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A Machine Learning-Based Forecasting Model with Deep Learning Capabilities for Unbalanced Time Series Data 2

A Machine Learning-Based Forecasting Model with Deep Learnin...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Amudha, A. Suri, Sahil Sindhu, M. Goyal, Shubhi Kukreja, Mansi Pathak, Kishor R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350388916

During the last several years, deep learning capabilities have surpassed those of more conventional models on a wide variety of machine learning-related tasks. Neural networks with deep layers have indeed been effectively used to time series forecasting issues, which are crucial to the field of data mining. Their ability to instantly understand the temporal connections contained in time series has made them a viable option. Yet, deciding on a deep neural network architecture and then optimizing its parameters calls for a high level of skill. Thus, it is essential to conduct comprehensive studies of the predictive capacities of all current architectural frameworks. The completion of this work is hampered by two primary factors: (1) a complete examination of the most recent research on depth learning's application to time series forecasting, and (2) an empirical inquiry analysing the effectiveness of the most commonly used architectures. Seven distinct deep learning models are evaluated and compared for their efficiency and accuracy. We present scores and distributions based on our analysis of the suggested models trained with various architectures and hyperparameter settings. The 50,000 time series in the databases are used to solve 12 unique forecasting issues. Using this data to train approximately 38,000 models, we present the largest deep learning research to date for forecasting time series. While LSTMs were shown to be the most significantly predictive, both LSTMs & CNNs showed promise in this research. Comparable performance is attained by CNNs, but with less variance in results as well as a more based on the best available overall. © 2024 IEEE.

关键词： Prediction models

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Effectual LDA Approach for Optimal Topic Modelling from Social Network Data 2

Effectual LDA Approach for Optimal Topic Modelling from Soci...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Dhabliya, Dharmesh Singh, K. Ranjith Singh, Neelesh Revathi, K. Goyal, Shobhit Shalini Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798350388916

Methods like document clustering & topic modelling in OSNs allow for the classification, annotation, and analysis of massive volumes of user-generated content. Using these methods, you may finally comprehend the material. Among the various approaches that have been developed throughout time are text mining and clustering techniques. Other techniques, such as hidden subject models & neural embedding approaches, have also been developed. Prior Inference for the Use of Latent Dirichlet Allocation is included as a comparison to our other topic models. Given the many accessible methods of assessment, we review the research to determine which extrinsic measures will provide the best results and then provide guidance on how to use them. Furthermore, we demonstrate the efficacy of the methods on data sets consisting of documents of varied lengths. Our study found that, when using appropriate extrinsic evaluation measures, the best results were obtained across all data sets by using clustering techniques to neural embedding versions of features. Using tf-idf weights & embedding distance measurements, we also provide a top-words based approach to make sense of the clusters. This strategy is predicated on the notion that a sentence's most important words are also the ones that occur most often in it. © 2024 IEEE.

关键词： Embeddings

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Efficient Resource management in IoTs Using Evolutionary and Swarm Intelligence Algorithms

Efficient Resource Management in IoTs Using Evolutionary and...

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Innovative Approaches in Smart technologies (ISAS), International Symposium on

作者： Keshav Dev Gupta Korhan Cengiz Charu Awasthi Siva Akash Ramini Shyamapriya Chatterjee M Pavithra Department of Computer Science Engineering Poornima College of Engineering jaipur India College of Information Technology University of Fujairah UAE Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Department of information technology JSS academy of Technical Education Noida India Department of School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh Department of Information Technology Narula Institute of Technology Kolkata West Bengal Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems.

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Utilization of Big Data Analytics for Risk management 4

Utilization of Big Data Analytics for Risk Management

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Santhikumar, R. Kartillkayani, K. Mishra, Manoj Kumar Thota, Sridhar Beschi, I.S. Mishra, B.P. Aditya Institute of Technology and Management India Srm Institute of Science and Technology Computer Science and Engineering India College of Business and Economics Salale University Fitche Ethiopia College of Engineering Design Alliance University Department of Ece Alliance India India Jss Academy of Technical Education India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Big data (BD) is a form of data, which contains a huge variety and grows exponentially with time. Additionally, risk management also becomes an important part of an organisation so that they can identify their future potential risks and develop new strategies for maximising organisational success. This paper has aimed to analyse different ways of using BD analytics regarding risk management of organisations. In order to achieve research aim, the paper has conducted literature review, where it has discussed different forms of big data and different organisations that use big data regarding risk management. It has only used topic-related existing resources to get a clear insight into this topic. Additionally, it has considered a secondary research method, where it has collected secondary data from currently available journals and articles so that it could understand perspectives of other authors on this topic. Based on collected information, it has conducted a thematic analysis. Findings of this research paper indicated that utilisation of big data could help businesses to identify fraud, financial risk and potential risk factors regarding their business operations. Moreover, several large organisations are significantly utilising BD for their business works and they are using different big data technologies including, AI, and R-programming. Hence, it has been concluded that in this digital era, when there are increased issues of fraud and cyber-threats, Big data helps in identifying risks and opportunities and making fact-based decisions. Additionally, all these technologies are offering extensive positive results to companies in terms of risk management. © 2022 IEEE.

关键词： Risk management

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Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method

Predicting House Price Model : A Comprehensive Analysis with...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Mansi Sharma Deepak Sharma Rushikesh Burle Pawan Patil Isha Joge Chetan Puri Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research Wardha India Department of Commerce & Management Sciences Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India

This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the *** dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset.

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Blockchain Enabled Privacy- Preserved Secure e-voting System for Smart Cities

Blockchain Enabled Privacy- Preserved Secure e-voting System...

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science, Technology, Engineering and management (ICSTEM), International Conference on

作者： P. Chinnasamy Ramesh Kumar Ayyasamy Poovendran Alagarsundaram S. Dhanasekaran B.Santhosh Kumar Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Humetis Technologies Inc Kingston NJ USA Department of Information Technology Kalasalingam Academy of Research and Education (Deemed to be University) Srivilliputtur Tamilnadu India Department of Computer Science & Engineering Guru Nanak Institute of Technology Ibrahimpatnam Telangana

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In today's world, voting online is becoming increasingly popular. It has a lot of power to reduce administrative costs and increase the number of voters. Eliminates the need for ballot papers or polling stations, allowing citizens to vote anywhere via the Internet. Apart from these benefits, online voting methods are viewed with extreme caution as they pose additional risks. One mistake can easily lead to disaster. When used in elections, electronic voting systems must be legitimate, accurate, secure, and competent. However, the ability to have difficulty with computer voting methods can reduce acceptance. Blockchain technology is created to address these concerns and provides separate nodes for electronic voting. It is used to create electronic voting systems because of its ultimate verification benefits. With distributed, non-disposable features, and security protection, this technology is a great way to establish common electronic voting solutions. Blockchain is a system where each item is treated as a block, with a link that connects all these blocks, hence the name Block-Chain. Each block contains all possible data for one business, as well as a timestamp and, in exceptional cases, once. The hashing function is used to determine the hash value of all data in a block. All data blocks are accelerated with the same function. The field across all blockchain blocks contains the precedent block address. Improving security Face recognition is used. Face recognition helps to ensure that the voter is legal or not. It helps election planners identify fraudulent voters and removes them from participating in the voting process. Voting with E using the blockchain facilitates the proper distribution of votes and ensures the security of total data throughout the process. It makes the election clean and protects the vote of the affected people.

关键词： Electronic voting systems Costs Smart cities Law Face recognition Disasters Blockchains

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Machine Learning based Lung Cancer Detection & Analysis

Machine Learning based Lung Cancer Detection & Analysis

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Indumathi, A. Sathanapriya, M. Vinodh, N. Ashok, Merugu Aishwarya, N. Kongunadu Arts and Science College Department of Computer Technology Coimbatore India Karpagam Academy of Higher Education Department of Commerce Coimbatore India S.A.Engineering College Department of Management Studies Thiruverkadu India Cvr College of Engineering Department of Ece Telanganaa India Vinayaka Mission's Research Foundation Deemed to Abe University Vinayaka Mission's Kirupananda Variyar Engineering College Salem India

ISBN: (纸本)9798350333602

The key to treat cancer is early detection. This study has reviewed the fractal image analysis technique for cancer cell detection. Typical abnormalities in cancer cells include uncontrolled cell proliferation. Measurement of morphological complexity and study of figures with atypical shapes are both possible with fractal analysis. Investigations were conducted using simulations of human breast cancer cells. We investigated and compared changes in the fractal dimension between cancer cells and normal cells. The preliminary results demonstrate that the picture based fractal analysis technique is able to locate breast cancer cells. It has a great deal of potential to shed light on the morphological classification of tumor growth and could be used as a marker for early cancer identification and the effectiveness of cancer treatments. The segmentation and data enhancement categorization scheme has been completed. e and The accuracy of lung cancer detection is greater. Cancer can spread to other organs and impair their normal activities, making it a fatal condition. The cancer grows more deadly as it advances in stage. The doctor will do a number of tests to ascertain the degree and seriousness of the disease, and based on the results, the stage of cancer will be determined. Before giving a chance to develop and spread, certain cancers can be detected early. Early cancer discovery results in significantly better treatment outcomes and less physical, emotional, and financial suffering. © 2023 IEEE.

关键词： Cancer cells

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mohammad Ahmar Khan Pushpendra Kumar Verma Shivangi Ghaldiyal N. V. Balaji G.Arun Francis Swati Bula Patil Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman School of Computer Science and Applications IIMT University Meerut Department of Computer Applications Invertis University Bareilly Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India IT Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab’s gift to the. We provide the whole text of the news that have already been applied based on human evaluation of the articles’ authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study.

关键词： Deep learning Accuracy Text categorization Training data Big Data Natural language processing Reliability Labeling Fake news

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Constructing an Evaluation Framework for English Teaching in Higher education: Integrating Neural Networks and Natural Language Processing 3rd

Constructing an Evaluation Framework for English Teaching in...

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3rd International Conference on Mechanical and Energy technologies, ICMET 2023

作者： Deshmukh, Ram Ramachandran, K. Anute, Nilesh Rajeswari, T.S. Chouhan, Kuldeep Fatma, Gulnaz School of Engineering SR University Telangana Warangal India Postdoctoral Researcher Jesselton University College Jalan Lintas Sabah Kota Kinabalu88300 Malaysia ASM’S Institute of Business Management and Research Pune India Department of English Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida201310 India Department of English Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789819727155

In the dynamic landscape of higher education, the effectiveness of English teaching is paramount, as it directly impacts students’ communication skills, academic success, and future career prospects. To enhance the quality of English instruction in higher education, this study presents an innovative evaluation model that harnesses the power of neural networks and natural language processing (NLP). The model's potential for customization and scalability makes it a valuable tool for higher education institutions seeking to optimize their English teaching programs. Furthermore, it contributes to the broader exploration of how AI and NLP technologies can transform the field of education, fostering more efficient and personalized learning experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

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On Multi-objective Fuzzy Shortest Path Problem

On Multi-objective Fuzzy Shortest Path Problem

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International Conference on Advances in Data science and Computing technologies, ASDC 2022

作者： Halder, Swapna Majumder, Saibal Biswas, Arindam Mandal, Bijoy Kumar Peng, Sheng-Lung Centre for IOT and AI Integration with Education–Industry–Agriculture Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology West Bengal Kolkata India Dr. B.C. Roy Engineering College West Bengal Durgapur India School of Mines and Metallurgy Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering NSHM Knowledge Campus West Bengal Durgapur India Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan32462 Taiwan

ISBN: (纸本)9789819936557

Being a network optimization problem, the shortest path problem (SPP) is considered as one of the popular and frequently encountered optimization problems to address real-world problems. In this paper, we investigate a multi-objective SPP under the paradigm of fuzzy set theory. The proposed problem minimizes both cost and time of a given network, where every edges are associated with cost and time parameters represented as fuzzy triangular numbers to incorporate uncertainty involved with real-world phenomenon. Here, we have modeled a fuzzy chance-constrained model of multi-objective SPP and eventually solved the deterministic transformation of the model using weighted sum method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy set theory

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