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

作者： Ambika, R. Lahane, Prashant Kumar, G. Rabbi, Fazle Maarif, Muhammad Anas Qureshi, Mohmad Kashif Bms Institute of Technology and Management Department of Ece India Mit World Peace University Faculty of Engineering India Srm Institute of Science and Technology Department of Management India Australian Computer Society Australia Institut Pesantren Kh. Abdul Chalim Pacet Mojokerto Indonesia Sanskriti University Computer Science & Engineering Professor Cum Data Scientist India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Intelligent control system is a control technique that utilises different computing approaches such as fuzzy logic, Bayesian probability, neural networks and machine learning. Information processing is change of information within a manner detectable through an observer. It describes everything that happens in universe from falling of rocks to printing text files from a digital computer system. This paper aimed to analyse the impact of intelligent control systems (ICS) to assist humans with complex information systems. In order to achieve this aim, the paper has conducted a literature review to understand other authors' perspectives. It has discussed concept of ICS and its role in assisting information systems. Additionally, the paper has analysed information processing and other information about it. In order to collect relevant data on selected research topic, paper has conducted a secondary data collection method. It has selected data from secondary resources and analysed data with help of thematic analysis. This data analysis method helped to understand data patterns on selected research topic. Based on data analysis, results found that key methods in intelligence control are fuzzy control, neural network control, and expert control and these controls offer useful and relevant programming regarding complex information systems. Additionally, intelligent control can adapt, learn and plan under a huge uncertainty as well as cope with a huge amount of information. Finally, based on robust discussion, paper has formulated an appropriate conclusion for this research. © 2022 IEEE.

关键词： Fuzzy control

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Utilizing Recurrent Neural Networks for Sentiment Analysis of Financial News to Enhance Market Prediction and Investment Strategies

Utilizing Recurrent Neural Networks for Sentiment Analysis o...

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Automation and Computation (AUTOCOM), International Conference on

作者： Ramu Pandiri K. Thilagavathi Aman Mittal Muntader M. Almusawi P. Nagaveni T. Rajesh Kumar Senior Budget Consultant Infomatics Corp Dallas Texas Associate professor Department of Commerce Saveetha school of law(SIMATS) Chennai India Lovely Professional University Phagwara Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this study, an attempt was made to analyze the prospect of Recurrent Neural Networks (RNNs) in carrying out sentiment analysis of financial news in order to improve market prediction and advancement in investment plans. Thus, employing developed NLP methods and neural networks, the study purpose is to develop an automatic solution for identifying the sentiment of financial news articles. The approach that is going to be used includes the data acquisition of great volume of financial news, the data preprocessing, the model training using BiLSTM with BERT embeddings for the sentiment analysis and the combination of the sentiment scores with the other standard financial figures in order to create the novel hybrid market predictor model. The comprehensive analysis shows a high increase in the accuracy of sentiment classification and market prediction and a higher efficiency of the hybrid model over traditional ones. Feedback from the financial analysts substantiates the applicability of the presented system describing it as convenient, precise, and compatible with other systems. In regards to the concerns of the study, hypotheses preoccupy the question of data quality as well as the interpretability of models, focusing on ethical concerns. Finally, the study proves that sentiment analysis based on RNN can give a significant contribution to the illumination of financial activities and help to enhance the effectiveness of the financial investment decision-making process.

关键词： Sentiment analysis Analytical models Solid modeling Accuracy Bidirectional long short term memory Predictive models Data models Stock markets Standards Investment

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Cooperative self-scheduling routing protocol based IOT communication for improving life time duty cycled energy efficient protocol in SDN controlled embedded network

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Measurement: Sensors 2022年 24卷

作者： Dhandapani, Anbarasu P, Venkateswari T, Sivakumar C, Ramesh P, Vanitha Department of Electronics Rathinavel Subramaniam College of Arts and Science Sulur Coimbatore India HOD & Associate Professor Department of Electronics Rathinavel Subramaniam College of Arts and Science Sulur Coimbatore India Principal Rathinavel Subramaniam College of Arts and Science Sulur Coimbatore India Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Associate Professor Department of Management Studies M.Kumarasamy College of Engineering Karur India

Internet of Things (IoT) is a developing interconnecting medium in a wireless network through internet of communication in network devices like sensors, smart devices and controllers. This interconnect the devices via the internet to make communication. Due to large sectors IoT devices and Software-Defined Networking (SDN) controllers contains energy constraints in improper routing strategies. Specifically in health care industries, the patient monitoring data's consumes higher energy to reduce the lifetime of network communication leads data losses during the IoT devices collecting the information become *** embedded communication are relatively controls through the Software defined network to maintain the energy consumption to improve the lifetime of energy efficient network. But leastpropagation energy losses arise communication losses because of traffic, packet losses, latency beyond with throughput limits. The main problem is Passive Infrared Sensor (PHR) sensor nodes and Relay nodes attain more energy because of non-uniform node displacement in routing in uneven energy drainage. So resolving these problems, in this system propose a cooperative self-scheduling routing protocol based IoT communication for improving energy efficient SDN controlled embedded *** this construct the Neighbor Distance Discovery Protocol (NDDP) to identify the minimum depletion path by identifying Sensor closest route to the relay node. Then construct the Multipath Cooperative Self-Scheduling Protocol (MCSSP) to formalize the non-traffic route. By estimating the energy consumption on each sensor node on routing medium, this considers the prior routing communication rate by calculating average transmission delay, sensor absorption rate, power consumption, node response rate, and packet loss rate. UsingLife Time Duty Cycled Energy Efficient Protocol (LTDCEEP) to calculate the delimit of the feature limits along the network threshold latency energy limits. Based on the

关键词： Internet of things

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Problems and Optimal Methodologies Agile Project Management Solution in Publicly accessible Sector: A Comprehensive Evaluation of the Literature

Problems and Optimal Methodologies Agile Project Management ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sakshi Takkar Amanpal Singh Rayat Vikrant Sharma Tiyas Sarkar Kapil Jairath Amanpreet Singh School of Computer Science & Engineering Lovely Professional University Phagwara Punjab India School of Computer Science & Engineering Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Sciences Trinity College Jalandhar Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Agile technique use in the public domain is not new. Governments all throughout the globe have been adopting Agile development in recent years, particularly in light of the Pandemic, which calls for swift decision-making. Agile implementation is still challenged, meantime, by the distinctions between the public and private sectors, such as the adherence to the hierarchy of authority concept. The purpose of this work is to investigate the difficulties and provide remedies for implementing Agile project oversight in the public domain by use of a PRISMA-based systematic literature review (SLR). Four papers databases-Scopus, ACM, IEEE Explore, & Emerald Research Insight-provided the material employed in the SLR. An aggregate of 43 difficult topics were identified from the analysis of 18 appropriate publications out of 595 that were found. The answer for every problem is found using the mapping findings from the SLR articles with PMBOK 7th version Guidebook. Each of these problems is grouped according to 8 project accomplishment areas of PMBOK 7th edition. Results revealed that, with eight shortcomings apiece, the Development Approach, Lifecycle, & project-related category segments had the greatest problems. Next the team with seven problems, the stakeholder with six, the delivery with five, the measurement with four, the scheduling with three, and uncertainty with two. Academics and practitioners might find this study helpful as a reference for tackling the difficulties of introducing agile project administration in the public sphere.

关键词： Uncertainty Pandemics Measurement uncertainty Government Decision making Scheduling Teamwork Stakeholders Informatics Systematic literature review

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Efficacy of rain barrels and rain gardens to reduce urban pluvial flooding in densely built-up residential areas: a case study on Miyahara-Cho in Saitama City, Japan

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City and Built Environment 2023年第1期1卷 1-17页

作者： Lin, Jiajing Uchiyama, Shun Bhattacharya, Yasmin Nakamura, Hitoshi Master student Graduate School of Engineering and Science Shibaura Institute of Technology Saitama City Japan Associate Professor Department of Planning Architecture and Environmental Systems Shibaura Institute of Technology Saitama City Japan Professor Department of Planning Architecture and Environmental Systems Shibaura Institute of Technology Saitama City Japan

In recent years, flooding in densely built-up residential areas has tended to increase because of the increased frequency of heavy rain and climate change. Urban pluvial flooding mitigation is thus needed by reducing the runoff of sewer systems using green infrastructure solutions. In this regard, rain barrels (RBs) and rain gardens (RGs) are considered effective stormwater management methods. This study aimed to verify the effectiveness of RBs and RGs and their combined use in reducing urban pluvial flooding in densely built-up residential areas. This research undertook a case study in Miyahara-Cho, Saitama City, Japan. It involved the analysis of the total runoff of sewer systems and the flood condition in the sewer to verify the rainwater runoff control effect of RBs and RGs through simulations using the US Environmental Protection Agency’s stormwater management model. This study simulates two main types of rainfall events: short duration and long duration. The results of the short duration rainfall showed that the combination of RBs and RGs is highly effective and can reduce the impact of excess runoff. In the case of the long duration rainfall, the volume and speed of stormwater runoff of the first peak rainfall were reduced.

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Shielding NLP Systems: An In-depth Survey on Advanced AI Techniques for Adversarial Attack Detection in Cyber Security

Shielding NLP Systems: An In-depth Survey on Advanced AI Tec...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： T. Sai Lalith Prasad K. B. Manikandan J. Vinoj Research Scholar Department of Computer Science and Engineering Vignan's Foundation for Science and Technology & Research (Deemed to be University) Guntur Andhra Pradesh India Assistant Professor Department of Artificial Intelligence and Data Science Vignan Institute of Technology and Science Telangana India Assistant Professor Department of CSE Vignan's Foundation for Science Technology and Research (Deemed to be University) Guntur Andhra Pradesh India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research investigates the challenges posed by adversarial perturbations to the robustness and reliability of Natural Language Processing (NLP) models. The study focuses on three key paradigms for enhancing the resilience of NLP systems: meta-learning, reinforcement learning, and self-supervised learning. A comprehensive evaluation of these strategies is conducted, analyzing their strengths and weaknesses in mitigating adversarial attacks. The study provides insights into the most promising approaches for enhancing the robustness of NLP models and offers directions for future research in this critical area. By advancing the resilience of NLP systems, this research contributes to the development of more robust and trustworthy AI applications across various domains, including healthcare, finance, and security.

关键词： Training Surveys Renewable energy sources Scalability Self-supervised learning Natural language processing Robustness Security Artificial intelligence Resilience

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Next-Gen Stroke Detection: Neuroimage Analysis With Machine Learning

Next-Gen Stroke Detection: Neuroimage Analysis With Machine ...

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Data science and Business Systems (ICDSBS), International Conference on

作者： Ms. Chengamma Chitteti Pamisetty Navyasree Dr. M. Dharani Vuluvapalle Sainath Reddy Medepalle Sai Vijay Ganesh Challa Bharath Assistant Professor Department of Data Science Mohan Babu University (Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh India UG Scholar Dept. of CSSE Sree Vidyanikethan Engineering College Tirupati A.P Associate Professor Dept of ECE Mohan Babu University (Sree Vidyanikethan Engineering College) Tirupati A.P

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Exact and provoke stroke recognition is urgent for compelling treatment and working on tolerant results. In any case, customary demonstrative procedures frequently face difficulties in giving high precision and proficiency. This study presents an AI based technique for diagnosing strokes utilizing cerebrum X-ray *** approach leverages deep learning models, specifically MobileNet and ResNet, to classify images into two categories: Normal and Stroke. MobileNet was chosen for its lightweight structure, making it suitable for real-time use in settings with limited resources, while ResNet was selected for its powerful feature extraction, enhancing diagnostic accuracy. The models were trained on a diverse set of brain MRI images, and preprocessing methods were applied to optimize the quality of the data and improve the models’ performance. The results showed that MobileNet achieved an accuracy of 92%, and ResNet reached 94%, both outperforming the baseline CNN model, which had an accuracy of 85%. ResNet also showed superior precision, recall, and F1-score, making it ideal for applications requiring high accuracy. Meanwhile, MobileNet's balance of efficiency and accuracy makes it a suitable option for environments with limited computational resources. This exploration features the capability of profound learning in stroke location, giving medical care experts a solid device for early conclusion and opportune therapy. Future work will involve testing these models on larger datasets and conducting clinical trials to validate their effectiveness in real-world settings.

关键词： Deep learning Accuracy Computational modeling Magnetic resonance imaging Stroke (medical condition) Brain modeling Feature extraction Data models Real-time systems Residual neural networks

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Multiple Redundant K-Means Clustered Scheme Based on Weighted Cluster Head Selection for Damaged S-UAV

Multiple Redundant K-Means Clustered Scheme Based on Weighte...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Cyprus and Department of Electrical & Electronic Engineering Science University of Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATLAB. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.

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Enhanced Redundant Weighted Clustered Scheme for Damaged S-UAV

Enhanced Redundant Weighted Clustered Scheme for Damaged S-U...

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IEEE International Conference on Communications (ICC)

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia computer science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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