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Enhanced Stock Market Trend Prediction on the Indonesia Stock Exchange Using Improved Bacterial Foraging Optimization and Elitist Whale Optimization Algorithms

Enhanced Stock Market Trend Prediction on the Indonesia Stoc...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepak Kumar Priyanka Pramod Pawar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula K S Shwetha Department of Information Technology University of the Cumberlands Williamsburg USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

It was difficult to find low-risk firms in 2022 due to the large number of 825 listed on the Indonesia Stock Exchange (IDX). Essential concerns in financial works include stock price forecasting and price movement forecast. The stock market research has shown encouraging outcomes in the past. We still don't have a consensus on the finest stock value forecasting classical due to the fact that each study report has different architecture and uses different stock issuers. This paper presents a new method for optimizing bacterial foraging that uses optimal deep learning to forecast stock market trends. For the prediction process, the given model employs a multiplicative long short term memory (MLSTM) perfect, with IBFO serving as the model's fine-tuner. The research paper presents a modified whale optimization technique for feature selection. This research proposes an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) to fix the issue where the whale optimization procedure does not strike a balance among exploration and exploitation and instead falls into the local optimum. To improve the odds of evading the local optimum, the shrinking encircling mechanism employs an elitist tactic based on random Cauchy mutation. Using the random Cauchy mutation as a starting point, the method cleverly selects the population with the best mutation solutions to move on to the next iteration. To strike a compromise among the two, the logarithmic spiral mechanism makes use of a nonlinear parameter. There are 2,588,451 entries in the dataset representing 727 IDX firms' open, highest, lowest, closed, and volume transactions. Various stock time records are used to examine the trial findings using current methodologies in terms of distinct metrics.

关键词： Microorganisms Spirals Predictive models Feature extraction Market research Prediction algorithms Whale optimization algorithms Stock markets Forecasting Tuning

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Enhanced Predictive Model to Diagnose Coronary Heart Disease Using Decision Tree

Enhanced Predictive Model to Diagnose Coronary Heart Disease...

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Data science and Network Security (ICDSNS), International Conference on

作者： Sujata Joshi Ananya Dixit Mydhili K. Nair Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Information Science and Engineering B.M.S. Institute of Technology and Management Bangalore India School of Computer Science RV University Bangalore India Bangalore India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

As per WHO reports, Heart diseases or Cardiovascular Diseases (CVDs) rank as the leading cause of death worldwide. Approximately 17.7 million individuals succumbed to CVDs in 2015, accounting for 31% of all global fatalities. Among these fatalities, an estimated 7.4 million were attributed to coronary heart disease, one of the prevailing forms of heart disease.. According to the Global Burden of Disease study, nearly quarters (24.8%) of all deaths in India are due to CVD. Hence there is a need for careful and periodic methods of examining heart related diseases. The majority of cardiovascular diseases found in India are coronary heart diseases (CHD) The main contribution of this work is to develop efficient predictive models for Coronary heart diseases using decision trees. In this work, the predictive models are developed for diagnosis of heart disease using the cardiovascular disease dataset from UCI repositories with the objective of improving the accuracy. We have also obtained echocardiograph data obtained from M. S. Ramaiah hospital and used in this work. It is found that the data used in this work is heterogeneous in nature and the decision trees work well on heterogeneous data and also they are expressive and interpretative, we have opted to use decision trees. The predictive model namely Enhanced_Pred_Model CVD-DATA-DT is developed using both the datasets. It is found that the model shows marginal accuracy increase from 77% in baseline model to 80% by the proposed model. The proposed method has better performance in terms of accuracy as well as error rate. The models are also tested on the echocardiograph data for prediction of LVH event and has shown improvement in the performance.

关键词： Heart Accuracy Hospitals Error analysis Predictive models Network security Prediction algorithms Data models Decision trees Cardiovascular diseases

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Hybrid Optimization Based Convolutional Neural Network for Intrusion Detection System

Hybrid Optimization Based Convolutional Neural Network for I...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sureka, N. Girirajan, B. Kumar, Komuravelly Sudheer Priya, B. S. Deepa Sowmya, M. Agni College of Technology Department of Biomedical Engineering Chennai India Sr University Department of Ece Warangal India Sr University School of Computer Science & Artificial Intelligence Warangal India Bannari Amman Institute of Technology Department of Cse Sathyamangalam Erode India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

Intrusion Detection System (IDS) is one of the common deep learning (DL) techniques that are used to find and identify outliers to prevent adversarial attacks, fraud, and network intrusions. This paper proposed a hybrid Particle Swarm Optimization and Grey Wolf Optimization (HPSOGWO) based Convolutional Neural Network (CNN) model for intrusion detection systems. The proposed HPSOGWO is evaluated on the NSL-KDD dataset which contains 5 different classes and 41 features. The PSO is good for global exploration and GWO is for local exploitation. The hybrid PSO and GWO algorithm achieves a better balance between exploration and exploitation and enhances convergence speed. The CNN is utilized to enhance the system's capability to identify and classify intrusion accurately and effectively. The proposed HPSOGWO-based CNN model attains better results by utilizing evaluation metrics like accuracy, precision, recall, specificity, and f1-score values about 0.9918, 0.9852, 0.9908, 0.9879 and 0.9767 correspondingly which is comparatively higher than existing techniques like Chicken Swarm Optimization based Deep Long Short-Term Memory (ChCSO based Deep LSTM), Deep Neural Network (DNN) and LSTM. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Grey Wolf Optimization Internet of Things Intrusion Detection System Particle Swarm Optimization

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Beam Selection Approach in Millimeter Wave Communication using Multi-Objective Prairie Dog Optimization

Beam Selection Approach in Millimeter Wave Communication usi...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Reddy, R. Archana Alzubaidi, Laith H. Hussein, Abbas Hameed Abdul Pradeep Kumar, S. Rajaram, J. SR University School of Computer Science and Artificial Intelligence Warangal India The Islamic University Najaf Iraq Ahl Al Bayt University College of Pharmacy Karbala Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Malla Reddy Engineering College for Women Hyderabad India

ISBN: (纸本)9798350315455

The selection of beam relies as a major conflict in wireless communication systems. The beam selection has been widely utilized in millimeter wave bands which helps to mitigate the path loss and minimize the energy consumption. The existing beam selection approaches results in high computational and communication costs and hinders the widespread deployment of mm Wave communications. Moreover, the existing approaches faced issues related to select the users for multiple radio frequencies and this suffers the users to face computational burden and results in beam optimization problem. So, this research utilized a Multi-Objective Prairie Dog Optimization (MOPDO) algorithm to perform an effective beam selection for multiple users in a single iteration. MOPDO considers the multiobjective functionalities such as beam power, energy efficiency, spectral efficiency and computational time. The experimental evaluation of proposed approach is evaluated with Alternative Grey Wolf with Beetle Swarm Optimization (AGW-BSO). For example, computational time of the proposed MOPDO is 320.87 seconds whereas the existing AGW-BSO is 355.72 seconds. © 2023 IEEE.

关键词： Beam selection Computational time Energy efficiency Millimeter wave communication Multi-Objective Prairie Dog Optimization

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An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Pasha, Syed Nawaz Anitha, D. Anbunathan, R. Gobinath, C. Madhura, G.K. Sr University School of Computer Science & Artificial Intelligence Warangal India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sa Engineering College Department of Mathematics Chennai India Reva University School of Computing and Information Technology Bangalore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification. © 2023 IEEE.

关键词： Contrast-Limited Adaptive Histogram Equalization Convolution Neural Network Deep Learning Iris Recognition and Support Vector Machine

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Development of Healthcare Model Using AR-DVAE with Mayfly-MLP-BPN for Parkinson's Disease Detection

Development of Healthcare Model Using AR-DVAE with Mayfly-ML...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Murthy, G. N. Keshava Pareek, Piyush Kumar Manjunath, T.N. Shwetha, N. Deepak, R. Rashmi, P. Siddaganga Institute of Technology Department of Electronics and Instrumentation Engineering Tumakuru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Department of Electronics and Communication Engineering B G Nagara India Inventuriz Labs Private Limited Bengaluru India

ISBN: (纸本)9798350301595

Research on reliable biomarkers that permit the creation of medical-decision support technologies is currently a major focus in healthcare biometrics. These gadgets aid in the analysis and nursing of conditions like Parkinson's disease (PD). If PD patients' vocal alterations could be detected, this would allow for early diagnosis and treatment, thereby preventing the onset of physically disabling symptoms. More recent research has centred on the suggestion of new automatic and objective tools to aid in diagnosis and severity assessment. In this study, we provide a dual approach to detecting PD. The first section extracts features mechanically with the help of an AR-DVAE model (autoregressive deep variational autoencoder). Lastly, the Mayfly Algorithm is used to maximise the bias weight in a modified multilayer perceptron utilising a backpropagation network (MLP-BPN). To conduct this research, we used the publicly available PD Using Digitized Graphics Tablet dataset. In comparison to other models, the suggested model performed better across the board, as shown by the validation analysis. © 2023 IEEE.

关键词： Decision support systems

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Advancements in Multi-Cloud Applications for Enhanced E-Healthcare Services 1

Advancements in Multi-Cloud Applications for Enhanced E-Heal...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Naidu, P Ramesh Gowda, Dankan Sarma, Parismita Arora, Dinesh Suneetha, Sampathirao Patil, Sandip Ramesh Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore India Bms Institute of Technology and Management Department of Electronics & Communication Engineering Karnataka Bangalore India Gauhati University Assam Department of Information Technology Assam Guwahati India Chandigarh Engineering College Department of Electronics and Communication Punjab Mohali India Andhra University Department of Computer Science & Systems Engineering Andhra Pradesh Visakhapatnam India Bharati Vidyapeeth College of Engineering Department of Communication Skill Navi Mumbai India

ISBN: (纸本)9798350330915

E-Health Record Security Research on a Cloud-Based Multi-Layer Framework reaches its climax in a string of noteworthy discoveries, demonstrating how the framework may transform cloud health data security. The framework's multi-tiered design proved to be an effective barrier against a wide range of cyber threats, protecting the privacy and security of patients' medical records. An important factor in the varied healthcare industry is the fact that it can be easily scaled and adjusted to meet the needs of healthcare providers of varied sizes and capabilities. Nevertheless, there are obstacles that need to be addressed, according to the report. These include the difficulty of implementation and the ongoing need for upgrades and modifications to address changing cyber threats and technical advances. Healthcare firms must continuously implement educational programs due to the reliance on user compliance and training. With an eye toward the future, this structure lays the groundwork for more sophisticated studies in the subject. Potential research directions include improving cross-platform compatibility, optimizing resource utilization to reduce performance implications, and integrating AI and ML for automated threat response and predictive analytics. © 2023 IEEE.

关键词： Cloud Computing Data Security Healthcare Optimization Multi-Cloud Applications Patient Data Privacy Scalability

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U-Net based Segmentation and Transfer Learning Based-Classification for Diabetic-Retinopathy Diagnosis

U-Net based Segmentation and Transfer Learning Based-Classif...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： B.D. Parameshachari B M Nalini H M LeenaShruthi Padmavathi Diggi Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

Diabetic retinopathy (DR) diagnostic automation using AI is becoming more common. Diseases affecting the blood vessels in the retina, such as those produced by diabetes, are a primary cause of sightlessness and visual impairment worldwide. As a result, early screening and treatment of DR would benefit substantially from automated DR detection systems, preventing visual loss caused by DR. Over the last several years, many methods for identifying anomalies in retinal pictures have been presented by researchers. Traditional automated approaches for detecting diabetic retinopathy relied on manually extracted features from retinal pictures and a classifier for final classification. Diabetic retinopathy may be detected and classified in fundus pictures with the help of a deep learning approach suggested in this study. In this method, the network makes a prediction depending on the quality of the dataset it was trained on. In the first stage, OD (eye) and BV (blood vessel) segmentation are performed using separate U-Net models. The second stage involves applying transfer learning to the deep learning models in order to fine-tune them for improved performance in both the training and validation phases. We use partial data augmentation methods to evenly expand our training dataset. Compared to the sum of the separate models, the suggested weighted classifier performs the best. Additionally, the model is evaluated using the open-access dataset, which consists of 3662 pictures. The APTOS 2019 dataset served as the basis for the five groups. The suggested technique significantly recovers the presentation of DR detection for fundus pictures as assessed by a number of different metrics and compared to current methods to DR identification.

关键词： Deep learning Training Visualization Retinopathy Transfer learning Visual impairment Blood vessels

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Embedded Based Health Monitoring Secure System on IoT with Artificial Intelligence 2

Embedded Based Health Monitoring Secure System on IoT with A...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Amutha, R. Damodar, Navya Sivasankari, G.G. Kalshetty, Jagadevi. N. Amc Engineering College Department of Ice Bengaluru India Amc Engineering College Ise Department Bengaluru India Amc Engineering College Department of Cse Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Using the individual's RFID tag, this multi-limit patient screen continuously collects the patient's physiological basic indicators and filters them. This theoretical framework, as well as the Internet of Things (IOT), in clinical practice. In this article, we present an Internet of Things (IoT)-based system that monitors four vital signs (ECG, temperature, humidity, and heartbeat) by making use of sensors that are related to one another. The project is doing well thanks to the use of RFID names for the distinct individual information that may be stored on IOT. Information that should be kept private for therapeutic purposes is in grave risk of being disclosed, and several incidents with a similar theme have taken place during the course of the investigation. It is possible for individual clinical security information to be leaked to insurance working conditions, which not only compromises the privacy of individuals but also impedes the steady progress of the medical business. In this paper, in order to address clinical validation, we made use of the RFID development. The proprietor is required to login to the system, and the proprietor will either bring or not bring data from things talk, which is a place where appropriately all data moved by the stuff is compiled into one record and stored in the cloud. because the owner of the information may share it with another customer. After the customer has selected an option and logged into the system, they will be able to download data that has been directly sent by the owner. © 2022 IEEE.

关键词： Internet of things

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm 2

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Archana Reddy, R. Reddy, Chetana Nagarjuna, N. Pradeep Kumar, S. Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Telangana India Oxford College of Engineering Department of Electronics and Communication Bangalore India Cvr College of Engineering Department of Csit Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10-2.5, SNR 10-3.4 and PAPR of.10-4.2, underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems. © 2024 IEEE.

关键词： Landscape of optimization MIMO-OFDM system Non-linear weight factor Peak to Average Power Ratio Whale optimization

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