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

{"title":"Market Analysis and Forecasting using Trending Data Analytical Tool","authors":"C. A. Anuradha, A. Sivakumar, M. Sivaraman","doi":"10.1109/ICSCSS57650.2023.10169749","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169749","url":null,"abstract":"Marketing refers to the process of promoting, selling, and distributing products to potential consumers. It involves identify the preferences and needs of target consumers, developing a service or product that satisfies those needs, determining the most effective way to reach the target market, and creating and implementing a marketing plan that includes strategies for product pricing, promotion, and distribution. Marketing can take many forms, including social media marketing, internet search engine optimization, marketing via email, and conventional marketing and many others. The ultimate aim of marketing is to increase drive sales, brand awareness, and generate leads. Perfect marketing requires a deep understanding of the target market, their needs and desires, and the competitive landscape. Marketing plays a significant role in our society. The opportunity to increase one’s income is a big advantage of investing in the stock market. Analysis and Forecasting of the marketing sector are among the most challenging tasks. That’s also due to a number of factors, such as volatility in the market and a wide range of other independent as well as dependent variables that affect the stock’s market value. So, Stock market experts will find it difficult to perfectly forecast the market collapse or growth using all these factors. There are many marketing analytical tools available previously. This research work was done by the R data analytics tool for forecasting the best result.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"1 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":"115289865","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 Solar-Powered Mushroom Farming for Sustainable Agriculture","authors":"Mahvish Konain, Ravichander Janapati, Syed Musthak Ahmed, Mohammad Mustafa Ahmed","doi":"10.1109/ICSCSS57650.2023.10169539","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169539","url":null,"abstract":"Agriculture plays a crucial role in any country, providing a means to overcome extreme poverty and feed a growing population. Modern agricultural technologies, such as the Internet of Things (IoT), have proven to be superior over conventional methods in increasing productivity and efficiency. This research study focuses on mushroom cultivation, which has gained attention as a secondary source of income due to its rich nutritional value. In present work, an IoT-based solar-powered mushroom farming using a fully controlled environment setup is presented. To monitor the mushroom crop, a variety of sensors, including the DHT11 for temperature and humidity, MQ135 for CO2 level, and soil moisture sensors are incorporated. The proposed system employs a water pump and exhaust fan mechanism to regulate temperature and humidity levels automatically, ensuring optimal growth conditions for the mushrooms. The entire system is powered by solar energy and wirelessly monitored using the Blynk IoT application. The implemented work demonstrates the implementation of IoT to improve agricultural practices and enhances food security besides providing a sustainable source of income for farmers.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"5 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":"115404484","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":"Predicting Buy and Sell Signals for Stocks using Bollinger Bands and MACD with the Help of Machine Learning","authors":"Chaitanya Deepthi Kothapalli, Guggilam Navya, Unnikiran Jaladhi, Shaik Ruhi Sulthana, Dasari Lokesh Sai Kumar, S. P. Praveen","doi":"10.1109/ICSCSS57650.2023.10169500","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169500","url":null,"abstract":"Investors usually take the decisions of buying and selling the stocks by evaluating a company’s performance. These stock prices can be increased or decreased. Technical traders use many indicators to develop trading strategies. These tools can also help ordinary investors. With a little background knowledge, an ordinary person can also buy and sell signals at right time with huge profits and low losses. This is accomplished by combining two dominant technical indicators Moving Average Convergence Divergence and Bollinger bands indicators, to create strategies, and then forecasting the trend using the ARIMA Time Series model. This model is mainly useful for short term trading. This model is provided with 98% accuracy in predicting the trend.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"11 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":"124450128","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":"Five Stage Pipelined MIPS Processor Verification Interface and Test Module using UVM","authors":"M. Ramesh, D. Charan Kumar, L. Yashwanth, G. Jyoteeswara Reddy, K. Naveen, K. Pandiaraj","doi":"10.1109/ICSCSS57650.2023.10169648","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169648","url":null,"abstract":"The Million Instructions Per Second (MIPS) processor design is one of the very earlier foundation of designs for modern-day processor. This brings various different basic concepts of modern-day computing such as Pipelining, Data Dependency handling, and forwarding to one place to enhance the capabilities and speed of processing. This research work proposes to verify few functionalities of a five-stage pipelined MIPS processor. The design under test comes with a total of 16 instructions with a total of 49 variants, 5 pipeline stages, and a hazard unit. Also, this research study presents the verification of this design. The functionality of each instruction is tested using Constrained Random Verification and for implementing verification techniques in System Verilog Unified Verification Methodology (UVM) is used.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"49 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":"124788953","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":"Smart Artificial Intelligence based Drowsy Detection and Alerting System for Vehicles, Industries","authors":"U. Meenakshi Sundereswaran, K. Paramasivam, R. Suresh Kumar","doi":"10.1109/ICSCSS57650.2023.10169560","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169560","url":null,"abstract":"Smart artificial intelligence based drowsy detection system is designed using raspberry-pi processor. The processor is used to detect facial eyebrows closure time period to confirm sleepy state of the user. The user face is fed as input to the processor using 2volt USB camera, and using machine learning OpenCV facial detection algorithm the users face is checked for sleep state. If the user is detected in sleepy state, then the buzzer is turned on and nearby vehicles or obstacles are detected using ultrasonic sensor. If the ultrasonic sensor detects some vehicles (obstacles) at a particular fixed distance, then the vehicle engine will be turned-off automatically. Also, this system can be modified into a system that detects the sleepy state of the worker in the industry and wake him up using buzzer and in motor-based industries the motor can be stopped automatically if the worker is detected in sleepy state. Thus, preventing heavy loss and accidents in industries due to human error.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"50 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":"124945093","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":"Survey on Face Recognition using an Improved VGGNET Convolutional Neural Network","authors":"K. Nuthan, B. V. Santhosh Krishna, R. Gound","doi":"10.1109/ICSCSS57650.2023.10169193","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169193","url":null,"abstract":"Many well-known convolutional neural networks are now based on huge training samples and super computers. It makes lot of issue to overcome this, so this research study develops a deep learning algorithm in VGGNET for Figure classifying and put forward a person’s face to recognition system that called Micro Face. Micro Face employs the CASIA Web Face storage for testing and training samples. This study describes that, when differentiate to the initial algorithm, the upgrade algorithm brings down the parameters, and make that image visible in improving the pooling function, and it would increase the more number of convolution networks for the kernels, which not only decrease the weakness on huge training samples and super computers, it can also carry off 96% identification rate with acceptable identification on performance to achieve and determined value.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"83 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":"122967790","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":"Controlling and Analysis of Battery Energy System in Electric Vehicle","authors":"R. Nagalakshmi, Dr. K. Rahimunnisa","doi":"10.1109/ICSCSS57650.2023.10169296","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169296","url":null,"abstract":"The largest concern in the situation of today, when the majority of people use automobiles is whether or not the car will be safe. Due to the alarming increase in the number of car accidents and life-threatening injuries, it is now essential that vehicles have suitable safety features. It is believed that the concept of smart-hybrid vehicle is appropriate enough to achieve fuel efficiency and improved safely for the user which makes the city and transportation smart at an initial level environmental pollution is worsening as per capita energy consumption increases. With the new alternative energy source, it is essential to reduce the use of traditional fuel-powered engines. Pure electric vehicles cannot be mass-produced due to many problems such as short running time and in-sufficient charging equipment. Therefore, the development of the hybrid electric vehicle over a period of time is of particular importance. The price of battery technology, battery longevity, the quantity of charging stations, and charging time are some of the issues with electrification. Electric vehicle’s energy management and propulsion optimization are achieved in an efficient way using machine learning and deep learning algorithms. The primary factors that will determine the improvement of the propulsion system in EVs include vehicle range, cost of the vehicle, battery pack replacement cost, battery pack life, rapid and simple recharging. The majority of propulsion system development will focus on battery solutions, with the exception of vehicle cost. The charging system of electrical should be robust and reliable as the usage is increasing day by day. Significant efforts were made to address the fuel shortages and pollution issues. The lower emissions and operational costs are due to the variation in primary global consumer demand. The price of battery technology, battery longevity, the quantity of charging stations, and charging time are some of the issues with electrification. Electric vehicle’s energy management and propulsion optimization are achieved in an efficient way using machine learning and deep learning algorithms.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"56 6 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":"123015554","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":"Innovative Method for Predicting Vehicle Speed Detection using Fuzzy Local Information Means and CNN","authors":"R. Umanesan, M. Rao, Sadu Bhavana, Md. Abul Ala Walid, Priyank Udaybhai Trivedi, P. N. K. Reddy","doi":"10.1109/ICSCSS57650.2023.10169523","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169523","url":null,"abstract":"Thanks to technological advancements, now have a number of options for obtaining traffic data. Yet, the precision with which various technologies measure drivers’ actual speeds varies greatly. It is a widespread complaint among transportation researchers and professionals that they lack information of device accuracy. This proposed approach employs video data image processing alongside the Euclidean distance approach to estimate vehicle speeds from a variety of perspectives. To begin, the proposed approach applies preprocessing to the frames we retrieved from the video data in order to reduce the shadow impact. Then, they use a Gaussian Mixture Model (GMM) to pull out the foreground from the background. The next step is to apply a median filter on the resulting foreground. Using Haar Wavelet decomposition for feature extraction, appearance-based hypothesis verification ensures the correctness of hypotheses based on their outward appearance. After that, FILM-CNN is used to train the model which outperforms k-Means and CNN methods.","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":"121611702","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 Approach for E-Commerce System for Sale Prediction with Denoised Auto Encoder and SVM based Approach","authors":"Irfan Abdul Karim Shaikh, R. Pujar, S. P. Kishore, S. Ragamayi, P. V. Krishna, Akabarsaheb B Nadaf","doi":"10.1109/ICSCSS57650.2023.10169595","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169595","url":null,"abstract":"E-commerce, or online shopping, has grown in prominence over the past few years thanks to the proliferation of the Internet. Yet, there are a lot of things that can affect an online store’s success, and if the operators don’t correctly assess their supply and marketing partnerships, they could lose a lot of money. Thus, it is crucial to create a model that can reliably produce high precision sales prediction in order to guarantee the long-term success of e-commerce businesses. The suggested method comprises three stages: preprocessing, Feature selection, and model training. This proposed approach uses zero-phase component analysis and normalization in the preprocessing phase to get rid of noise and inconsistent data. Finally, the model is trained with DAE-SVM after information gain is employed for feature selection. When compared to convolutional neural network and support vector machine models, the proposed model excels.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"36 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":"121722592","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":"Arduino based Wheelchair Fall Detection System using GPS and GSM Module","authors":"Ch.V.Sai Revanth, B. Alekhya, Mohamed Abbas","doi":"10.1109/ICSCSS57650.2023.10169322","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169322","url":null,"abstract":"Wheelchairs are essential for people with physical disabilities, the elderly, and those who are temporarily incapacitated due to illness or accidents. However, traditional wheelchairs require personal effort or assistance from others. With the advancement of technology, patient support and monitoring systems have become more user-friendly and easier to use. This study involves the development of a wheelchair prototype and a fall detection system using Arduino. The prototype and system can be monitored by an overseer or concerned individual. The fall detection system consists of an accelerometer, GSM module, and GPS module. In the event of a fall, the system triggers an emergency buzzer alert and sends a notification to the concerned person along with the location. This innovative system is designed to improve patient safety and security by providing real-time monitoring of wheelchair users. With the use of Arduino, the system is easy to operate and can be customized to meet specific patient needs. The combination of the accelerometer, GSM, and GPS modules provides a reliable and effective fall detection solution. Overall, this study offers a promising solution to improve the quality of life for wheelchair users and their caregivers.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"1 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":"121774341","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}