The increase in congestion on traffic lanes is a major problem hindering the development of an urban city. The reason for this is the increasing number of vehicles on roads leading to large time delays on traffic intersections. To overcome this problem and to make traffic control systems dynamic, several methods and techniques have been introduced throughout the years. The static traffic control systems worked on fixed timings which were allocated to each traffic lane and were not able to be altered. Also, there was no provision for counting and detection of pedestrians on the zebra crossings as well as the detection of emergency vehicles in traffic. We will explore several machine learning and deep learning models for the detection of vehicles and pedestrians in this review article, evaluate their viability in terms of cost, dependability, accuracy, and efficiency, and add some new features to improve the performance of the current system.
{"title":"Smart Traffic Management Using Transfer Learning Approach for Improve Urban Mobility","authors":"Jenil Gohil, Yuvraj Chauhan, Dhaval Nimavat","doi":"10.32628/cseit2490217","DOIUrl":"https://doi.org/10.32628/cseit2490217","url":null,"abstract":"The increase in congestion on traffic lanes is a major problem hindering the development of an urban city. The reason for this is the increasing number of vehicles on roads leading to large time delays on traffic intersections. To overcome this problem and to make traffic control systems dynamic, several methods and techniques have been introduced throughout the years. The static traffic control systems worked on fixed timings which were allocated to each traffic lane and were not able to be altered. Also, there was no provision for counting and detection of pedestrians on the zebra crossings as well as the detection of emergency vehicles in traffic. We will explore several machine learning and deep learning models for the detection of vehicles and pedestrians in this review article, evaluate their viability in terms of cost, dependability, accuracy, and efficiency, and add some new features to improve the performance of the current system.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"17 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239209","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}
Classification on real world database is an important task in data mining. Many classification algorithms can build model only for data in single flat file as input, whereas most of real-world data bases are stored in multiple tables and managed by relational database systems. As conversion of relational data from multiple tables into a single flat file usually causes many problems, development of multi relational classification algorithms becomes popular area of research interests. Relational database based multi relational classification algorithms aim to build a model that can predict class label of unknown tuple with the help of background table knowledge. This method keeps database in it normalized form without distorting structure of database. This paper presents survey of existing multi relational classification algorithms based on relational database.
{"title":"A Survey on Relational Database Based Multi Relational Classification Algorithms","authors":"Komal Shah, Kajal S Patel","doi":"10.32628/cseit2390656","DOIUrl":"https://doi.org/10.32628/cseit2390656","url":null,"abstract":"Classification on real world database is an important task in data mining. Many classification algorithms can build model only for data in single flat file as input, whereas most of real-world data bases are stored in multiple tables and managed by relational database systems. As conversion of relational data from multiple tables into a single flat file usually causes many problems, development of multi relational classification algorithms becomes popular area of research interests. Relational database based multi relational classification algorithms aim to build a model that can predict class label of unknown tuple with the help of background table knowledge. This method keeps database in it normalized form without distorting structure of database. This paper presents survey of existing multi relational classification algorithms based on relational database.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"75 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238479","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}
Varad Sham Kulkarni, Bhairo Amankumar Jaibir, Patel Rudra Nayneshkumar, Vraj Pujara, R. V. Chander, Himadri Vegad
An advanced vehicle monitoring, and seat availability system is designed to monitor the vehicles from any source location A to destination location B in real time to the passengers. The proposed system would make good use of modern technologies via leveraging ultrasonic sensors, cloud API for example Thing Speak, and in-house Word Press software. The system track’s location, speed, and passenger count in real-time. This data fuels optimized scheduling and route planning, maximizing seat occupancy, and boosting overall productivity. A user-friendly dashboard visualizes vehicle activity within designated time slots, empowering faculty, and administrators with data-driven insights for improved resource allocation and scheduling. By comprehensively monitoring location, speed, and passenger count, the system ensures efficient Electric Vehicle operation within the campus confines, ultimately revolutionizing campus transportation and maximizing resource utilization.
设计了一个先进的车辆监控和座位可用性系统，用于实时监控从任何来源地 A 到目的地 B 的车辆，并向乘客提供信息。通过利用超声波传感器、云 API（例如 Thing Speak）和内部 Word Press 软件，拟议的系统将很好地利用现代技术。该系统可实时跟踪位置、速度和乘客人数。这些数据有助于优化调度和路线规划，最大限度地提高座位占用率，提高整体生产率。用户友好型仪表板可直观显示指定时段内的车辆活动，使教师和管理人员能够获得数据驱动的洞察力，从而改进资源分配和调度。通过全面监控位置、速度和乘客人数，该系统可确保电动车辆在校园范围内高效运行，最终彻底改变校园交通，最大限度地提高资源利用率。
{"title":"Enhancing Campus Connectivity: A Smart Intra-Transit Strategy for Efficient Vehicle Throughput","authors":"Varad Sham Kulkarni, Bhairo Amankumar Jaibir, Patel Rudra Nayneshkumar, Vraj Pujara, R. V. Chander, Himadri Vegad","doi":"10.32628/cseit2490218","DOIUrl":"https://doi.org/10.32628/cseit2490218","url":null,"abstract":"An advanced vehicle monitoring, and seat availability system is designed to monitor the vehicles from any source location A to destination location B in real time to the passengers. The proposed system would make good use of modern technologies via leveraging ultrasonic sensors, cloud API for example Thing Speak, and in-house Word Press software. The system track’s location, speed, and passenger count in real-time. This data fuels optimized scheduling and route planning, maximizing seat occupancy, and boosting overall productivity. A user-friendly dashboard visualizes vehicle activity within designated time slots, empowering faculty, and administrators with data-driven insights for improved resource allocation and scheduling. By comprehensively monitoring location, speed, and passenger count, the system ensures efficient Electric Vehicle operation within the campus confines, ultimately revolutionizing campus transportation and maximizing resource utilization.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"5 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241100","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}
This research paper presents a novel approach to training an AI agent for walking and survival tasks using reinforcement learning (RL) techniques. The primary research question addressed in this study is how to develop an AI system capable of autonomously navigating diverse terrains and environments while ensuring survival through adaptive decision-making. To investigate this question, we employ RL algorithms, specifically deep Q-networks (DQN) and proximal policy optimization (PPO), to train an AI agent in simulated environments that mimic real-world challenges. Our methodology involves designing a virtual environment where the AI agent learns to walk and make survival-related decisions through trial and error. The agent receives rewards or penalties based on its actions, encouraging the development of strategies that optimize both locomotion and survival skills. We evaluate the performance of our approach through extensive experimentation, testing the AI agent's adaptability to various terrains, obstacles, and survival scenarios.
{"title":"Walking and Survival AI Using Reinforcement Learning - Simulation","authors":"Bharate Nandan Lahudeo, Makarand Vayadande, Rohit Malviya, Atharva Haldule","doi":"10.32628/cseit2390629","DOIUrl":"https://doi.org/10.32628/cseit2390629","url":null,"abstract":"This research paper presents a novel approach to training an AI agent for walking and survival tasks using reinforcement learning (RL) techniques. The primary research question addressed in this study is how to develop an AI system capable of autonomously navigating diverse terrains and environments while ensuring survival through adaptive decision-making. To investigate this question, we employ RL algorithms, specifically deep Q-networks (DQN) and proximal policy optimization (PPO), to train an AI agent in simulated environments that mimic real-world challenges. Our methodology involves designing a virtual environment where the AI agent learns to walk and make survival-related decisions through trial and error. The agent receives rewards or penalties based on its actions, encouraging the development of strategies that optimize both locomotion and survival skills. We evaluate the performance of our approach through extensive experimentation, testing the AI agent's adaptability to various terrains, obstacles, and survival scenarios. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140244694","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}
An important topic in the field of natural language processing is the measurement of sentence similarity. It's important to precisely gauge how similar two sentences are. Existing methods for determining sentence similarity challenge two problems Because sentence level semantics are not explicitly modelled at training, labelled datasets are typically small, making them insufficient for training supervised neural models; and there is a training-test gap for unsupervised language modelling (LM) based models to compute semantic scores between sentences. As a result, this task is performed at a lower level. In this paper, we suggest a novel paradigm to handle these two concerns by robotics method framework. The suggested robotics framework is built on the essential premise that a sentence's meaning is determined by its context and that sentence similarity may be determined by comparing the probabilities of forming two phrases given the same context. In an unsupervised way, the proposed approach can create high-quality, large-scale datasets with semantic similarity scores between two sentences, bridging the train-test gap to a great extent. Extensive testing shows that the proposed framework does better than existing baselines on a wide range of datasets.
{"title":"Contextual Sentence Similarity from News Articles","authors":"Nikhil Chaturvedi, Jigyasu Dubey","doi":"10.32628/cseit2390628","DOIUrl":"https://doi.org/10.32628/cseit2390628","url":null,"abstract":"An important topic in the field of natural language processing is the measurement of sentence similarity. It's important to precisely gauge how similar two sentences are. Existing methods for determining sentence similarity challenge two problems Because sentence level semantics are not explicitly modelled at training, labelled datasets are typically small, making them insufficient for training supervised neural models; and there is a training-test gap for unsupervised language modelling (LM) based models to compute semantic scores between sentences. As a result, this task is performed at a lower level. In this paper, we suggest a novel paradigm to handle these two concerns by robotics method framework. The suggested robotics framework is built on the essential premise that a sentence's meaning is determined by its context and that sentence similarity may be determined by comparing the probabilities of forming two phrases given the same context. In an unsupervised way, the proposed approach can create high-quality, large-scale datasets with semantic similarity scores between two sentences, bridging the train-test gap to a great extent. Extensive testing shows that the proposed framework does better than existing baselines on a wide range of datasets. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242879","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}
Mansi Chauhan, Vraj Limbachiya, Naisargi Shah, Riya Shah, Yassir Farooqui
Safe-Pass presents a user-friendly and secure solution for simplifying digital access. With a downloadable application that operates seamlessly across your devices, it eliminates the inconvenience of traditional passwords. The process begins with accessing the Master password app through a distinctive image-based authentication. Operating inconspicuously in the background, the app not only enhances the strength of your existing passwords but also manages and facilitates automatic logins. This system offers adaptable security options, enabling swift access through a single factor or heightened security through the combination of multiple factors. Addressing the persistent threat of phishing, wherein sensitive user information is compromised, we introduce an innovative approach leveraging Visual Cryptography and Steganography for enhanced online security. Our method involves the application of Visual Cryptography to confidential credentials, generating two shares. One share is stored on the server, while the other is concealed within a reCAPTCHA image or a user-defined image through Steganography. During login attempts, users provide their username along with the reCAPTCHA image or chosen image. Successful authentication grants access, while repeated failed attempts trigger email notifications. Master Login prioritizes user privacy, safeguarding passwords as individual and exclusive data. Data sharing or selling is never practiced, ensuring the confidentiality of user information.
{"title":"SafePass : Reinventing Digital Access with Visual Cryptography, Steganography, and Multi-Factor Authentication","authors":"Mansi Chauhan, Vraj Limbachiya, Naisargi Shah, Riya Shah, Yassir Farooqui","doi":"10.32628/cseit2490214","DOIUrl":"https://doi.org/10.32628/cseit2490214","url":null,"abstract":"Safe-Pass presents a user-friendly and secure solution for simplifying digital access. With a downloadable application that operates seamlessly across your devices, it eliminates the inconvenience of traditional passwords. The process begins with accessing the Master password app through a distinctive image-based authentication. Operating inconspicuously in the background, the app not only enhances the strength of your existing passwords but also manages and facilitates automatic logins. This system offers adaptable security options, enabling swift access through a single factor or heightened security through the combination of multiple factors. Addressing the persistent threat of phishing, wherein sensitive user information is compromised, we introduce an innovative approach leveraging Visual Cryptography and Steganography for enhanced online security. Our method involves the application of Visual Cryptography to confidential credentials, generating two shares. One share is stored on the server, while the other is concealed within a reCAPTCHA image or a user-defined image through Steganography. During login attempts, users provide their username along with the reCAPTCHA image or chosen image. Successful authentication grants access, while repeated failed attempts trigger email notifications. Master Login prioritizes user privacy, safeguarding passwords as individual and exclusive data. Data sharing or selling is never practiced, ensuring the confidentiality of user information. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"16 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242888","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}
Feature selection is one of the important data pre-processing techniques that are used to increase the performance of machine learning models, to build faster and more cost-effective algorithms, and to make it easier to interpret the predictions made by the models. The main objective of this research work is to investigate the influence features to predict particulate matter (PM10). This research uses 24-hour average pollutant concentration data of 36 air quality monitoring stations provided by Gandhinagar Smart City Development Limited (GSCDL), Gandhinagar, Gujarat. Important features were identified using five feature selection techniques (correlation, forward selection, backward elimination, Exhaustive Feature Selection (EFS), and feature importance derived using Random Forest Regressor). With selected features six regression algorithms (Multiple Linear Regression, Random Forest, Decision Tree, K-nearest Neighbour, XGBoost, and Support Vector Regressor) were trained to predict PM10. Further, the models were compared based on the Root Mean Square Error (RMSE) and Coefficient of determination (R2) parameters to identify the model with good performance. This proposed model can be utilized as an early warning system, providing air quality information to local authorities to develop air-quality improvement initiatives.
{"title":"Evaluation of the Effectiveness of Feature Selection Methods Combined with Regression Algorithms to Predict Particulate Matter (PM10) in Gandhinagar, Gujarat, India","authors":"Zalak L. Thakker, Sanjay H. Buch","doi":"10.32628/cseit2390641","DOIUrl":"https://doi.org/10.32628/cseit2390641","url":null,"abstract":"Feature selection is one of the important data pre-processing techniques that are used to increase the performance of machine learning models, to build faster and more cost-effective algorithms, and to make it easier to interpret the predictions made by the models. The main objective of this research work is to investigate the influence features to predict particulate matter (PM10). This research uses 24-hour average pollutant concentration data of 36 air quality monitoring stations provided by Gandhinagar Smart City Development Limited (GSCDL), Gandhinagar, Gujarat. Important features were identified using five feature selection techniques (correlation, forward selection, backward elimination, Exhaustive Feature Selection (EFS), and feature importance derived using Random Forest Regressor). With selected features six regression algorithms (Multiple Linear Regression, Random Forest, Decision Tree, K-nearest Neighbour, XGBoost, and Support Vector Regressor) were trained to predict PM10. Further, the models were compared based on the Root Mean Square Error (RMSE) and Coefficient of determination (R2) parameters to identify the model with good performance. This proposed model can be utilized as an early warning system, providing air quality information to local authorities to develop air-quality improvement initiatives.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"8 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242109","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}
The AI-driven IPC Section Prediction for Crime Classification project is a groundbreaking initiative with far- reaching implications for the legal and law enforcement sectors in India. Traditional crime classification and the assignment of the appropriate IPC section are often time-consuming and prone to human error. Our web application addresses these challenges by offering an efficient, accurate, and user-friendly solution. One of the key strengths of our application lies in its adaptability. It can process a wide range of crime descriptions, including those involving complex legal language or colloquial terms, ensuring its utility in diverse scenarios. Additionally, our system is designed to continuously learn and evolve. It adapts to changes in legal terminology, updates in the IPC, and emerging crime trends, thereby maintaining its relevance and precision over time. The social impact of this project cannot be overstated. By streamlining crime classification, it empowers law enforcement agencies to allocate resources more efficiently and prioritize cases based on severity and relevance. It also aids legal professionals by expediting case preparation and documentation. Moreover, it facilitates greater public engagement with the legal system, enabling citizens to better understand and navigate the complexities of the IPC. In conclusion, our AI-driven IPC Section Prediction web application is a pioneering tool that has the potential to revolutionize crime classification and legal processes. Its adaptability, continuous improvement, and positive societal impact make it an asset for law enforcement, legal practitioners, and the general public alike.
{"title":"Justice : A Predicting Criminal Acts According To IPC Section","authors":"Gaurav Varshney, Modi Manankumar R, Rajesh Maheshwari, Tirth Chhabhaiya Chhabhaiya, Bikram Kumar","doi":"10.32628/cseit2490215","DOIUrl":"https://doi.org/10.32628/cseit2490215","url":null,"abstract":"The AI-driven IPC Section Prediction for Crime Classification project is a groundbreaking initiative with far- reaching implications for the legal and law enforcement sectors in India. Traditional crime classification and the assignment of the appropriate IPC section are often time-consuming and prone to human error. Our web application addresses these challenges by offering an efficient, accurate, and user-friendly solution. One of the key strengths of our application lies in its adaptability. It can process a wide range of crime descriptions, including those involving complex legal language or colloquial terms, ensuring its utility in diverse scenarios. Additionally, our system is designed to continuously learn and evolve. It adapts to changes in legal terminology, updates in the IPC, and emerging crime trends, thereby maintaining its relevance and precision over time. The social impact of this project cannot be overstated. By streamlining crime classification, it empowers law enforcement agencies to allocate resources more efficiently and prioritize cases based on severity and relevance. It also aids legal professionals by expediting case preparation and documentation. Moreover, it facilitates greater public engagement with the legal system, enabling citizens to better understand and navigate the complexities of the IPC. In conclusion, our AI-driven IPC Section Prediction web application is a pioneering tool that has the potential to revolutionize crime classification and legal processes. Its adaptability, continuous improvement, and positive societal impact make it an asset for law enforcement, legal practitioners, and the general public alike.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249759","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}
Priyesha Patel, Ayushi Falke, Dipen Waghela, Shah Vishwa
In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Also discussed were the distinctions between various machine learning and deep learning approaches. Finally, it will point the way for future study in Indian spoken language recognition, as well as AI technology.
{"title":"Utilizing Deep Learning Techniques for the Classification of Spoken Languages in India","authors":"Priyesha Patel, Ayushi Falke, Dipen Waghela, Shah Vishwa","doi":"10.32628/cseit2390556","DOIUrl":"https://doi.org/10.32628/cseit2390556","url":null,"abstract":"In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Also discussed were the distinctions between various machine learning and deep learning approaches. Finally, it will point the way for future study in Indian spoken language recognition, as well as AI technology. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"24 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252684","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}
This article introduces an innovative approach leveraging a combination of machine learning techniques to enhance early diabetes detection, a crucial step given the disease's global impact. With the prevalence of sugar and fats in contemporary diets contributing to an increased diabetes risk, early identification through symptom recognition is key. The proposed method integrates Using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms, patient data is analyzed to classify diabetes diagnoses as either affirmative or negative. The study involves the utilization of a dataset that has been divided into 70% for training data and 30% for testing data. The outputs from the SVM and ANN models serve as inputs for a fuzzy logic system, which then makes the final diagnosis determination. This hybrid model is stored on a cloud platform for accessibility and uses real-time patient data for predictions. The combined machine learning model demonstrates superior accuracy in predicting diabetes compared to existing methods.
本文介绍了一种利用机器学习技术组合加强早期糖尿病检测的创新方法，鉴于糖尿病对全球的影响，这是至关重要的一步。现代饮食中糖和脂肪的普遍存在增加了糖尿病的风险，因此通过症状识别进行早期识别至关重要。所提出的方法整合了支持向量机（SVM）和人工神经网络（ANN）算法，对患者数据进行分析，将糖尿病诊断分为肯定和否定两种。研究使用的数据集分为 70% 的训练数据和 30% 的测试数据。SVM 和 ANN 模型的输出作为模糊逻辑系统的输入，然后由模糊逻辑系统做出最终诊断判断。该混合模型存储在云平台上，可用于访问，并使用病人的实时数据进行预测。与现有方法相比，混合机器学习模型在预测糖尿病方面表现出更高的准确性。
{"title":"Diabetes Prediction with Machine Learning with Python","authors":"S. R. Kumar, Kruthi. G, V. Supraja","doi":"10.32628/cseit2390651","DOIUrl":"https://doi.org/10.32628/cseit2390651","url":null,"abstract":"This article introduces an innovative approach leveraging a combination of machine learning techniques to enhance early diabetes detection, a crucial step given the disease's global impact. With the prevalence of sugar and fats in contemporary diets contributing to an increased diabetes risk, early identification through symptom recognition is key. The proposed method integrates Using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms, patient data is analyzed to classify diabetes diagnoses as either affirmative or negative. The study involves the utilization of a dataset that has been divided into 70% for training data and 30% for testing data. The outputs from the SVM and ANN models serve as inputs for a fuzzy logic system, which then makes the final diagnosis determination. This hybrid model is stored on a cloud platform for accessibility and uses real-time patient data for predictions. The combined machine learning model demonstrates superior accuracy in predicting diabetes compared to existing methods. ","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"30 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252270","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}
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