2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)最新文献

{"title":"Comparison of Hybrid Novel Pearson Correlation Coefficient (HNPCC) with K-Nearest Neighbor (KNN) Model to Improve Accuracy for Movie Recommendation System","authors":"Syed Mohammed Shoaib, J. K","doi":"10.1109/ACCAI58221.2023.10200272","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200272","url":null,"abstract":"A hybrid recommendation model based on the HNPCC and the K-Nearest Neighbor (KNN) model were evaluated to increase movie recommendation accuracy. The information gathered from the movielens dataset, which contains 23 attributes and with 30 samples, for use in a hybrid movie recommendation system. The sample size for each set is 30 people, and pre-test power is 0.8.Using an independent t-test to decide statistical significance with p<0.05, it was found that HNPCC has a slightly higher accuracy of 94.3% significantly, while KNN has a lower accuracy of 92.9%.As a result of the comparison, the HNPCC outperforms the KNN in terms of enhanced accuracy.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"3 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873757","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 Biometric Vehicle Theft Identification and Prevention Scheme using GPS Location Tracking","authors":"K. Jeevitha, J. Venkatesh, V. Indhumathi, K. Veni, R. Prem Kumar, Devadarshini. M","doi":"10.1109/ACCAI58221.2023.10199574","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199574","url":null,"abstract":"The demand for security is rising in every sector of society as the pace of technological development and scientific discovery quickens. Right now, having access to a car is essential. At the same time, preventing theft from happening is crucial. The expense and complexity of conventional methods of protecting vehicles are substantial. There is no further action or option that might help the car's owner recover their vehicle once it has been stolen. The usage of biometrics such as fingerprints is widespread and is now routine in many settings, including businesses, public buildings, educational institutions, and more. The primary objective of this study is to secure the car against unwanted entry using fingerprint recognition technology that is both quick and simple to implement, as well as clear, reliable, and cost-effective. A method was necessary to track where each vehicle was at all times and how far it had gone. These days, active vehicle monitoring and GPS technologies are used to keep tabs on our motor vehicles. Images of fingerprints are taken by the sensor, and the sensor then compares each fingerprint it reads to a database of recorded fingerprints or to a module inside the system. A GPS and GSM-based anti-theft car monitoring system would be the most cost-effective means of tracing a vehicle's whereabouts and could even be used to locate stolen vehicles. It is a built-in unit that utilizes GPS and the GSM network to pinpoint the exact location and trajectory of a moving vehicle (GSM)","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131109847","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":"Coffee Price Prediction: An Application of CNN-BLSTM Neural Networks","authors":"Mekala K, L. V., Jagruthi H, S. Dhondiyal, Sridevi.R, Amar Prakash Dabral","doi":"10.1109/ACCAI58221.2023.10199369","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199369","url":null,"abstract":"Coffee is one of the world's most popular beverages, and its production and demand have been steadily increasing in recent years. In 2020/21, worldwide coffee output hit 174.5 million bags, according to the International Coffee Organization. coffee year, which is a 1.9% increase from the previous year. The demand for coffee is driven by various factors, including changing consumer preferences, economic conditions, and demographic trends. In particular, the growing popularity of specialty coffee and the increasing consumption of coffee in emerging economies have contributed to the growth in demand. However, the coffee market has also faced challenges such as climate change, which can affect coffee production by altering the growing conditions, and the COVID-19 pandemic, which has disrupted supply chains and caused fluctuations in prices.In terms of regional When it comes to coffee output, Brazil leads the globe, followed by Vietnam, Colombia, and Indonesia. These countries collectively account for more than 60% of global coffee production. The United States, Germany, and Japan are the largest importers of coffee.Overall, coffee continues to be an important commodity in the global market, with a significant impact on the economies of producing countries and the daily routines of consumers around the world.In this article, we propose a fresh method of coffee price prediction using the The BLSTM (bidirectional long short-term memory) and CNN (convolutional neural networks) models.We start by collecting historical coffee price data from publicly available sources and preprocess it using feature engineering techniques. The The collected data was then split into training and validation sets and testing sets and feed it into the proposed CNN-BLSTM model.The CNN extraction by using layers the relevant features from the input data and reduce its dimensionality, while the BLSTM layers learn temporal dependencies in the data and capture long-term patterns. The outputs from the BLSTM layers are then fed into fully connected layers, which output the final price prediction.We Use measures like MSE, RMSE, and MAE to measure how far off you are from your target assess how well our suggested model performs (MAE)in both the test and validation data. Our According to the obtained CNN-BLSTM model outperforms several other state-of-the-art machine learning models, including traditional time-series models, on the same dataset.Overall, our approach demonstrates the effectiveness of combining CNN and BLSTM models for coffee price prediction and can be extended to other related forecasting problems.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"285 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116441408","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":"Improving the Accuracy of Intrusion Detection System in the Detection of DoS using Naive Bayes with Lasso Feature Elimination and Comparing with Naive Bayes without Feature Elimination in Wireless Adhoc Network","authors":"A.Senthil kumar, T. Nagalakshmi, R. Scholar, Corresponding Author","doi":"10.1109/ACCAI58221.2023.10199248","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199248","url":null,"abstract":"The aim of this research is to create an InnovativeNaive Bayes with Lasso Feature Elimination Intrusion Detection System (IDS) that uses Naive Bayes without feature elimination (Group 1) and compare its performance to that with Lasso feature elimination (Group 2). NSL-KDD Dataset was used to design the data set and collect an IDS. A total of 38 samples were obtained from each of the 19 groups. The data was analyzed using the SPSS application for statistical analysis. Both groups were subjected to an independent sample T test, which yielded a significance of 0.595 for accuracy. Here p > 0.05. For Group 1, the mean accuracy of Naive Bayes without feature elimination is 0.7432, and for Group 2, the mean accuracy of Lasso feature elimination is 0.6005. Conclusion: The accuracy of the Naive Bayes with Lasso feature elimination is similar to that of the Naive Bayes without feature elimination, but here significance is existing.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127077910","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":"Comparative Analysis of Shannon and Hilbert Envelograms for Heart Sound Segmentation","authors":"Mahesh Veezhinathan, Melwin Meston T, Raama Narayanan Anantha Narayanan, Geethanjali B","doi":"10.1109/ACCAI58221.2023.10199584","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199584","url":null,"abstract":"The objective of this study is to compare the performances of envelope extraction algorithms used to segment heart sounds under normal conditions as well as during valvular disorders. Two different approaches were followed to implement the extraction of envelope of the first and second heart sounds-Normalized Average Shannon Energy (NASE) and Hilbert Transform method. Envelope extraction using the above stated algorithms were performed on raw as well as signals denoised using Discrete Wavelet transform (DWT) and the time duration parameters associated with each heart sound were computed after segmentation. Based on the results obtained, the efficiency of each algorithm was studied and their advantages and disadvantages were compared.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873083","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":"Data scientific approach to detect the DoS attack, Probe attack, R2L attack and U2R attack","authors":"S. R, Sivasundarapandian S, Aranganathan A, V. V, Rajinikanth E, G. T","doi":"10.1109/ACCAI58221.2023.10199636","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199636","url":null,"abstract":"Data reliability is compromised by various cyber-attacks. Computational infrastructure is completely disturbed, broken or guided by these attacks. The current status of cyberspace foretells uncertainty for the future of the Internet and its rising user base. Data collected by the sensors and other input devices can be easily stolen by the unidentified user. It is a severe threat to the programming environment and individual personals. It is necessary to take into account the advanced technologies to counterfeit these cyber-attacks. Existing algorithms are decoded over certain period of time. Because of this always important to adapt the new technology that can prevent cyber-attacks. In this paper, various cyber-attacks predictions are analyzed and combines as a group based on its features. After analyzing the various cyber-attacks and its classification, recent technologies which can prevent the cyber-attack is studied. One of the main technologies that can able to learn themselves is the machine learning. Networking environment must use advanced machine learning approaches to protect the Data. Machine learning technique is classified as supervised and unsupervised technique. Supervised machine learning technique uses features that can be extracted from the source dataset. The most effective machine learning algorithm for predicting the types of cyber-attacks has been determined through a comparison study of different algorithms. We categorize attacks into four categories: R2L attacks, DOS attacks, U2R attacks, and probe attacks. Various machine learning algorithms are applied to detect and rectify the cyber-attacks. Their performances are compared and analyzed in terms of accuracy, F1 score, precision and recall.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132844669","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":"CO2 Emission Rating by Vehicles using Supervised Algorithms","authors":"Sudarshni Ramesh, Shiny Shalynn I M, J. Justus","doi":"10.1109/ACCAI58221.2023.10200707","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200707","url":null,"abstract":"Personal vehicles play a significant role in contributing to the issue of global warming. Gasoline used in cars emits an estimated 24 pounds of carbon dioxide and other greenhouse gases per gallon, which accounts for roughly 20% of all emissions. Over five pounds of heat-trapping pollutants are produced throughout the fuel's extraction, manufacture, and delivery, while it's important to note that cars release over 19 pounds per gallon directly from their tailpipes. At present, gas-powered vehicles typically achieve a fuel efficiency of around 22.0 miles per gallon, covering an annual distance of 11,500 miles. As a consequence, the combustion of one gallon of gasoline results in approximately 8,887 grams of CO2 being released into the atmosphere. In 1998, the automobile industry pledged to reduce new car emissions by 25% by 2008. At that time, CO2 emissions from new cars were approximately 203g/km. Currently, the average emissions of vehicles stand at approximately 170g/km and it is projected that they will not decrease to 140g/km until after 2020. The amount of CO2 emitted by a standard passenger car is typically around 4.6 metric tons per year, but this can fluctuate depending on factors such as fuel type, fuel efficiency, and mileage. While predicting emissions becomes more challenging as more variables come into play, some experimental designs consider controllable variables and their interactions. To predict gas emissions, we've developed a model that utilizes a car's attributes to determine if it exceeds the CO2 threshold. If it does, the RTA will take action. One of the best techniques for predicting CO2 emissions is supervised machine learning.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688547","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 Fertilized Vertical Irrigation Control and Management System","authors":"Lalitha Sree Machavarapu, Iswarya S, R. S., V. Meenakshi","doi":"10.1109/ACCAI58221.2023.10200148","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200148","url":null,"abstract":"Destiny agricultural generation, known as \"agriculture four.0,\" is product of a number of important allowing technologies. Agriculture is an essential issue of every country’s development, and it frequently requires the right quantity of fertilization, irrigation, and seasoning to provide an enough quantity of food goods. Fertilization systems have demonstrated effective in promoting plant increase, development, and large-yield crop manufacturing. The reason of this undertaking is to layout an automatic fertilized vertical irrigation management and control system for the improvement of soil vitamins and porosity via the timely application of fertilizer and water tiers needed to sell plant improvement. This automatic vertical farming system using sensors effectively assesses the requirements of plants and it detects the moisture content material and fertilizer content material in the soil. Thus the plants are provided with the required minerals and water so as to get greater yield together with reduction in time and cost.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130817063","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":"Underwater Photography Noise cancellation Using Artificial Intelligence and Deep Learning","authors":"Sanjiv. S, K. R., R. Ranjith, A. Chandrasekar, V. K.","doi":"10.1109/ACCAI58221.2023.10200197","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200197","url":null,"abstract":"Cameras submerged underwater are widely used to see the ocean floor. Unmanned underwater automation, autonomous underwater vehicles, and in situ ocean sensors are common places to find them (AUVs). While being an essential sensor for keeping track of underwater landscapes, recent underwater camera sensors have several issues. Because of how light moves through water and the biological activity at the seafloor, underwater photographs are typically cluttered with scatters and noise. Over the past five years, a variety of tactics have been developed to Important facts of oceanographic study include image processing and underwater sensing. One prior challenge is light absorption with a scattering effect, which reduces the image quality in underwater conditions with respect to its ground truth.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133277391","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 Innovative Method for Encrypting Pictures without a Key","authors":"Mrs. D. Navaneetha, Dr. Bhaludra R Nadh Singh, Ms. Nelanti Renuka, Ms. Rishitha Jeedipally, Ms. Bachireddy Sahasra","doi":"10.1109/ACCAI58221.2023.10200774","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200774","url":null,"abstract":"In order to circumvent the constraints imposed by picture size and key records, as well as the prohibitive computing costs associated with key-oriented strategies, several researchers have turned to brute-force assaults. This study proposes a better keyless method of encrypting lossless RGB photos. For the purpose of picture encryption, two methods are in use: image splitting and multiple sharing. By scattering the pixel bit randomly over the picture, we hope to improve the degree of security. SST is also a great way to increase storage space. For this keyless method, we'll use reversible encryption to protect the original file without sacrificing quality.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"76 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113959389","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}