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Robust and Predictive Deep Neural Networks for Stock Price Forecasting 3

Robust and Predictive Deep Neural Networks for Stock Price F...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Binekar, Ekansh Bhanudas Raghuvanshi, Ashmit Maindola, Parth Bagra, Mridul Pradhan, Nihar Ranjan Khan, Tauseef VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350381931

Forecasting stock prices has become a widely researched domain, and conventional prediction methods primarily rely on statistical and econometric models. Nevertheless, these methods encounter challenges when confronted with dynamic, nonstationary time series data. The advent of the internet alongside the surging prevalence of social media has brought about a significant shift. Online news snippets and user comments now frequently mirror investor sentiments and perspectives on various stocks, encompassing a wealth of crucial insights that can greatly enhance stock price prediction. In this paper, forecasting of stock prices has been implemented using two cutting-edge deep learning models, namely Long Short-Term Memory and Gated Recurrent Unit. Both models have been evaluated on two publicly available datasets, namely the Google stock price dataset and Tesla stock price dataset. Experimental results show that LSTM has achieved accuracies of 87.91% and 85.67%, while GRU has achieved accuracies of 93.62% and 88.78% on the Google dataset and Tesla dataset, respectively. Keeping in view the complexity of the dataset, the obtained results, in turn, pave the way for practical applications within the dynamic realm of real-time stock market prediction. © 2024 IEEE.

关键词： Long short-term memory

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Reinventing Grocery Shopping with Reinforcement Learning 3

Reinventing Grocery Shopping with Reinforcement Learning

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Choppa, Anvitha Kesavarshini, appikatla Bhavishya, Mallampati Rao, Yamarthi Narasimha School of Computer Science and Engineering VIT-AP University Amaravathi Guntur522237 India

ISBN: (纸本)9798331532420

Grocery shopping stands as a quintessential task in everyday life, yet it often involves complex decision-making processes influenced by factors such as individual preferences, budget constraints, and time limitations. In response to the intricacies of this routine chore, the integration of Artificial Intelligence (AI), particularly Reinforcement Learning (RL), has emerged as a promising avenue for optimization. RL empowers autonomous agents to navigate the grocery shopping landscape by learning optimal strategies through iterative interactions with the environment. By employing RL algorithms, these agents can adapt and refine their decision-making processes over time, aiming to maximize cumulative rewards such as cost savings, time efficiency, and customer satisfaction. Such personalized and dynamic approaches hold significant potential for revolutionizing traditional grocery shopping experiences, offering tailored recommendations, mitigating decision fatigue, and ultimately streamlining the overall shopping journey for consumers. In this context, the application of RL techniques serves as a powerful tool to optimize the process of grocery shopping. By formulating the problem within a framework of states, actions, transition probabilities, and rewards, RL algorithms enable an autonomous agent to learn an optimal strategy for navigating through a network of shops to fulfill a given shopping list efficiently. In this work, through the implementation of value iteration, Q-learning, and representation policy iteration methods, the agent can learn and refine its decision-making policy, ultimately simplifying and enhancing the grocery shopping experience for consumers. © 2024 IEEE.

关键词： Budget control

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Brain Tumor Classification Using Optimal Features and Ensemble Learning Algorithms 1

Brain Tumor Classification Using Optimal Features and Ensemb...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Kuppusamy, P. Kodavaluru, Veena Sree Manojkumar Bogar, Shruti VIT-AP University School of Computer Science & Engineering India

ISBN: (纸本)9798350342796

The use of modern technology for better diagnosis of life-threatening diseases like brain cancer is becoming increasingly vital for the medical field. The manual verification of brain images lacks accuracy due to human errors. The Magnetic Resonance Imaging (MRI) dataset contains information on the tumor's existence, and different types of brain tumors like glioma, meningioma, and pituitary. Recent developments in the fields of Machine Learning (ML) can substantially improve the accuracy of medical scanning image (a.k.a MRI images) processing. This paper focuses on utilizing different ML algorithms to examine brain MRI images and identify the presence or absence of tumors. First, the MRI images are enhanced using an adaptive median filter. Then, the MRI dataset is pre-processed using canny edge and Gabor filter to extract features. After that, the dimension of the feature is decreased by applying Principal Component Analysis (PCA) method which decreased the dimension drastically. Several ML algorithms are then applied, namely, logistic regression, Support Vector Machine (SVM) with linear, polynomial, and RBF kernels, ensemble methods Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost). The experimental results showed that (a) the performance of the ensemble method XGBoost is better than other ML algorithms such as logistic regression, SVM with linear, polynomial, and RBF kernels, Random Forest, Gradient Boosting in terms of accuracy, precision, recall, and F1-score;(b) the XGBoost consumes more training time due to the more number of trees, depth, and iterations, and (c) the experimental results proved that feature reduction using PCA decreased the training time in ML algorithms. © 2023 IEEE.

关键词： brain tumor feature extraction feature reduction Gradient Boosting Principal component analysis XGBoost

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A Novel Classification approach for Smart Card Fraud Detection

A Novel Classification Approach for Smart Card Fraud Detecti...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Tekkali, Chandana Gouri Natarajan, Karthika Bhuvanesh, Valiveti Manikanta VIT-AP University School of Computer Science & Engineering AP Amaravati India

ISBN: (纸本)9798350344387

The primary objective of this research is to monitor and detect fraudulent transactions within payment systems. As the use of smart transactions continues to increase, so does the prevalence of fraudsters. These individuals consistently seek to disrupt others by engaging in illegal activities, resulting in financial losses for customers while benefiting the fraudsters. Therefore, it is crucial to prioritize the monitoring and early detection of fraud. This article presents a unique Machine Learning (ML) model for the early identification of fraudulent transactions, utilizing a range-based classification approach. Many existing machine learning algorithms struggle to improve the misclassification rate while maintaining good accuracy. However, the proposed range-based classification model exhibits strong accuracy while addressing the issue of rising misclassification rates. The experimental results are presented through a bar graph and a classification report. Additionally, compare the proposed model with several existing technologies to showcase its effectiveness. © 2023 IEEE.

关键词： Classification Credit card fraud Machine learning preprocessing

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Automated Monitoring System for Healthier Aquaculture Farming 2

Automated Monitoring System for Healthier Aquaculture Farmin...

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2nd IEEE International Conference on Advances in Computing, Communication and applied Informatics, ACCAI 2023

作者： Kusuma, Charvi Meela, Neha Saridena, Saketh Kanuparthi, Yamini Haripriya Kaushik Mantha, Narayana Sumathi, D. VIT-AP University School of Computer Science and Engineering AP Amaravathi India

ISBN: (纸本)9798350315905

Maintaining a healthier aquaculture system is difficult and without regular monitoring, significant losses may occur. The condition of the pond is a key concern and if the sites are left uncleaned, the number of dead fish will grow over time. Manually identifying these dead fish and consistently keeping aquaculture facilities in good condition is difficult, hence automation is the solution. This research offers a reliable model that can be applied to a UAV surveillance system and has been trained on thousands of images to recognize dead fishes floating over the surface, indicating an imbalance in the pond. By alerting the farmer of the presence of dead fish, immediate steps could be made to mitigate its impacts. We used the YoloV5 architectures to effectively detect dead-fish objects in real time and built the model to an accuracy of 88% considering various parameters like low light conditions and cropped versions of the fish image. © 2023 IEEE.

关键词： Aquaculture

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Gene Expression Analysis Using Machine Learning : A Multi-Dataset approach 7

Gene Expression Analysis Using Machine Learning : A Multi-Da...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Dhanush, K Sai Kondaveeti, Hari Kishan Chandrika, M. Putchakayala, Rohan School of Computer Science and Engineering VIT-AP University India

ISBN: (纸本)9798350340600

The expression levels of a fraction of the genes directly control the regulatory or functional activities inside the cell. Individual gene activity is recorded in gene expression, which helps reveal underlying cellular dynamics. High-throughput investigations require grouping genes based on their temporal expression patterns, often achieved using unsupervised machine learning methods. The majority of clustering methods, however, either need to account for the temporal structure of the data or suffer from gene expression that needs to be longer in duration. The hierarchical framework can resolve these problems we suggest in this research, a revolutionary learning-based system for grouping known as Random Forest (RF), Naïve Bayes (NB) and C4.5. The anticipated model first transforms the data to provide richer data representations. For the created images, a deep network classifier is then used. Compared to frequently employed algorithms, the proposed classifiers' assessment show that this new paradigm is useful for prediction and classification. Keywords- Gene Expression, Prediction, Classification, Accuracy, Data Representation © 2023 IEEE.

关键词： Gene expression

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Cybersecurity in Healthcare: IOT Security for Medical Devices 15

Cybersecurity in Healthcare: IOT Security for Medical Device...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Aitty, Prajwal Sri Tej Kumar, A.V.S. Hemanth Vamshi Krishna, T.K. Nadagoudar, Vishal.R. Rajamohanreddy, Nadenlla Turukmane, Anil vithalrao VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350370249

In the rapidly evolving Internet of Things (IoT) era, the healthcare sector's fast embrace of digital technology and connected medical devices has revealed hitherto unknown prospects for improved patient care and operational efficiency. However, the digital revolution has resulted in an increase in cybersecurity vulnerabilities unique to the healthcare business. To maintain the safety of patient data and medical equipment, this study proposal, Cybersecurity in Healthcare: Securing Patient Data and Medical Devices, explores the primary cybersecurity challenges threatening patient data integrity. This study suggests a number of methods focused on protecting personal health information and preventing unauthorized access to connected medical devices. These include in-depth security assessments of medical equipment, secure communication protocols, the development of privacy-preserving data handling policies, and proactive steps to prevent insider threats and phishing schemes. This study intends to protect patient privacy, reinforce medical infrastructure, and eventually improve the quality and security of healthcare services by improving cybersecurity in healthcare. © 2024 IEEE.

关键词： Block Chain Cyber Security Health Internet of Things (IoT) Medical devices

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An Innovative Blockchain-based System for Employees in the Healthcare Industry 14

An Innovative Blockchain-based System for Employees in the H...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Turukmane, Anil V. Computer Science & Engineering VIT-AP University Amaravati AP Vijaywada India

The pandemic caused by the COVID-19 virus has unquestionably opened our eyes to the possibility that we may run out of essential supplies, such as medications and medical equipment, which would result in people's deaths and increased anxiety. Insufficient funds and raw materials aren't the only factors contributing to this situation;there's also another factor that's much more discouraging. Even in the midst of these challenging circumstances, some individuals have been able to see an opportunity in the scenario that allows them to maintain and store vital equipment such as personal protective equipment kits, masks, sanitizers, and the like, and then start black-selling such items. This very problematic circumstance has arisen as a direct result of deficiencies in the supply chain's control, transparency, and traceability. Blockchain technology presents itself as a potentially useful answer to this issue. The information may be tracked and traced using blockchain technology, which can also be used to store the data in a decentralised and immutable manner, ensuring that it remains private and is protected from unauthorised users. This article discusses the many use cases for healthcare personnel as well as the various constraints of the present blockchain architecture for supply chain. [Citation needed] The shortcomings of the current blockchain framework for supply chains are then addressed via the proposal of a new framework. © Grenze Scientific Society, 2023.

关键词： COVID-19

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Feature Selection Using Data Mining Techniques for Prognostication of Cardiovascular Diseases 5th

Feature Selection Using Data Mining Techniques for Prognosti...

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5th EAI International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

作者： Vanami, Naga Venkata Jashwanth Chintalapati, Lohitha Rani Challagundla, Yagnesh Mohanty, Sachi Nandan School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9783031505706

Cardiovascular diseases (CVD) are a major cause of mortality world-wide causing about 17.9 million deaths per year. Cardiovascular illnesses are a group of conditions that affect the heart and blood arteries. These illnesses may have an effect on various parts of the heart and/or blood vessels. CVD encompasses coronary artery disorders (CAD), such as myocardial infarction and angina. To reduce the risk and deaths caused by cardiovascular diseases it is important to predict it at an early stage. It is crucial to be aware of these cardiac disease-related signs in order to forecast outcomes and offer a solid foundation for diagnosis for which data mining and feature selection prove to be useful. However, manual analysis and prediction are laborious and tiring due to the sheer volume of data. In this study, data science is used to predict cardiac problems. The potential method for heart disease prediction is one that analyses the relationships between variables and extracts hidden knowledge from the data. Through a variety of indications, our study attempts to anticipate cardiac disease correctly and promptly. We propose a cardiovascular disease prediction model which uses a dataset obtained from Kaggle on which we perform various data pre-processing techniques on which feature selection is done and the refined data is given to different machine learning models for the prediction of the disease. We obtained the highest accuracy of 99.4% using Random Forest, demonstrating the effectiveness and dependability of the heart disease prediction approach we presented. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Feature Selection

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Voting System using Blockchain 15

Voting System using Blockchain

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Turukmane, Anil V. Ganesh, Jetty Sai Sunkara, Keerthi School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331300579

Even though electronic voting significantly decreased election costs and offered some convenience, it has been criticized for being unreliable when compared to the traditional pen and paper method because it is physically possible for someone with physical access to the system to change the results. A central system is also necessary to oversee the entire process, including electronic voting. Election results and tracking of those results. Given how easy it is to target votes, voters are not entirely safe. It also poses a severe danger to voting rights and transparency. This article offers a solution to the problems associated with traditional elections by utilizing blockchain technology, which has become a fascinating technology for various applications due to its distinct qualities that set it apart from other technologies. The aim of this project is to develop a decentralized electronic voting system that verifies voter identity through an honest and transparent voting process and protects voter privacy during data transfer using blockchain technology. © Grenze Scientific Society, 2024.

关键词： Blockchain

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