2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)最新文献

{"title":"Machine Learning Based Sentiment Analysis and Swarm Intelligence","authors":"Rajendra Kumar Patra, Bassamma Patil, T. S. Kumar, G. Shivakanth, M. M","doi":"10.1109/ICICACS57338.2023.10100262","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100262","url":null,"abstract":"Social networking platforms, online news outlets, and weblog hosting services continue to expand, and with them come an increasing number of user-generated content contributions such product evaluations, comments on recent articles, and more. Products, movies, shopping sites, and review sites are common areas for customer feedback. The sheer volume and rate of growth of material that expresses opinions is becoming a burden on manufacturers who must manually categorise this data. Also, the perspective on entities at the level of aspects is expected by the public. It is for this reason that an automated sentiment analyzer must be built, one that can detect the bipolar and multipolar sentiment polarity of documents and/or aspects. People's ability to voice their opinions openly in public has greatly increased with the advent of various social networking apps. As a result, this helps to further the field of automated emotional analysis by providing a wealth of data on which to base analyses of people's feelings. User review categorization and analysis has emerged as an important part of sentiment analysis in recent years. Opinion mining is used to determine the degree of positivity or negativity in each user review posted on a social network. Numbers, star ratings, and descriptive text are the three polarity indications in a review. The sentiments of the public have been analysed using a wide variety of machine learning methods, but these methods often fall short in key areas such as classification accuracy, precision, recall, and F-measure due to pre-existing classification problems such as the two-class problem, overfitting, and parallel processing. The primary goal of the study is to create a fully automated system that can analyse a massive dataset of movie reviews using aspect-based SA or OM. We use natural language processing to tally up the good, bad, and ugly reviews. The research enhances advertising efforts and guides customers to the most suitable products. In this study, we use a variety of machine learning and swarm intelligence optimization techniques to the problem of determining the tone of movie reviews. Profits are increased and product failures are decreased thanks to this study for a wide range of businesses. The effectiveness of these procedures has been measured using MATLAB data from critical assessments of movies. The simulation results demonstrate that the proposed HIRVM scheme outperforms the state-of-the-art sentiment analysis schemes like HKELM, ID3, and J48 with respect to accuracy (96.82 percent), sensitivity (97.1 percent), specificity (91.2 percent), precision (96.2 percent), recall (90.2 percent), and F-Measure (89.5 percent). As compared to conventional methods, the suggested HIRVM significantly reduces both processing time (28.14s) and processing cost.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122946497","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":"Automatic Identification of Epileptic Seizure Using Kelm Optimized By Grey Wolf Algorithm","authors":"D. Saranya, A. Bharathi","doi":"10.1109/ICICACS57338.2023.10099688","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099688","url":null,"abstract":"Epilepsy is the common neurological disorder where nerve cell activity is disturbed which results in causing seizures. Automatic epileptic seizure detection is an essential requirement for clinical diagnosis of epilepsy. Classifying facts is the usual task in machine learning. Epileptic seizure identification using Support Vector Machine (SVM) have few limitations like its outcomes lacks transparency. Extreme Learning machine is computationally uncomplicated and yields definite results also because of its quick learning pace, (ELM) is utilised to pick input data. The motivation of this research is to boost the training rate and correctness of the ELM. Kernel Extreme Learning Machine (KELM) is chosen for refining generalization capacity and to improve the classification accuracy nature inspired swarm intelligence Grey Wolf Algorithm (GWO) is adapted. The grey wolf algorithm optimizes the KELM parameters. The GWO-KELM classifier is laid on Epileptic Seizure Data Set from UCI and the experimental results such as learning accuracy, learning error and classification accuracy are compared with traditional classifiers. By performing analysis with the proposed GWO- KELM classifier faster learning speed, accuracy with high precision and low error rate is achieved and proposed classifier outperforms other classifiers in identification of epileptic seizures.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123012092","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 Modern Podcast Player for Mobile Platform","authors":"Nongmeikapam Thoiba Singh, P. Kaur, Simarjeet Kaur, Damandeep Kaur","doi":"10.1109/ICICACS57338.2023.10100023","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100023","url":null,"abstract":"Since podcasting started, the field has grown slowly and steadily, eventually becoming a major trend in making content for social media. As the world becomes more hectic, the podcast format has gained immense popularity. Audio content enables multitasking among listeners. The fact that the listener may control playback is one of the most tempting parts of downloading podcast episodes. Particularly while listening to interview podcasts, it is much more convenient to be able to choose the speed and to fast-forward or rewind. Because it is a cross-platform mobile application built with Flutter, the podcast player is available on both Android and iOS. With advanced technology, you may search for podcasts, subscribe to them, view podcast charts, stream and download episodes, and play podcasts in the background. It is created with Flutter and the Dart programming language. The Application Programming Interface (API) communicates with the iTunes API (via a package) in order to retrieve and parse podcast data. Multiple technologies, including Flutter SDK Dart programming language, Sembast -NoSQL Database, API - iTunes and Podcast Index, RxDart, and Podcast Search, were utilised in order to construct this platform for sharing experiences. We have collaborated to produce a clean and user-friendly app that will appeal to a broad audience, ranging from children who can listen to stories to adults and the elderly who can listen to any interesting genre based on their preferences and use all the app's features by interacting with its simple user interface (UI).","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117016846","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":"The Improved Waves based Surface Energy Analysis using Frictional Force Determination Model","authors":"Navjot Rathour, Bharti Ramola","doi":"10.1109/ICICACS57338.2023.10099540","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099540","url":null,"abstract":"In general, semiconductors should be able to conduct electricity easily. It should have low resistance. Should have high tensile strength and should be flexible. Should be unaffected by environmental conditions. That is, it should not be affected by chemical effects in the air, or by exposure to sun, rain, etc. When the electricity passes through a conductor, the conductor heats up. Hence should have non-expansion by heat. They are divided into three types based on their ability to conduct electricity easily, giving less resistance to the flow of current. They are solid state conductors, liquid state conductors and gaseous state conductors. Soldering should be easy. It should be readily available at low cost. Metals such as silver, copper, brass, aluminum, tungsten, nichrome, zinc, and iron are all good conductors of electricity. These can be converted into thin wires and thin strips for electrical work.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117281939","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":"PSO Algorithm-Based Management Method and Measurement of Enterprise Employee Performance Evaluation","authors":"Mei Li","doi":"10.1109/ICICACS57338.2023.10099919","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099919","url":null,"abstract":"The main purpose of PE in enterprises is to motivate employees to work hard, enhance their sense of responsibility and mission, and create value for the enterprise. And EPE as an important indicator system to measure EP has been widely used in the PM of enterprises. However, two factors need to be considered when using PE index system in PM: one is employees' subjective attitude, PE index determination method; the other is the result of PE. The main purpose of this paper is to conduct a study on the management method and measurement of EPE in enterprises based on PSO algorithm. This paper first classifies the PE results, and then uses different evaluation methods to manage different objects according to the classification, i.e., the study of EEPE management methods and measurement methods using the PSO algorithm. In this paper, the PSO algorithm-based method of enterprise employee performance evaluation (EEPE) is investigated and organized. The results show that the method based on PSO technology can better evaluate the work ability and performance of employees; it also improves the work efficiency and work attitude of employees; it improves the motivation and satisfaction of employees in the enterprise. It has an important role in EP management.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129821630","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":"The Smart Bacteriological Examination and Treatment of Urinary Tract Infections in Children using Fuzzy Logic Control","authors":"N. Misra, Tarun Prashar","doi":"10.1109/ICICACS57338.2023.10099921","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099921","url":null,"abstract":"It is very important to pay attention to the discomfort and various changes in the body. Diagnosing and treating urinary tract infections is a time-consuming but important process. There are diseases that affect both men and women. Infections of the genitourinary system fall into the category of those diseases to which no one is immune. Pathogens do not distinguish people by gender, do not look at age, do not pay attention to social status. Genital infections are diseases characterized by specific changes in the body caused by certain types of pathogens, excluding sexually transmitted diseases. The treatment of genital infection directly depends on the degree of inflammation of the affected area, the type of microorganism that caused the disease, and the specific pathology that occurs in the body during the disease. The symptoms that bother the sick person are also important, because they are often very pronounced. Various pathogens can cause infectious diseases of the genitourinary system. Often there are cases where the cause of ill health is not one, but several microbes. Depending on the type of pathogens, infections are divided into two main groups - opportunistic and pathogenic.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129945929","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":"Construction of Supervised Learning Model for Crop prediction based on Environmental Condition","authors":"A. Reddy, K. Sumathi, P. B, S. Chaudhary, K. Prathebha, S. Ramesh","doi":"10.1109/ICICACS57338.2023.10099661","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099661","url":null,"abstract":"Agriculture is defined as the backbone of our nation. Along with providing food products, it also increases the economic growth of the country. There is a crucial need for technologists and engineers to come up with various technologies and aids to help the farmers to succeed in farming to prevent the death of farmers and urbanization. Urbanization is one of the major threats to human society. If the farmer can identify the property of the soil and nutrients available in the soil even before sowing, it will be extremely helpful for him/her to proceed with the further steps i.e., to pick a perfect crop that can produce a maximum yield. If that happens and a perfect cycle repeats every year, it will also increase the number of nutrients in the soil. This proj ect includes the analysis of the property of the soil by measuring certain environmental parameters such as nitrogen, potassium, and the phosphorous content in the soil and also environmental parameters such as temperature, humidity, ph. level and the rainfall amount. This data acquired further undergoes some pre-processing techniques like cleaning the data and transformation of the data to the desired format. The cleaned data is then split into two divisions. One is for training and the other one is for testing the software. The data used for training is then used analyzed using various machine learning algorithms such as Linear Discriminant Analysis, Decision Tree algorithm, and the Random Forest algorithm. Then a graph is generated for each of the algorithm based on certain parameters. These parameters include precision comparison, recall comparison, and the Fl score comparison for various fruits like apple, banana, grapes, mango, muskmelon, orange, papaya, etc., and other crops varieties such as chickpea, coffee, kidney beans, lentils, moth beans, maize, etc. Once all the parameters are analyzed using the graph, the best crop that is suitable for the soil will be suggested to the farmer. Using this data, he/she can sow a suitable crop and increase the average yield of the soil.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130143978","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":"Improved Analysis of Inflammatory Diseases in Women using Artificial Intelligence based Approach","authors":"Vibha Verma, Yadvendra Singh","doi":"10.1109/ICICACS57338.2023.10100269","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100269","url":null,"abstract":"Infections of the genital system are the main causes of inflammatory diseases in women. This pathology can be both gynecological and urological and is very dangerous for the female body. Inflammation of the genitourinary system can lead not only to violations of urination and menstrual irregularities, which can trigger the development of an ectopic pregnancy, and often lead to the development of infertility. If we talk about the reproductive system, it consists of external and internal genital organs. Inside the small pelvis are the uterus, fallopian tubes and ovaries. If a woman does not pay attention to the signs of the development of the inflammatory process in time and does not completely cure the acute form of the disease, it will turn into a chronic form, which will periodically worsen and cause complications and discomfort. girl Diseases of the genitourinary system, which are inflammatory in nature, are caused by pathogens of pathologies","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321008","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":"Convolutional Neural Network Method for Effective Plant Disease Prediction","authors":"R. Mishra, Dhiraj Singh","doi":"10.1109/ICICACS57338.2023.10099559","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099559","url":null,"abstract":"Agriculture as a source of food is essential for humankind. Therefore, the diagnosis of plant diseases is a significant concern. Plant disease diagnosis through plant monitoring is necessary for maintainable agriculture. Observing plant diseases automatically is very challenging. Managing plant diseases requires a lot of effort and expertise. Traditionally, identifying plant foliar disease is subjective, inefficient, and expensive, requiring a large number of personnel and a large amount of information about plant disease. This novel uses a deep learning-based Convolutional Neural Network (CNN) approach for plant disease identification to tackle this problem. First, collect the dataset from online Kaggle and pre-process images to remove noise in the first phase. Then Logistic Decision Regression (LDR) method was utilized for feature selection in the pre-processed plant image. After that, we apply segmentation based on selected features. Finally, our proposed method proficiently classifies the plant's disease based on segment images. Therefore, this approach produces high disease detection accuracy and specificity with a minimum error rate compared to different methods.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129139325","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":"Cardio-Vascular Disease Prediction using Machine Learning Techniques","authors":"Srinivas Konda, N. K. Kar, Padmaja Pulicherla, G. Shivakanth, R. C","doi":"10.1109/ICICACS57338.2023.10099769","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099769","url":null,"abstract":"The main goal of this study is to use Data Mining Method and Artificial Neural Network to develop a system that can automatically and rapidly predict the risk of coronary heart disease (ANN). The IRT Perundurai Medical College and Hospital's master health checkup data on occupational drivers were used to test this idea (PMCH). Analysis for risk identification is performed in the first stage of the hybrid approach suggested in this study, and level prediction is performed in the second. The sensitivity, specificity, precision, receiver operating curve, area under curve, 10-fold cross validation technique, and the F-measure are used for this investigation. The initial step of the study involves thinking about the most common and changeable dangers. Systolic blood pressure, diastolic blood pressure, and body mass index (BMI) are three biophysical variables, whereas fasting blood sugar, postprandial blood sugar, and triglyceride levels are three blood chemical factors (TG). All of these characteristics have a predetermined margin value that is based on WHO guidelines. Support Vector Machine (SVM), Naive Bayes (NB), and the C4.5 algorithm in Decision Tree are the three approaches used to categorize these variables and forecast the risk (DT). The C4.5 algorithm fared best in forecasting CHD risk when the three approaches were compared using the performance metrics, as discovered by the investigation. The decision tree C4.5 method outperformed the other two classifiers with an improved 99.5% accuracy and 99.67% sensitivity. The increased percentage demonstrates that the Decision tree method delivered consistent results that were better to those produced by the Naive Bayes and SVM models.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197232","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}