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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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The Impact of Advanced Neural Network Architectures for Extracting Elusive Information 3

The Impact of Advanced Neural Network Architectures for Extr...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kachhoria, Renu Mittal, Love Divakaran, Prakash Singh, Vinay Thangarasu, N. Rajarajeswari, S. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India Usha Martin University Department of Coputing & Information Technology Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India

ISBN: (纸本)9798350319125

Advanced Neural community Architectures, consisting of Convolution Neural Networks (CNNs), have emerged as powerful solutions to demanding situations in extracting complicated and elusive information from large, high-dimensional datasets. CNNs have effective feature extraction abilities that may be used to identify subtle patterns, traits, and relationships in those datasets. but, effective architectures must be tailored to the particular nature of the hassle that allows you to maximize the potential of these fashions. CNNs were used effectively in some of domain names to discover styles in facts that could otherwise remain hidden. As an example, CNNs have been used inside the medical domain to quickly and accurately become aware of cardiologic patterns in electrocardiograms. Similarly, they have been used within the security domain to perceive capability threats earlier than they occur. In each domain names, the capability to extract cognitive facts from numerous datasets speedy and appropriately has saved lives and avoided failures. The exercise of the use of superior neural architectures to permit statistics extraction is complex and requires thoughtful layout. The structure must recall the traits of the project, which include the records kind, the range of dimensions concerned, and the amount of noise or outliers, to determine which strategies are maximum suitable for every specific utility. © 2023 IEEE.

关键词： architectures cognitive Convolution particular strategies

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Natural Language Processing: Bridging the Gap between Human Language and Machine Understanding 2

Natural Language Processing: Bridging the Gap between Human ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business technologies, TQCEBT 2024

作者： Kumar, Keerthipati Syed, Mohammad Haider Bhargava, Saumya Mohakud, Lalit Lalitav Serajuddin, Mohammad Lourens, Melanie S.V. College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Saudi Electronic University Department of Computer Science Saudi Arabia Pgdm Indira School of Business Studies Pune India Jadavpur University Department of Education Kolkata700032 India King Saud University College of Dentistry Saudi Arabia Durban University of Technology South Africa Faculty of Management Sciences South Africa

ISBN: (纸本)9798350384277

NLP in relation to deep learning models is focused on the review of NLP evolution from the middle of the 1950s up to present days. Today, NLP remains the pillar on which rests our digital age, connecting speech nuances of a human language and the computational power of machines. It is tracked along with the historical evolution from rule based systems to the paradigm shift to the machine learning decade since the nineties and constellations of the NLP components. The methodology involves using a conventional linguistic model alongside ultra-modern machine learning models to overcome ambiguity as well as contexts associated with other languages. The technical specifics and continuity loops under the Human-in-the-loop (HITL) framework are discussed. For example, they look at words like "uncommon"words, which have cultural implications in showing how involved human speech is. For instance, the strength of NPL can be depicted by being used in diverse sectors like health and social media. Finally, this article illustrates an advanced time when smart NPL units give topnotch solutions proving that interconnection between linguistics, computing and artificial intelligence is ongoing. © 2024 IEEE.

关键词： Adversarial machine learning

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Digital Signature for Content Authentication 2

Digital Signature for Content Authentication

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

作者： Rai, Ashutosh Kumar Singh, Manmohan Sudheendramouli, H.C. Panwar, Varij Balaji, N. Alangudi Kukreti, Ravish Ies College of Technology Department of Computer Science and Engineering Madhya Pradesh Bhopal India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science Karnataka Mysuru570016 India Graphic Era Deemed to Be University Department of Electronics & Communication Engineering Uttarakhand Dehradun248002 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Graphic Era Deemed to Be University Department of Hospitality Management Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350315905

A digital signature is a mathematical method used to confirm the genuineness and safety of a digitally transmitted message or file. content, such as electronic documents, emails, software, and multimedia files. It is a form of electronic signature that uses encryption and public-key cryptography to sign and verify digital content. In order to generate a digital signature, one must first several steps. First, the sender of the content results in unique hash of the message that has been content utilizing a secure hashing algorithm. The message digest is a sequence of bits that is set in length and specific to the content and cannot be reversed to reveal the original content. Finally, the sender encrypts the message digest using their secret code to create online signature. When The Present Recipient Acquires content and digital signature, they use digital signature decryption using the sender's public key and obtain summary of communications. The recipient then generates a new Briefing Notes of the content in the same way cryptographic hash function sender's role. If the two message digests match, the content is considered authentic and has not been tampered with in transit. Digital signatures provide several benefits for content authentication. They provide non-repudiation, meaning that the sender cannot deny having sent the content since their secret code is used to Construct the electronic signature. They also provide integrity, as any changes to the content will result in a different message digest and therefore an invalid digital signature. Lastly, they provide confidentiality, as only the intended recipient with the corresponding digital signature using a publicly available key pair message digest. Overall, digital signatures are an essential tool for ensuring the authenticity and integrity of digital content and are commonly employed in many fields, finance, healthcare and public administration. © 2023 IEEE.

关键词： Electronic document identification systems

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Bridging Automated Reasoning and Machine Learning for Information Analysis 2

Bridging Automated Reasoning and Machine Learning for Inform...

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2nd IEEE International Conference on Futuristic technologies, INCOFT 2023

作者： Joshi, Sarita Murugan, R. Balaji, N.V. Chandrakala, P. Deshmukh, Gopal B. Mathur, Mala Maharishi School of Business Management Maharishi University of Information Technology Uttar Pradesh India Bangalore Department of Computer Science and Information Technology Karnataka India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350308846

Recent advances in automated reasoning and system studying have appreciably impacted the automation of statistics evaluation. These technologies facilitate understanding the increasingly more complicated volumes of facts in a ramification of approaches. Automatic reasoning helps decide valid solutions based on structured good judgment, while machines can pick out patterns and developments in massive datasets. Bridging those two technologies improves accuracy and efficiency in automated facts evaluation. This paper will overview present-day research on bridging automated reasoning and device-gaining knowledge for such purposes and gift novel approaches to combining the two technologies for more effective data evaluation. Discussion subjects will encompass integrating neural networks and reasoning engines, methods for addressing records incompleteness and uncertainty, and enhancement of reasoning-driven context-conscious systems. Furthermore, the paper will discover the capacity benefits that can be performed via leveraging the strengths of each automated reasoning and gadget gaining knowledge of to optimize answers for a selection of software scenarios. © 2023 IEEE.

关键词： Gaining Optimize Reasoning Selection Uncertainty

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Medical Image Analysis Using Deep Learning Technnique 5

Medical Image Analysis Using Deep Learning Technnique

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5th International Conference on Information management and Machine Intelligence, ICIMMI 2023

作者： Saxena, Surabhi Parveen, Nikhat Agarwal, Devendra Department of Artificial Intelligence and Data Science Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of Computer Science and Engineering Konneru Lakshmaiah Education Foundation Vijayawada Guntur India Goel Institute of Technology and Management Uttar Pradesh Lucknow India

ISBN: (纸本)9798400709418

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. This review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonias, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. This research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. The primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications. © 2023 ACM.

关键词： Deep learning

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IoT-Driven Genomic Ecology: Exploring Genetic Adaptation in Changing Environments using CNN Model 3

IoT-Driven Genomic Ecology: Exploring Genetic Adaptation in ...

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3rd International Conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2024

作者： Ranganathan, Chitra Sabapathy Riazulhameed, Arshadh Ariff Mohamed Abuthahir Sahoo, Santosh Kumar Sumathi, V. Sudhakar, G. Rathinam, Anantha Raman Mphasis Corporation Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada Vaddeswaram India

ISBN: (纸本)9798350369083

The Internet of Things (IoT) technology and Convolutional Neural Networks (CNNs) catalyzes this investigation, which dives into the complex relationship between genomes and the surrounding environment. It monitors various environmental factors in real-time using a distributed network of Internet of Things sensors installed across various natural and urban locations. While doing this, this system collects genomic data from creatures that live in or interact with these habitats. This allows us to explore the intriguing dynamics of genetic adaptation in response to environmental changes. The research sheds light on the genetic factors responsible for an organism's capacity to flourish in constantly changing environments. The system can shed light on patterns of genetic mutations, disease prevalence, and variations in biodiversity by integrating environmental data collected from the Internet of Things with genomic information. These realizations contribute to our comprehension of the mechanisms that drive evolution;help create conservation plans for species at risk of extinction, inform disease epidemiology, and assist sustainable farming practices. The article "IoT-Driven Genomic Ecology"demonstrates a multidisciplinary approach to converging genomics, environmental science, and IoT technology. Its purpose is to shed light on the underlying principles that control life's adaptability to a world that is always changing. © 2024 IEEE.

关键词： Environmental monitoring

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Influence of Human Resource management Practices in Medium Sized Organization on Employee Performance

Influence of Human Resource Management Practices in Medium S...

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2023 International Conference on Disruptive technologies, ICDT 2023

作者： Varun, T. Kudale, Kaniphnath Malhari Ganesh, Kvb Ramkumar, M. Anna University Department of Management Studies Tamil Nadu Chennai India Telangana Warangal India Koneru Lakshmaiah Education Foundation Department of Business Management Andhra Pradesh India Knowledge Institute of Technology Salem Department of Computer Science and Business Systems Tamil Nadu India

ISBN: (纸本)9798350323887

The worker productivity is now widely recognized as a key factor in determining a company long-term viability. The productivity of an organization workers is a major factor in the company success. Employee productivity is given considerable weight in both organizational psychology and human resource management. Globalization, greater investor and consumer expectations, and fiercer product rivalry all contribute to a dynamic economic climate. Public agencies, faced with intense competition and a reducing cost, must constantly seek ways to boost their efficiency, effectiveness, and customer satisfaction. This action is taken to ensure one continued viability in this hostile ecosystem. In order to analyze the data collected here, a method known as quantitative research was employed. © 2023 IEEE.

关键词： Human resource management

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Fuzzy-Deep Learning-Based Artificial Intelligence for Edge Computing and Real-Time Decision-Making in Uncertain IoT Environments 1

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Joshi, B.P. Singh, Akhilesh Kumar, Nagendra Rautela, Siddharth Graphic Era Hill University Department of Mathematics Department of Computer Science & Engineering India VMSB UTU Department of Electrical Engineering Campus Pithoragarh India GL Bajaj Institute of Technology & Management Department of Electrical & Electronics Engineering Greater Noida201306 India Central Board of Secondary Education Department of Mathematics Uttarakhand Bhimtal India

ISBN: (纸本)9798331518578

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff. © 2025 IEEE.

关键词： Energy utilization

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A Machine Learning Approach for Enhanced Security Using Blockchain in Finance Auditing Services 15

A Machine Learning Approach for Enhanced Security Using Bloc...

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

作者： Sheerin Banu, M. Parveen, Nikhat Gayathri, R. Gera, Anju Kumar, Sunil Anitha, G. R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Rajalakshmi Engineering College Department of ECE Tamilnadu Chennai India G.L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Greater Noida India SGT University Faculty of Commerce and Management Haryana Gurugrm India R.M.D. Engineering College Department of ECE Tamilnadu India

ISBN: (纸本)9798350370249

In financial auditing services, which is constantly evolving, it is of the utmost importance to establish stringent security protocols. Since the financial sector is increasingly being targeted by sophisticated cyber-attacks, solutions are required to protect sensitive information and prevent the integrity of financial audits from being compromised. Conventional security measures, in many instances, do not devote sufficient attention to addressing the known vulnerabilities that exist in the auditing systems that are currently in use. This research proposes a novel approach to enhance security in financial auditing services by integrating Support Vector Machines (SVM) with Blockchain technology. By combining Blockchain technology with Machine Learning (ML), and more specifically SVM, this research suggests a novel approach to enhancing the level of security that can be achieved. Taking advantage of the inherent capability of SVMs to correctly categorise and predict potential security concerns, the research makes use of these models for the purposes of machine learning. Utilising Blockchain technology allows for the generation of a record that can be audited since it is reliable and verifiable. By combining SVM with Blockchain, it is possible to quickly identify and resolve any security breaches that may have occurred. The findings indicate that the level of protection provided by financial auditing services has significantly increased in recent years. Considering that the SVM with Blockchain architecture can recognise and classify a wide variety of threats, it has the potential to be utilised in the process of making defences more proactive and robust. © 2024 IEEE.

关键词： Support vector machines

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