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2nd Conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Gadde, Sai Sudha Anand, Dama Sasidhar Babu, N. Pujitha, B.V. Sai Reethi, M. Pradeep Ghantasala, G.S. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Student Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Vijayawada India

ISBN: (纸本)9789811902956

To predict the accurate future performance of the students, we consider the data like student’s past and present academic records. It is very important to complete student’s academics in a successful way. The problem is to identify the students whose performance is poor in their courses. So according to their performance and capability there will be a chance to take some remedial actions. There are many literatures on predicting the grades and improving their accuracy. In this paper, we predict the final grades using naive Bayes algorithm and linear regression to predict the final performance of students so that we can get more accurate values. It issues a forecast for every student exclusively, when the expected exactness of the prediction is adequate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear regression Naive Bayes algorithm Performance prediction

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A Novel Approach to Prevent Hijacking of Accounts in the Cloud

A Novel Approach to Prevent Hijacking of Accounts in the Clo...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Ananthoju Vijay Kumar Yeshitla Woinshet Mihret Nesru Kemal Nephtalem Woldeyohanes Ahmad Shah Dagemawi Ameha Computer Science and Engineering Associate Professor Jain University Bangalore India Computer science and engineering student Jain University Bangalore India

According to studies, majority of enterprises globally are now adopting cloud services, and the number of migrants to cloud environments in the industrialized nations is expanding ever since. Moving business data from a centrally hosted server to cloud storage has a lot of merits. However, it increases the chance of data security breach. An aaccount hijacking is the most frequent type of attack that happens in cloud system, it is a type of attack where hackers will get the account holder's credentials and control over the account. Considering the fact that cloud system is vulnerable to attacks, it is crucial to have a remediation mechanism like a secured authentication system. Authentication system which uses textual password can be used in cloud system, but it is vulnerable to regular attacks like brute-force attack, spyware attack, and so many more. The present research mainly focuses on creating a unique authentication system that prevents account hijacking in cloud by mitigating common attacks. To achieve the key objective a graphical password authentication system has been developed with OTP and colouring scheme in order to specifically minimize brute force and shoulder surfing attacks. It is a simple and easy to remember authentication system for customers while minimizing account hijacking in cloud at the same time. It is proved that the present approach is successful authentication system that is less vulnerable to shoulder surfing and brute force attack while involving easy way for customers to remember their passwords without hassle.

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Application of Liquid State Machine for Accurate Rainfall Prediction

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Procedia computer science 2025年 258卷 1554-1564页

作者： Meet K Patel Mohini Darji Bansari Patel Student of Bachelor of Technology Department of Computer Science & Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR) Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Changa 388421 Gujarat India

Adequate rainfall prediction is important in many aspects, the most important being planning in agriculture, correct management of water resources, and early warnings in the event of impending natural disasters. Traditional techniques have been unsuccessful in handling nonlinear and dynamic characteristics of atmospheric processes. In this research, we look into the usage of Liquid State Machines, a type of spiking neural network, in rainfall prediction. One of the main reasons behind this choice is that the LSMs are known to handle temporal data with dynamic memory capabilities robustly, so they would turn out to be the right choice in modeling nonlinear and time-dependent patterns inherent in rainfall data. This will include preprocessing the dataset to normalize the input features and create lag features to capture the temporal dependencies. Then, the constructed high-dimensional state of the reservoir has to be mapped back to the desired output—rainfall amount or quantity—which involves building a model for LSM with an input layer, a dynamic reservoir or liquid state, and then the readout layer. The model will be trained using backpropagation across time to minimize the mean squared error between the anticipated and real rainfall quantities. For this purpose, the LSM model is trained and tested on a dataset built from the historical weather and rainfall data of the Gujarat region. Various metrics such as MSE, RMSE, MAE, and R² are used to quantify the accuracy of the predictions. The LSM model accurately predicts rainfall by capturing temporal and nonlinear interactions in the data.

关键词： Prediction Rainfall LSM Gujarat Time series

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Weather Prediction on Different LSTM Techniques for Time Series Data in Chennai, India

Weather Prediction on Different LSTM Techniques for Time Ser...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Tk, Harikishan Sasikiran, S. Manoj Kumar, G. Joseph, Ferdin Joe John Sathiyanarayanan, Mithileysh Srm Institute of Science and Technology Student of Computer Science and Engineering Ramapuram Campus Chennai India Thai-Nichi Institute of Technology Thai-Nichi International College Bangkok Thailand Research & Innovation Mit Square London United Kingdom

ISBN: (纸本)9798350317060

The prediction of weather has always been an integral factor in our lives as it determines if the day of activity leads to favorable or unfavorable conditions. The existence of different deep learning models has facilitated several major problems which without such techniques would have been a very time-consuming and exhaustive process. In this following paper, we explore various types of LSTM models and analyze which among them provides the best fit for the Time Series data that is present in the Weather data. Upon gathering the datasets, a strategy called Long Short-Term Memory (LSTM) is carried out. LSTM is a deep learning model which is primarily focused on time-series data. By employing such technical improvisations on the same data with multiple models each with their own unique characteristics, we can ultimately determine the best model which has an overall better accuracy. Based on the metrics produced by employing each technique, it is found that our LSTM model which proposed here has the best performance in terms of predicting the data with an overall low error rate and loss during the training phase. © 2023 IEEE.

关键词： Long short-term memory

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Banana Leaf Disease Detection using Advanced Convolutional Neural Network

Banana Leaf Disease Detection using Advanced Convolutional N...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： E Mohanraj R Hariharan S Keerthana K B Cavin Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Any plant’s ability to grow disease-free is crucial for both the environment and human existence. Nevertheless, various diseases, viruses, and fungi affect the plant and highly influence the yield quality and production quantity. If the disease and its origin are not appropriately diagnosed, illness control procedures might be a waste of effort and resources, causing additional plant losses. Plant disease tracking by manual process is quite challenging. It requires a considerable amount of work, expertise in plant diseases, and protracted processing durations. Traditional neural networks require a large number of parameters to train effectively, which can make them slow and computationally expensive. The previous study includes the leaf disease detection performed by CNN, R-CNN, and SVM models. Using the Faster R-CNN, which outperforms other models in terms of computation time and accuracy, this study intends to develop a banana leaf disease detection system.

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Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis

Machine Learning Techniques for Diabetes Prediction: A Compa...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： J Mythili T Surendhar P Suryaprakash K Suresh Kumar Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

As the prevalence of diabetes mellitus rises, more families are being impacted. Most diabetics don’t know much about their health situation or the risks they face before getting a diagnosis. A unique data mining-based method was suggested for the prediction of type 2 diabetes mellitus (T2DM) in this study. The accuracy and robustness of the prediction model are the primary priorities, ensuring flexibility so that it may be used to other datasets. The model is made up of two parts: the enhanced Logistic Regression with Multilayer Perception (LRMLP) and Naïve Bayes (NB) method with a set of preprocessing techniques. To compare the findings with those of other studies, the Diabetes Dataset for Pima Indians was used with the Environment for Knowledge Analysis toolkit. According to the findings, the model’s prediction accuracy is higher than that of earlier research. Additionally, this approach guarantees that the dataset quality is enough. The system was applied to two more diabetes datasets in order to assess its effectiveness further. Both research findings show strong performance. The model is shown to be helpful for managing diabetic health in a realistic manner as a result. For experimental analysis, data from the University of California, Irvine’s (UCI) machine learning repository’s Pima Indian Diabetes Database (PIDD) is used. These classification algorithms’ performance is evaluated using a variety of statistical metrics, including sensitivity (recall), precision, F-score, and accuracy. The measurement of correctly and erroneously classified cases is called accuracy. According to the testing findings, LR-MLP outscored other classification algorithms and provides the maximum accuracy of 96.3%.

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Crime Type Prediction based on Various Occurrence using Parallel LSTM

Crime Type Prediction based on Various Occurrence using Para...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： M Saradha T Nithesh Priyan D U Shreeram S Viknesh Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well.

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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A Comprehensive Review on Detection of Dacrocytes from Blood Smear Images using ML Algorithms

A Comprehensive Review on Detection of Dacrocytes from Blood...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： D. Kavitha B. Padmavathi Shakthi Priya S Sanjana N Sidesh S Department of Computer Science and Engineering Assistant Professor of Easwari Engineering College Chennai India Department of Computer Science and Engineering Student of Easwari Engineering College Chennai India

A nation’s capacity to develop is greatly impacted by the prevalence of anemia, a major public health issue. Despite this, it is a sickness that many developing countries choose to overlook. A million people die each year from iron deficiency, which is the underlying cause of anemia in at least half of those cases. Approximately 75% of these fatalities take place in Southeast Asia and Africa. One of the leading causes of death on a global scale is iron deficiency anemia (IDA). The use of machine learning techniques for the prediction of anemia was the focus of this work. Light Gradient Boosting Machines, Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, and K-Nearest Neighbors were the five machine learning models studied in the study. The voting classifier method is used to merge various models in order to improve the accuracy of predictions. The need of accurate sickness forecasting by medical practitioners is highlighted by the study. Being able to predict when anemia may occur is crucial for effective prevention and treatment of the condition. This study proves that specific machine learning algorithms can improve disease prevention and treatment by predicting the occurrence of anemia. Using advanced algorithms and data processing techniques can help doctors make better predictions and decisions, which in turn improves patient outcomes. According to the results, the vote classifier has a 99.95% accuracy rate.

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Reliability of Wireless Mesh Networks and Smart Grids: A Review

Reliability of Wireless Mesh Networks and Smart Grids: A Rev...

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Intelligent Technologies (CONIT), International Conference on

作者： Vishal Singh Dhanpratap Singh Student of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India

Technology and network topologies are changing quickly. Mesh topology has been more popular among other topologies due to its many benefits, including several paths, linkages, and many others. Given this, wireless mesh networks are quite advantageous. WMNs are significantly more stable than other network topologies, accommodate a wide range of protocols, and are self-healing. However, there were a number of underlying problems, including mobility, service quality, link failures, and so forth. This mostly falls under the category of dependability. WMNs are a promising technology used in many different industries and fields, including automation, healthcare, disaster management, and other areas. Wireless Mesh Networks have evolved into a more cost-effective substitute for traditional networks. In order to raise the number of nodes and boost the path's redundancy, one of the most popular solutions to problems like end-to-end dependability is to increase the number of nodes. However, that has inherent problems like cost and the best node location. Wireless mesh networks have been the subject of extensive research. A few examples include routing design, resource allocation, mobility, security, scalability, and energy savings. Using case studies from various locations, we analysed different WMNs potential and constraints in the field, particularly linked to smart grids, reliability issues, and we looked at prospective directions for future research.

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