2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)最新文献

{"title":"Early Detection of Breast Cancer using Machine Learning","authors":"David A. Omondiagbe, Shanmugam Veeramani, A. Sidhu, Yash Y Panchal","doi":"10.1109/ICSCSS57650.2023.10169777","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169777","url":null,"abstract":"Globally, Breast cancer is one of the most common types of cancer in women. Additional research is required to address the challenges and limitations of traditional detection process, as well as to create standardized processes for data collection and analysis of breast cancer by utilizing mammograms and other medical imaging data. Several research has been undertaken in recent years to create and assess ML models for breast cancer diagnosis, recurrence prediction, and therapy planning. Machine learning algorithms may also be trained to recognize small changes in breast tissue that may be symptomatic of cancer, even before a tumor can be seen on a mammogram, as done traditionally. The application of ML approaches in breast cancer diagnosis has resulted in higher accuracy and specificity, making it a suitable tool for assisting clinicians in clinical decision-making. Overall, the findings indicate that more study may be required to address the problems and limitations of ML in breast cancer diagnosis, as well as to create standardized processes for data collection and analysis.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125583997","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":"PV Fed Power Converter Based Battery Management System for Electric Vehicle Application","authors":"A. U. Kumar, M. Subhashini, C. Vignes, P. Malathi, G. Pradeep","doi":"10.1109/ICSCSS57650.2023.10169308","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169308","url":null,"abstract":"With the aid of renewable energy, the electric vehicle is charged. In this case, the renewable energy is provided by solar panels. Electric vehicles can be operated in two different ways: either directly at the moment of generation, or by storing energy in batteries for later use. Renewable energy is obtained from the solar panel and transfer it to the solar charge controller. It is possible to immediately charge equipment from the solar charge controller, store the energy in batteries, and then charge electric vehicles using an inverter. By doing this, battery efficiency is improved. The converter can be changed to accomplish it.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123348485","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":"Surface Electromyography based Wrong Posture Muscle Detector","authors":"T.Paison Melkish, R. Joy, H. EphraimImmanuel, J. JeffersonRajadurai, A. Fabiyo","doi":"10.1109/ICSCSS57650.2023.10169315","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169315","url":null,"abstract":"Posture is the way a person holds their body so that their muscles aren’t overworked as they move. Several health problems can result from poor body posture. Back discomfort and weariness are just two symptoms of poor posture that might arise and interfere with our everyday activities. A gadget is increasingly needed because the majority of people nowadays have back pain, injuries, neck pain, shoulder difficulties, etc. Surface electromyography (sEMG), a noninvasive method for measuring muscle fatigue, is frequently utilized in research on occupational and rehabilitation as well as athletics. Such signals are complex, multicomponent, highly moving, and exhibit substantial inter-subject fluctuation, even more so during spontaneous contractions. In light of this, period-frequency driven machine learning techniques can facilitate the creation of computerized systems for this data. Approaches: This study suggests an analysis based on high-resolution time-frequency techniques, such as the Stockwell transform (S-transform), B-distribution (BD), and extended modified B-distribution (EMBD), to distinguish between non-fatigue and fatigue circumstances for dynamic muscle. Preprocessing, BD, and EMBD are applied to the non-fatigue and fatigue phases of sEMG signals obtained from the biceps brachii of 52 healthy participants. Each approach extracts twelve features, and the most important features are chosen via genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning techniques, including rotation forests, random forests, and SVMs with polynomial and radial basis kernels, are employed for the classification. Because sign languages (SLs) are so structured, they make a good learning environment for learning more abstract concepts about hand location, shape, and motion. The detection and recognition of static gestures, or hand posture and shape, is essential in SLs and in the field of motion trajectory it has a significant impact. Three methods—3D hand modelling, appearance-based techniques, and hand shape analysis—can be used to do vision-based hand shape identification.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123370618","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 based Environmental Assessment of Aquatic Ecosystem","authors":"R. Narmadha, Ram Nivedh Chopperla, Likhith Madhav Dasari, Inbakandan Dhinakarasamy, P. Naresh","doi":"10.1109/ICSCSS57650.2023.10169705","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169705","url":null,"abstract":"This study uses sensors to measure variables including pH, Total Dissolved Solids, dissolved oxygen, and CO2. Then, it shows the data on the Internet of Things cloud to track the health of aquatic ecosystems. Data was gathered for the study using sensors and Arduino micro-controllers, which were analyzed to determine the ecosystem’s health. The findings demonstrate that using IoT technology to measure factors in water bodies is a useful method of monitoring their health. The challenges faced by existing systems include the need for frequent sensor calibration, difficulty in ensuring data accuracy, and the risk of system failure due to sensor malfunction or data loss. The proposed system addresses these challenges by incorporating multiple sensors for redundancy, using machine learning algorithms for data analysis, and implementing an automated calibration system. The results can be utilized to create plans and laws that will safeguard and preserve aquatic environments. As a result, it is recommended to continue researching and developing the use of IoT technology for monitoring the health of aquatic ecosystems as it is viable strategy for maintaining their long-term survival.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123011533","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":"Comparing Resampling Techniques in Stroke Prediction with Machine and Deep Learning","authors":"M. R. Thanka, K. S. Ram, Shalem Preetham Gandu, E. Edwin, V. Ebenezer, Priscilla Joy","doi":"10.1109/ICSCSS57650.2023.10169237","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169237","url":null,"abstract":"Cerebrovascular accident (CVA), commonly known as a stroke, is a major cause of morbidity and mortality worldwide. Recent techniques in stroke prediction include the application of machine learning and deep learning algorithms, the integration of multimodal data, and the use of advanced feature selection methods. Challenges in stroke prediction systems include class imbalance, limited and heterogeneous data availability, interpretability of black-box models, and generalizability across diverse populations. The proposed objective of this study is to address these challenges and enhance the accuracy and reliability of stroke prediction models. Quick detection and management of stroke risk factors can reduce the incidence and severity of stroke. In recent times, machine learning approaches have been applied to estimate stroke risk based on patient data. This study uses three machine learning algorithms and an artificial neural network (ANN) model to predict stroke incidence based on a dataset containing 17 variables. The ANN model was optimized using the RandomSearch hyperparameter tuning technique and trained and tested on both the original unbalanced dataset and six resampled datasets generated using different techniques to address the class imbalance problem. The results indicate that the ANN model performed well on both the original dataset and the resampled datasets. The model achieved a higher accuracy of 99.3% on the dataset resampled using the SMOTE+RandomUnderSampling technique. The study suggests that the resampling techniques employed were effective in improving the performance of the ANN model, especially in dealing with class imbalance challenges in the dataset. The outcomes of this study recommend that the ANN model has the potential to be used as a predictive tool for stroke incidence. More study is needed, however, to confirm the effectiveness of the model on bigger, more diversified datasets and to evaluate its generalizability to other populations.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121091974","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":"Implementation of Machine Learning and Audio Visualizer to Understand the Emotion of a Patient","authors":"D. Anitta, A. Joy, Kottaimalai Ramaraj, M. Thilagaraj","doi":"10.1109/ICSCSS57650.2023.10169505","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169505","url":null,"abstract":"Understanding the emotion of a patient suffered by neurological disorder and etc., is a challenging task. In order to recognize the emotions from them, speech synthesizer is employed to extract the audio and then converted as text if needed. Presently, the application of Signal processing is having a tremendous effect in all disciplines ranging from complex medicinal applications to basic radio signal processing and transmission. The goal of Speech Emotion Recognition (SER) is to identify the emotional components of speech, regardless of its semantic content. While this activity can be successfully carried out by people as a natural element of spoken communication, research into the ability to carry it out automatically using programmable devices is still underway. The goal of studies on automatic emotion detection systems is to develop effective, real-time techniques for identifying emotions in a wide range of human-machine interface users, including call centre agents and consumers, drivers, pilots, and many more. This study will demonstrate a real-time audio visualization method for identifying human emotion based on the frequency of the human voice. Compared to the conventional techniques adopted by numerous researchers in this regard, the proposed model delivered better and precise outcome on recognizing the audio from the patients.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116063767","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":"Augmented Reality Application for Sustainable Development – A Grounded Theory Approach","authors":"Prasun Gahlot, Komal Suryavanshi, A. Gandhi, R. Raman","doi":"10.1109/ICSCSS57650.2023.10169566","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169566","url":null,"abstract":"Augmented Reality combines three-dimensional virtual items with reality. It is one of the valuable tools in promoting sustainable development. Companies from manufacturing, packaging, defense, and other areas are adopting AR technology to achieve sustainable development goals. This study explores the application of Augmented reality in attaining sustainable development from the expert’s point of view. An open-ended semi-structured questionnaire was prepared, and the interviews were conducted with 15 experts from academics and industry. With the grounded theory approach, five broad themes were explored. The findings revealed that AR application across industries is in its nascent stage. Although the application of AR is vast, there are a few challenges, including-hardware updates, availability of skilled employees, data privacy issues, and others.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115217981","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":"Blockchain based Letter of Recommendation Verification System for Higher Studies","authors":"C. Madanagopal, Ms Kaniskaa","doi":"10.1109/ICSCSS57650.2023.10169743","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169743","url":null,"abstract":"A letter of recommendation (LOR) is one of the important metrics in determining a student’s academic proficiency. It consists of the student’s academic milestones and is validated by the student’s professor which can be a key to pursuing higher studies in any reputed institution. It is required to be highly authentic and its evaluation is very uncompromising. A block-chain based LOR verification system has been proposed in this work. The student details are stored in the blockchain and the generation of a LOR is done with a secure hash key and is stored back in the block chain. External universities can examine the student’s LOR, through a portal to access the block chain. With the secure hash key, the LOR with the student’s professor’s validation is observed. By the proposed method, transparency, security and authenticity in the process of generating the LOR and sending it to desired universities is maintained.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"824 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115269396","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":"Transfer Learning-based Fruit Freshness Monitoring for Future Autonomous Industrial Robotic Arms","authors":"N. Srikanth, Ch. Madhuri, I. Venkata Narayana, J. Sahithi","doi":"10.1109/ICSCSS57650.2023.10169714","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169714","url":null,"abstract":"Consumption of fruits is a daily routine in humans’ life. The freshness of any fruit impacts their consumers, which will spoil health. So, the manual checking of each fruit freshness in farm field is difficult task. So, future autonomous industrial robotic arms automatically check the freshness of fruits in farm fields and distinguish the normal, abnormal fruits using artificial intelligence (AD properties. However, the conventional AI models like machine learning (ML), deep learning (DL) were failed to classify the multiple fruit freshness classes due to improper feature analysis. So, this work is focused on development of transfer learning (TL) based fruit freshness detection (TL-FFD) with multiple classes in automated environment. Initially, fruit freshness dataset is considered with green, ripe, and over ripe classes. Then, dataset pre-processing operation is implemented, which normalizes all images with uniform size. Later, the TL based GoogleNet model is adopted to train the pre-processed dataset, which stores the memory of multiple classes. Then, the random test image is applied to check the validity of the system, where GoogleNet model predicts the class of test images. Here, the proposed TL-FFD method achieved the 98.82% of accuracy and the simulation results shows that the proposed TL-FFD outperformed as compared to other ML, and DL models.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115957619","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":"Agricultural Crop and Fertilizer Recommendations based on Various Parameters","authors":"B. Mahalakshmi, V. Sakthivel, B. Sumitha Devi, S. Swetha","doi":"10.1109/ICSCSS57650.2023.10169320","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169320","url":null,"abstract":"Agricultural uncertainty lowers the Tamil Nadu’s productivity because it is a coastal state. More production should be possible only with more people and land, but it is not possible. Farmers used to rely on word-of-mouth, but climate circumstances prevent them from doing so. Soil is one of the important factors in cultivating the crops. The new farmers are unaware about the crop which has to be cultivated in the particular season. To solve this problem, this system helps the farmer to know about the crop which has to be cultivated in the particular season to increase the production of the crops. Machine learning methods allow us to forecast the future and produce a precise model from the data. Agricultural issues like crop rotation, crop forecasting, water and fertilizer requirements, and crop protection can be solved. The need for an effective approach to simplify crop cultivation and the need to assist farmers in managing and producing their crops results from the environment’s unstable climatic conditions. This could enable aspiring farmers to practice better agriculture. With the aid of data mining, a farmer may be given a system of suggestions to aid in crop production. Crops are suggested for use in such a strategy depending on their quantity and meteorological considerations. The analysis of the crop dataset resulted in crop recommendations based on season, soil type, water and productivity. Various techniques like Linear Regression, Support Vector Regression and Adaptive Network Based Fuzzy Inference System (ANFIS) are used in this system, which enable accuracy comparison and assist farmers in selecting the best crop under various situations.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115959574","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}