-consuming and may not capture all the relevant information in the EEG signals.In this paper, we proposed a novel deep-learning approach for real-time stress detection using raw EEG signals. Our approach utilizes a long short-term memory (LSTM) network to automatically capture features and classify the stress level. Our method allows for capturing all the relevant information in the EEG signals, without the need for manual feature engineering.We evaluated our approach on the DEAP dataset, which includes EEG signals from 32 subjects under various emotional states. Experimental results demonstrate that our approach achieves state-of-the-art performance in stress detection, with an accuracy of approximately 94%. Our proposed approach has the potential for real-world applications, such as stress management in the workplace and mental health monitoring in clinical settings.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123031935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0