Computational Intelligence and Machine Learning最新文献

{"title":"A Review of Text Summarization Techniques Using NLP","authors":"Kartik Aggarwal","doi":"10.36647/ciml/04.02.a001","DOIUrl":"https://doi.org/10.36647/ciml/04.02.a001","url":null,"abstract":"Techniques that employ natural language processing (NLP), often known as text summarizing, automatically construct summaries of extensive texts. Extractive and abstractive summarization are two main categories that may be used to classify these methods. In extractive summarizing, the most significant lines or phrases from a text are isolated and used to generate a summary. On the other hand, in abstractive summarization, a summary is generated that is clear, short, and accurate in its representation of the text's primary concepts. NLP methods like sentence segmentation, part-of-speech tagging, named entity recognition, and semantic analysis are used in generating a summary from a text and locating and extracting relevant information from the text. Text summarizing is a subject that has received a significant amount of study and has applications in various fields, including the summation of news articles, documents, and emails, among other things.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139319442","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}

{"title":"An Overview of Speech-To-Text Conversion","authors":"Kartik Aggarwal","doi":"10.36647/ciml/04.02.a003","DOIUrl":"https://doi.org/10.36647/ciml/04.02.a003","url":null,"abstract":"As a result of developments in science and technology, an automatic speech-to-text (STT) conversion system has been available. This system converts spoken words into text that can be read visually. People with trouble hearing may use this technology to communicate in other ways, including understanding voice communication and being able to follow directions using their visual abilities. There are instances when seeing something is more powerful than listening to something, particularly in long-distance communication; thus, speech-to-text conversion is crucial in situations like these. One of the fascinating developments to occur in the twenty-first century is the advent of machine learning. It has evolved from its roots in neurology studies conducted in the 1940s into something like artificial intelligence humans have created. Neural networks, a collection of complex structures, are the basis of machine learning. When combined with optimization techniques, these networks mimic the behaviour of neurons in the human brain and allow a computer to learn from its experiences. Here we explore one of many potential uses for such structures - the analysis of vocal performance in an original study. In particular, we dissect voice recognition systems to determine their inner workings.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139319458","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}

{"title":"Fake Review Detection using Machine Learning","authors":"G. M, Y.S.N Siva Teja, K.Ajay Sharma","doi":"10.36647/ciml/04.02.a002","DOIUrl":"https://doi.org/10.36647/ciml/04.02.a002","url":null,"abstract":"Online reviews have become increasingly important in the world of e-commerce, serving as a powerful tool to establish a business's reputation and attract new customers. However, the rise of fake reviews has become a growing concern as they can skew the reputation of a business and deceive potential customers. As a result, detecting fake reviews has become a key area of research in recent years.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139319398","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}

{"title":"Modern Optimization Techniques Based PID Controller Tuning for the Speed Control of a DC Motor","authors":"Subrata Pandey","doi":"10.36647/ciml/04.02.a004","DOIUrl":"https://doi.org/10.36647/ciml/04.02.a004","url":null,"abstract":"In this paper, the optimal configuration of the Proportional Integral Derivative (PID) controller for the speed control of a DC motor are determined and compared using five optimization algorithms. The five optimization algorithms are respectively Ant Lion Optimization (ALO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Multi-Verse Optimization (MVO) and Salp Swarm Optimization (SSO). The objective function uses The Integral of Time multiplied by Absolute Error (ITAE) as the performance index. A comparison of all these methods is done using the following step response parameters - steady-state error, settling time, maximum overshoot and rise time. ALO performed best among all the optimization algorithms.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139319473","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}

{"title":"Fake News Detection Techniques for Diversified Datasets","authors":"Dr. Gayathri M, Tarini S, G. S","doi":"10.36647/ciml/04.01.a006","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a006","url":null,"abstract":"The introduction of the World Wide Web and the quick abandonment of the social media policy cleared the method for the rapid dispersal of information that has never been seen during human archive. Due to the way social media manifesto are currently operating, users are producing and participating in more information than ever before, some of which is false and has no relevance to reality. The numerous lives of individualities now hang in the balance as a result of social media. important has formerly been fulfilled in these three fields, including contact, advertising, news, and docket advancement. Automated bracket of a textbook composition as misinformation or intimation is a grueling task. Indeed, an adept in a distinctive sphere must traverse multiple features before granting a decree on the probity of a composition. In this work, we bring forward to use a machine literacy quintet perspective for the automated bracket of newspapers. [1] Our study traverses contrasting textual parcels that can be used to discriminate fake appease from real. Social networking is one of the most critical subjects in the business world moment. For that reason, it is critical to pinpoint a vicious account. So, for that purpose we have developed machine learning algorithms to declare the real or fraud news. Machine learning algorithms will give the impose information about the data sets. These algorithms can decide to corroborate the real or fake news. [2] We have developed seven algorithms so that because of using these many algorithms finally we can compare the accuracy of all the algorithms. So, it can be tranquil to declare about the social media news. The data has been anatomized for these purposes, and learning algorithms have been used to identify fake news. By using these parcels, we instruct a coalescence of dissimilar machine learning programs using colorful septet styles and estimate their presentation on real world data files. Investigational appraisal confirms the supercilious presentation of our proposed chorus beginner perspective in correlation to solitary novice. Keyword : Artificial Intelligence, Authenticity, Classification, Fake News, social media, Websites","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122639053","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}

{"title":"A Review on Parkinson’s Disease Prediction and Tele-Consulting using Machine Learning","authors":"Shree Kumar, Akarsh N L, Manoj Kumar N D, Chethan Umadi","doi":"10.36647/ciml/04.01.a001","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a001","url":null,"abstract":"Large medical datasets are available in various data repositories and are used to identify diseases. Parkinson's disease is regarded as one of the most lethal and progressive nervous system diseases affecting movement. It is the second most common cause of disability in the brain and it Reduces life expectancy and has no cure. Nearly 90% of affected people with this disease have speech disorders. In real-world applications, data is generated using a variety of Machine Learning techniques. Machine learning algorithms assist in the generation of useful content from it. Machine learning algorithms are used to detect diseases in their early stages in order to extend the lives of the elderly. When considering the term 'Parkinson's,' the main concept is speech features. In this paper, we are reviewing various Machine Learning techniques such as KNN, SVM, Naïve Bayes, Deep learning techniques and Logistic Regression to predict Parkinson's disease based on user input, and the input for algorithms is the dataset. Based on these characteristics, we anticipate that the algorithms will be more accurate. The model is used in conjunction with the frontend to predict whether or not the patient has Parkinson's disease. Prediction is critical in the early stages of patient recovery. This can be accomplished with the assistance of Machine Learning Keyword : Convolutional Neural Network (CNN), Deep Belief Networks (DBN), Deep Neural Networks (DNN), K-nearest Neighbors Algorithm (KNN), Machine Learning, Parkinson’s, Speech disorders, Support Vector Machine Classifier (SVM).","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133068055","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}

{"title":"Big Data Analytics Using Machine Learning Techniques for Prediction on Datasets","authors":"Ankit Verma","doi":"10.36647/ciml/04.01.a002","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a002","url":null,"abstract":"Data analytics is the process of performing scientific and statistical analysis on raw data in order to transform it into information that can be used for gaining knowledge. A recently emerging trend in feature abstraction is the combination of computational techniques and big data analysis. This requires gaining knowledge from trustworthy data sources, being able to digest information quickly, and making accurate predictions about the future. The primary objective of this study is to locate the machine learning strategies that produce the most accurate prediction by utilising the model that has been proposed. The supervised and unsupervised strategies have been implemented in a variety of different ways using the MapReduce methodology; however, the suggested model makes use of the Apache Spark framework in order to compare the many existing methods. In this study, the emphasis is placed on elucidating the characteristics of datasets in order to conduct the most accurate analysis possible using machine learning techniques. For the purpose of conducting an analysis of the data sets, machine learning methods such as linear regression, decision trees, random forests, and gradient boosting tree algorithms are utilised. In light of the findings of this research, it is possible to draw the conclusion that when the Spark framework is applied on top of Machine Learning methods, the efficiency of the model is improved by a factor of seventy percent in comparison to the MapReduce paradigm. Keyword : Apache Spark Framework, Big Data Analytics, Machine Learning Algorithms, MapReduce Paradigm.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133776396","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}

{"title":"A Novel Medical Chatbot with Alzheimer’s Disease Detection Using Deep Neural Network","authors":"Maragathavalli P, Aishwarya Devi V, Bhuvanesh D, Manikandan S","doi":"10.36647/ciml/04.01.a004","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a004","url":null,"abstract":"The healthcare sector is one of the largest focus areas in the world today. Individuals are becoming increasingly susceptible to lifestyle diseases. Hospitals and clinic are the most widely used place by the patients to consult doctor and get treated. People consider it as the most reliable means to check their health status. But in this way of approach for treatment the patients need to wait for a long time to consult the doctor which makes them more sick . In order to avoid such situation we came up with the idea of medical diagnosis chatbot in which user can interact with the Artificial Intelligence chatbot , to analyze the disease based upon the symptoms and with the MRI scan report. Keyword : DNN, imagenet, inceptionV3, machine learning, mobilenet, MRI scan images.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114774725","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}

{"title":"Liveness Identity Verification for Face Anti-Spoofing in Biometric Validation using Recurrent Neural Network","authors":"Maragathavalli P, S. J, Syed Abdul Kareem, Nekkanti Bhavitha","doi":"10.36647/ciml/04.01.a003","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a003","url":null,"abstract":"Face anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person's face. It has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks. However, most previous approaches still suffer from diverse types of spoofing attacks, which are hardly covered by the limited number of training datasets, and thus they often show the poor accuracy when unseen samples are given for the test. To address this problem, a novel method is proposed based on liveness identity verification for face anti-spoofing in biometric validation using the Recurrent Neural Network (RNN). Keyword : Biometric Validation, Face Anti-Spoofing Identification, Face Liveness Detection, Face Recognition, Lightweight CNN, Machine Learning, RNN.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115894761","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}

{"title":"Efficient User Friendly Robot for Aged and Physically Disabled People","authors":"A. T","doi":"10.36647/ciml/04.01.a007","DOIUrl":"https://doi.org/10.36647/ciml/04.01.a007","url":null,"abstract":"Nowadays due to their busy schedule people leave their parents alone at home and stay abroad. Due to this there is rapid need for care taker one who looks after old aged people like their near and dear ones efficiently. So, this is need to design and implement efficient robot one who take care as human. Efficient user-friendly robot for aged and physically disabled people is designed and developed that is capable to take care of senior citizens in terms of their health monitoring systems. Communicates patients through camera and interacts them by knowing the patients temperature, SPO2, heart rate and room temperature. We address the creation of user-friendly robot for aged people and physically disabled in a proper indoor environment. We propose a design based on multimodal robot which take care of the aged and physically disabled people. Robot allows the user to specify in a natural way, in which it helps in health monitoring, obstacle avoidance, gas leakage detection & communication. The knowledge which we gain in this robot is that to plan and execute effective personal take. Keyword : Gas leakage detection, Health monitoring, IOT communication, obstacle avoidance.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122242431","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}