2023 7th International Conference on Computing Methodologies and Communication (ICCMC)最新文献

{"title":"A Composite Technique for Creating Contemporary MRS using Association Rule Mining & CF","authors":"M. Amanatulla, Muppalla Subba Rao, Pothireddy Hemalathareddy, Kadiyala Pavani","doi":"10.1109/ICCMC56507.2023.10084205","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10084205","url":null,"abstract":"The vast amount of information on the internet has made information available, but it has also made it difficult for users to choose the information that is necessary or interesting to them. To address this issue, recommender systems (RS) were developed to find relevant information using information filtering. Using RS, users may find the appropriate data from a vast collection. There are several types of RS, but those developed using collaborative filtering techniques have proven to be the most effective for a variety of issues. One of the most popular RS accessible is called the Movie Recommendation System (MRS). In this paper, suggestions will be made based on the shared features of user items. Both user objects and item objects are frequent in the movie recommendation system. In order to provide stronger suggestions, this paper integrates the collaborative filtering technique with association rule mining. By integrating collaborative filtering with association rule mining, a hybrid strategy that takes use of both techniques' advantages can boost the recommendation system's performance. Consequently, the recommendations that were generated can be regarded as strong recommendations. Collaborative filtering uses the past behavior of users to make recommendations, while association rule mining looks for patterns in the data to identify items that are frequently bought together. Combining these two approaches can help overcome the limitations of each individual method, such as the need for a large amount of data for collaborative filtering or the lack of personalization in association rule mining. This paper combines data mining and conventional filtering techniques to provide movie recommendation suggestions.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129591354","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":"Internet of Things (IoT) Feedback System using Raspberry Pi","authors":"P. G, Ramraj B, Manimuthu Ayyannan, Rohith Bhat C, K. T, Neelam Sanjeev Kumar","doi":"10.1109/ICCMC56507.2023.10083989","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083989","url":null,"abstract":"Constant monitoring and examination of customer input in modern businesses, public spaces, and institutional settings necessitates a significant investment in record-keeping equipment. This study includes a feedback mechanism to help with system documentation and maintenance. In the context of this work, “feedback” refers to the response of the surrounding environment to a specific activity. This study tracks the client reviews by physically rating them and then uploading the results to the cloud. Anyone with access can see how most of the comments were received. With this system in place, monitoring the status of each item of input is a breeze.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629892","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":"Submarine Communication for Monitoring Diver's Health using Li-Fi","authors":"K. K, Praveen N, R. N, Ram Srinivas C, Sanjay G","doi":"10.1109/ICCMC56507.2023.10083996","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083996","url":null,"abstract":"In order to conduct study on the underwater living world, diving is now frequently used. The health concerns divers have when diving are one of the main issues with diving, which is why it's important to check divers' health. The primary focus of this research is on diving health monitoring systems that communicate data utilizing Light Fidelity. This device detects many health parameters, including Panic button, temperature and body position. The detected health parameters are stored in a memory chip as a database for later study. The system only sends information to adjacent person and submarine when the critical situation happens in order to conserve electricity. A prospective technology to appreciate underwater communication is water electronic communication. Due to the physical scale being constrained in real water, the underwater electrical communication experiment performed in the lab is radically different from that conducted there. During this paper, many types of agent's area unit evaluated to vary the coefficients of experimental water exactly. The frequency domain characteristic of knowledge exchange across water channels is then evaluated and compared in experimental water as a criterion for the responsibleness of water recreation. To save power, the device only transmits data to nearby divers and vessels when its health is unhealthy. The results show that the type and size of the active substance can have a significant effect on the properties of water, and consequently the frequency-domain components of water communication signals are adversely affected by the concentration of the active substance. Diving has become a popular way to analyze the underwater world. On the other hand, natural disasters in recent decades have generated a great deal of interest in studying and monitoring the coastal environment. A green, clean and secure alternative to traditional communications, UVLC offers high data rates and low latency bandwidth. The usage of this method is more difficult than long-range communications. Additionally, UVLC systems suffer from extreme signal attenuation and highly turbulent channel conditions. As a result, this white paper provides a thorough and complete evaluation of current improvements in UVLC implementations to address the problem of optical signal propagation.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124684514","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":"Image Segmentation Approaches to Detect Abnormalities in Brain MRI Images using CNN & U-Net","authors":"Narisetty Srinivasarao, Ganta Rama Krishna, Chava Raghu, Kagitha Sasidhar","doi":"10.1109/ICCMC56507.2023.10083935","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083935","url":null,"abstract":"It is a challenging and crucial task in medical research to recognize and define brain cancers via Magnetic Resonance Imaging (MRI). Inspite new model, this paper comes with a solution for drawbacks in the (CNN+DWA (Distance Wise Attention)) model with the hybrid model, it has two models which are U-NET and (CNN+DWA). Even though CNN is the best model for brain tumor identification, it has one exception case, when the brain tumor is more than 1/3rd of the brain then it gives inaccurate values. In normal cases as usually, CNN models are used for analysis if an exception case has occurred then only in that condition this U-NET model comes into the picture, otherwise, this model is just beside without disturbing analysis of CNN. The CNN Model suggests using a pre-processing method that only affects a tiny portion of the MRI image as opposed to looking at the entire picture. It, therefore, resolves the fitting problems in the Cascading Deep Learning model and speeds up computation. In the second stage, a straightforward and effective convolutional neural network (C-Conv Net/CNN) is suggested to deal with a smaller portion of each slice's brain MRI images. This CNN model uses two different approaches to mine both local and global characteristics. Additionally, the DWA mechanism has been employed to enhance the accuracy of brain tumor segmentation as compared to contemporary models. The DWA approach takes into account the effects of a brain tumor being present in a critical region of the brain. U-NET Model, which is already exited but in addition to that included error value. This exception case is calculated by a model based on accuracy and computational time. It maintains accuracy and efficiency by adding error value in exceptional cases only.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129738283","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 Hybrid Gradient Boost Model for Intrusion Detection","authors":"R. Vaishali","doi":"10.1109/ICCMC56507.2023.10084018","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10084018","url":null,"abstract":"Due to the advancement of network threats at present, it is crucial to conduct research on identifying and preventing network anomalies. Machine learning (ML) is one strategy for Intrusion Detection System (IDS). Finding a reliable system to act as a networking shield is still difficult despite the fact that various IDS are suggested utilizing ML. This paper suggests a hybrid model combined with the gradient boost methods XGBoost and Lightgbm to forecast the various attacks that are urging in the network. To obtain the higher precision, hyperparameters of the algorithms are tuned. The proposed system is trained using the UNSW-NB15 dataset, which contains attacks for Generic, Exploits, Denial of Service (DoS), Shellcode, Fuzzer, and Reconnaissance. The system has an average accuracy of 99.89%. Because of the recent dataset training, the proposed system is relevant to modern Intrusion Detection Systems used in current network systems.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124200569","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":"Machine Learning and Deep Learning Techniques on Accurate Risk Prediction of Coronary Heart Disease","authors":"Mandadi Sai Gangadhar, Kalyanam Venkata Sree Sai, Salem Hruthik Sai Kumar, Kanaparti Anil Kumar, M. Kavitha, S. S. Aravinth","doi":"10.1109/ICCMC56507.2023.10083756","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083756","url":null,"abstract":"Coronary artery disorder is the heart disease that is prevalent across the globe today. It's a serious issue for the humans as it requires proper diagnosis. Coronary artery disease develops over time as a result of plaque build- up in coronary arteries results in partial blockage of blood flow as the, which is primarily composed of cholesterol, calcium, and fibrin. Coronary arteries help human heart to pump oxygen-rich blood throughout the body by supplying it. However, appropriate diagnosis and early prediction will lessen the likelihood of developing it. This study explores the possibility of foreseeing cardiac illness at an early stage using deep learning algorithms. This study's primary goal is to properly determine whether a person has heart problems or not. by using deep learning techniques, Deep learning and various machine learning algorithms can both be used to implement early-stage prediction. Data mining, Decision trees, Naive Bayes, Artificial Neural Networks (ANN) are some examples through which it can be implemented. The research is going to be done using ANN Model.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"103 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121148333","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":"IoT Wearable Breast Temperature Assessment System","authors":"B. Ashreetha, Dankan Gowda V, Harishchander Anandaram, N. A, Neeraj Gupta, Basant K. Verma","doi":"10.1109/ICCMC56507.2023.10083511","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083511","url":null,"abstract":"Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121156989","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":"Retinal Glaucoma Detection from Digital Fundus Images using Deep Learning Approach","authors":"S. S, D. V. Babu","doi":"10.1109/ICCMC56507.2023.10083712","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083712","url":null,"abstract":"A disorder called glaucoma that damages the optic nerve can result in either a partial or whole loss of vision. As a reason, it is critical to start glaucoma screening at a young age. Glaucoma symptoms do not manifest until the condition is advanced and the patient has already experienced considerable vision loss. The bulk of early diagnostic methods rely on careful feature engineering. Fundus images are particularly useful in the clinical context for the early detection of vision problems.Because of its superior performance, In related fields including image synthesis, disease segmentation, biomarker segmentation, and illness identification, deep learning is being employed more and more often. Convolutional neural networks have lately been used to diagnose glaucoma and other eye problems in ophthalmology. They have been effective in the early diagnosis of several disorders. Many layers of highly connected neural networks. The study approach makes use of models that were previously trained on ImageNet using the dristi dataset with the accuracy of almost 97% that was achieved, it is evident that the built classification model can accurately diagnose glaucoma.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126380343","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":"Railway Bridge Inspection using CNN","authors":"Lakshmi Narasimham Chennareddy, Sai Vamsi Gandabathula, Vivek Vardhan Jasthi, Fathimabi Shaik","doi":"10.1109/ICCMC56507.2023.10083695","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083695","url":null,"abstract":"The key issue for the railway department has been to examine and monitor railway bridges, as urbanization expands, the availability of railways grows, and the railway system has greatly expanded throughout the nation. The expense of maintaining railroad bridges and associated costs with personnel have been a burden on the railroads. To ensure transportation safety, concrete bridge crack detection is critical. Deep learning technology has made it possible to automatically and accurately detect faults in bridges. The present methods are not accurate and they require a large size of dataset for model training and they require a high computational power model training. The proposed model is a convolutional neural network (CNN) based end-to-end crack detection model. The proposed model achieved a 95% detection accuracy.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646191","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":"Performance Analysis of DC-DC Converter for Electric Vehicle Charging Applications","authors":"E. Parimalasundar, S. Maneesha, R. Hanish, P. M. Reddy, P. K. Harish, P. K. Rao","doi":"10.1109/ICCMC56507.2023.10084154","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10084154","url":null,"abstract":"In the coming years, more individuals will decide to retreat energy storage devices due to the faster development and widespread adoption of Electric Vehicles (EVs). High-performance DC-DC converters come in buck, boost, and buck-boost topologies, which are the three most widely accepted. These converters' output voltages are limited at high duty cycles due to increased component stresses. These battery packs, which have been chosen for retirement, can be used in energy-storage systems to support demand response operations with a high penetration of renewable energy. MATLAB/Simulink was used to simulate the DC-DC converter for integrating the two variable input supplies. The performance analysis of various DC-DC converter for EV charging applications.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125666865","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}