检索结果-内蒙古大学图书馆

Enhanced Stock Market Trend Prediction on the Indonesia Stock Exchange using Improved Bacterial Foraging Optimization and Elitist Whale Optimization Algorithms 2

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

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

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

ISBN: (纸本)9798331504960

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. © 2024 IEEE.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Unleashing the potential of Natural Language Processing for News Link Article Summarization: Comparing TF-IDF and Text Rank Algorithms

Unleashing the potential of Natural Language Processing for ...

引用

Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Sandhya B R Shusank Chaudhary Basant Pandit Khem Raj Seth Aditya Jha Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Summarizing news links articles has become a crucial technique in the digital age because so much information is produced daily. It can be difficult for readers to stay updated with current events due to the abundance of news sources and the rapid pace of news production. By offering succinct and pertinent summaries, summarization helps control this information overload by facilitating the reader's access to information without requiring a large time commitment. A comparative study of two algorithms TF-IDF and Text Rank has been done in this work. These algorithms are implemented to generate the summary in a concise and precise manner and are evaluated using Rouge Scores such as Rouge 1, Rouge 2, and Rouge L with recall, precision, and f-score. The Experimental results show that compared with TFIDF, the average recall scores of the Text Rank algorithm are improved by 0.6% of Rouge 1, 0.15% of Rouge 2, and 1.9% of Rouge L respectively.

关键词： Machine learning algorithms Text summarization Production Machine learning Information age Natural language processing computer networks computer security

来源：评论

学校读者我要写书评

暂无评论

Connectivity Enhancing-Automated Lip Interpretation Using Deep Learning

Connectivity Enhancing-Automated Lip Interpretation Using De...

引用

Data engineering and Communication Systems (ICDECS), International Conference on

作者： Sn Srujana S Vijaya Shetty Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India

The research paper exploits the advances in deep learning technology to build the convolutional deep learning model to extract text content from videos. We aim to understand the speech in the video clips by analyzing the lip movements of the person. To predict the sentence, videos are segmented into frames, and specifically, the mouth region is isolated and passed on to the convolutional neural networks to capture the important features. The Adam optimizer is used, and the learning rate is regularized. Bidirectional LSTMs are employed as they work extremely well with sequential data like speech. The Connectionist Temporal Loss Function is utilized in the back-propagation of the weights while the model is learning the outputs. Finally, the SoftMax activation is used to predict the sentence. The accuracy is 95% for the prediction during training and 92% for the test data, as the audios are synchronized with videos. The model provides higher robustness even in cases of variability in speech.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scheduling the Tasks in Cloud Computing using Adaptive Adjustment in Whale Optimization Algorithm 2

Scheduling the Tasks in Cloud Computing using Adaptive Adjus...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Nithyashree, G.D. Rajashekhar, S.A. Zanke, Pankaj Navyashree, K.S. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350381665

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm's iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm's exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account. © 2024 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Aquatic Debris Detection System: Image Processing—IOT Integration Perspective 6th

Aquatic Debris Detection System: Image Processing—IOT Integ...

引用

6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Fathima, Kubra Preethi, H.R. Pinki Myali, Rekha Nalini, N. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Bengaluru Karnataka India

ISBN: (纸本)9789811613371

Aquatic debris detection and monitoring is of very essential to ensure safety of aquatic habitats, human health and water transport. In this paper, we introduce the prototype of an aquatic debris detection system equipped with multiple aquatic sensors which are used to measure various parameters of the water body to determine quality of water. Based on this sensing platform, we propose a quick and accurate aquatic debris detection algorithm for remote monitoring which detects debris in the image captured by the camera. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Wandering Strategy-Sand Cat Swarm Optimization-Based Energy Efficient Underwater Wireless Sensor Network 2

Wandering Strategy-Sand Cat Swarm Optimization-Based Energy ...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Habelalmateen, M.I. Sravankumar, B. Keirolona Safana Seles, J. Phadke, Rekha Indupalli, Tabitha The Islamic University Najaf Iraq Vaagdevi College of Engineering Warangal India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Department of Computer Science and Engineering Griet Telangana Hyderabad India

ISBN: (纸本)9798350382952

Underwater Wireless Sensor Network (UWSN) includes a numerous number of sensors positioned in oceans and rivers to observe in underwater environment. Though, underwater sensors are limited for energy and that is tedious for replacing a battery, resulted in energy efficient is main problem. For clustering and routing, in this research proposed a Wandering Strategy - Sand Cat Swarm optimization (WS-SCSO) algorithm which enhanced network lifetime. The WS-SCSO algorithm selects the appropriate Cluster Head (CH) for network and then generates a cluster. Then, by using WS-SCSO algorithm identifies a suitable route for nodes that transmits a data. The proposed algorithm enhances a network lifetime with less energy consumption. The evaluation metrics used to evaluate proposed algorithm are alive nodes, dead nodes, total energy consumption and packet delivery ratio. The proposed algorithm attained less energy consumption of 3% at 200 rounds, 7% at 400 rounds, 18% at 600, 31% at 800 rounds and 55% at 1000 rounds which is effective than previous optimization algorithms like Improved Metaheuristics based Clustering with Multihop Routing protocol (IMCMR-UWSN). © 2024 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Medicinal Plant Identification in Real-Time Using Deep Learning Model

引用

SN computer science 2024年第1期5卷 73页

作者： Kavitha, S. Kumar, T. Satish Naresh, E. Kalmani, Vijay H. Bamane, Kalyan Devappa Pareek, Piyush Kumar Department of MCA Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Information Technology Rajarambapu Institute of Technology Affiliated to Shivaji University Kolhapur Sakharale Maharashtra Islampur India Department of Information Technology D. Y. Patil College of Engineering Akurdi Pune India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

Medicinal plants have a long tradition of being cultivated and harvested in India. The Indian Forest is the principal repository for many useful medicinal herbs. As a result of their critical role in maintaining people's life, medicinal plants have traditionally been the subject of intensive research and consideration. Yet, correctly identifying plants used in medicine is a laborious process that takes a lot of time and expertise. Because of this, a vision-based approach may aid scientists and regular people in the rapid and precise identification of herb plants. Therefore, this research suggests a vision-based smart method to recognize herb plants by creating a deep learning (DL) model. Although there is a wide variety of useful plants, we limit ourselves to just six from the Kaggle database: betel, curry, tulsi, mint, neem, and Indian beech. For each medicinal plant, we collected 500 images. The data undergo a process of resizing and augmentation to increase the sample size. For the fully automatic identification of medicinal leaves, the MobileNet DL model is selected. To determine the model's effectiveness, it must first be trained, then validated, and ultimately tested. The DL model is evaluated using measures including accuracy, precision, and recall. For this reason, the DL model was able to correctly identify medicinal leaves at an accuracy rate of 98.3%. After being thoroughly investigated, the DL model is uploaded to the cloud, and a mobile app is created for the real-time identification of medicinal leaves. To recognize leaf images, the built mobile app accesses the DL model on the cloud. The automated recognition of plants represents an extremely promising option for filling the taxonomic gap and gaining a lot of interest from the fields of botany and machine vision. © 2023, The Author(s).

关键词： Accuracy Augmentation Cloud Deep learning Medicine Mobile application Plant Resize

来源：评论

学校读者我要写书评

暂无评论

Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pareek, Piyush Kumar Ramya, I.M. Jagadeesh, N.B. LeenaShruthi, M.H. 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

ISBN: (纸本)9798350398465

There has been a lot of focus on agricultural image dispensation research in the previous years. computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision. © 2023 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark-Based Grid Environment

IET Blockchain

引用

IET Blockchain 2025年第1期5卷

作者： Kiran, Gangasandra Mahadevaiah Nalini, Narasimaiah Computer Science and Engineering Shridevi Institute of Engineering and Technology Karnataka Tumakuru India Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Grid computing is an emerging technology that enables the heterogeneous collection of data and provision of services to users. Due to the high amount of incoming heterogeneous requests, grid computing needs efficient scheduling to reduce execution time and satisfy service level agreement (SLA) and quality of service (QoS) requirements. For that purpose, we proposed the SprakGrid method to reduce execution time and satisfy SLA, and QoS requirements. The proposed work includes four consecutive phases which are explained as follows, in first we perform user authentication to ensure the legitimacy of the users using the elliptic curve-based chaos theory algorithm which generates a secret key and stores it in the blockchain. In the second we perform query scheduling for resource discovery using the soft actor critic algorithm by considering 3P's parameters which is performed by spark environment that schedules optimal resources based on the service request. Third, we perform a risk assessment and request dropping, in which the risk nodes of workers are evaluated by the master node. To address the resource wastage by attackers, this research dynamically evaluates the risk value using Shannon entropy. Based on the risk assessment the requests are classified into two classes such as normal and malicious. Fourth we perform service live migration, in which the malicious requests are dropped and normal requests are migrated from the source node to the target node using multi-constraints based emperor penguin optimization. Finally, simulation is performed by GridSim and the simulation results demonstrate that the proposed SparkGrid method achieves superior performance compared to other state-of-the-art methods. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics for Crime Prevention in Smart Cities Using Machine Learning

Predictive Analytics for Crime Prevention in Smart Cities Us...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kalpana, Ponugoti Kodati, Sarangam Sreekanth, Nara Ali, Hassan Mohamed Ramachandra, A.C. Avn Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Cvr College of Engineering Department of Information Technology Hyderabad India Bvrit Hyderabad College of Engineering for Women Department of Computer Science and Engineering Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350360660

Crime prevention in smart city infrastructure significantly impacts improving quality of human life. However, the substantial increase in the urban population in recent years affects accuracy, safety, and security. This paper presents a solution using the Hyperplane to improve the Radial Basis Function with Support Vector Machine (SVM) based on Machine Learning techniques to enhance accuracy, safety, and security. The SVM method aims to reduce crime rates through proactive measures based on predictive insights. The RBF kernel handles nonlinear relationships by mapping input features into a higher-dimensional space, allowing the model to capture complex patterns. Initially, data is obtained from the Chicago crime dataset and pre-processed by checking for missing values and performing feature scaling to ensure consistency. Feature selection is conducted using Principal Component Analysis (PCA) to identify relevant features from the data. Crime prediction using RBF with SVM is performed efficiently to enhance accuracy. The proposed RBF-SVM model is evaluated using the Chicago crime dataset, achieving a high accuracy of 0.89, a precision of 0.80, a recall of 0.82, and an F1-score of 0.85. The performance is compared to existing techniques such as Decision Tree (DT) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：