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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm 1

A Novel Hybrid Approach for Threat Detection in Cyber Securi...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Gupta, Abhishek Kumar Dixit, Neetu Kumar, Sujeet Rawat, Priyanka Madhumita Jagannath International Management School Department of Information Technology New Delhi110070 India RBMI Department of Computer Application Greater Noida201306 India GL Bajaj Institute of Management Greater Noida201306 India Lingaya's Vidyapeeth Department of Computer Science & Engineering Faridabad121002 India

ISBN: (纸本)9798350353648

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks. © 2024 IEEE.

关键词： Cyber attacks

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Utilizing the Fem Approach, PSO, and Firefly Algorithm, Surge Wire Grades Ring Modification was Achieved 11

Utilizing the Fem Approach, PSO, and Firefly Algorithm, Surg...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaur, Amandeep Singh, Harveer Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

As well as an arrester, semi power and electromagnetic current distributions have been seen, which impacts the dependability and lifespan of the device. With the HV load resistor, a rating ring is used to provide an even spread of the electric field. The Finite Element Technique (FEM), a tried-and-true approach, is used to assess the effects of sorting ring characteristics. It contributes to extending the useful life of surge arrester ZnO blocks under extreme stress. Optimizer (PSO) and BAT techniques are used for validation. © 2022 IEEE.

关键词： Optimization

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Comparative Analysis of CNN Architectures for Maize Crop Disease 10

Comparative Analysis of CNN Architectures for Maize Crop Dis...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Agrawal, Rakshit Singh, Vinayak Gourisaria, Mahendra Kumar Sharma, Ashish Das, Himansu School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India GLA University Department of Computer Engineering & Application UP Mathura281406 India

ISBN: (纸本)9781665467414

In the current scenario, the Agriculture industry is regarded as the leading industry for society, serving all the needs for the betterment of humanity. Plants are considered to be one of the primary sources of humanity's energy production concealed with nutrients, medicinal cures, etc. Any harm or disease due to exposure of pathogens, viruses, bacteria, etc. to the plants during agriculture leads to the downfall of productivity making it a crucial concern to prevent such diseases and take necessary steps to avoid them. Making accurate identification of such fatal diseases is an important step for the industry as well as for the farmers. In our study, we have implemented fifteen different Convolutional Neural Networks (CNN) which takes plant leaf image as an input source. These architectures have different layers, neurons per layer, optimizers, etc. Our goal is to provide a detailed comparative analysis between the various frameworks based on accuracy, precision, Least Validation Cross-Entropy Loss (LVCEL), etc. parameters in the most effective way. From the experimental results, we found the sixth architecture to be the most accurate model. With the modification of convolutional layers and the use of the correct optimizer, results can be improved to a great extends. © 2022 IEEE.

关键词： Productivity Humanities Pathogens Plants (biology) Neurons Crops Market research

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Using Classifier with Gated Recurrent Unit-Sigmoid Perceptron, Order to Get the Right Bird Species Detection 11

Using Classifier with Gated Recurrent Unit-Sigmoid Perceptro...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Sikand, Rainy Singh, Harveer Chandigarh School of Business Department of Master of Computer Application Jhanieri India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

The method for classifying a picture using the CNN algorithm and various activation functions is the subject of this research study. From a human perspective, there are several sorts of situations found in birds, such as various sizes, altitudes, colours, and forms. There is a more substantial variety in photos to recognize the species of bird than there is in the accepted audio categorization of birds. Additionally, people have a really tough time identifying birds from photos. The system is suggested to support the concepts of identifying a portion of the birds and extracting the CNN characteristics using several convolutional layers in order to discriminate and categorise the birds. These characteristics are then gathered and presented to the decoder at that time for classification. Various bird photos and activation functions are used to test the CNN method. The nodes of the picture are checked to the different testing data sets, such as cifar-10 and Berkeley Birds 200, with order to determine their correctness. Python is used to create the suggested system in either Omnibus 3 or Dragonfly 3 programs. According to the experiment results and analyses, CNN with activated Re LU-Sigmoid outperforms CNN with the other activation functions in terms of accuracy. © 2022 IEEE.

关键词： Chemical activation

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Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced Visual Understanding 3

Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Bhatt, Chandradeep Rai, Sumit Chauhan, Rahul Dua, Deepika Kumar, Mukesh Sharma, Sanjay Graphic Era Hill University CSE Department Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Uttranchal University Department of School of Computing Dehradun India Computer Application Department SGRRITS Dehradun India

ISBN: (纸本)9798350303360

Photo caption generators had gained huge recognition recently from the areas of NLP and computer vision. These generators produce captions that explain an image's content using deep learning algorithms. A sizable collection of image-caption pairs is used to train the algorithm. The photos are fed into a pre-trained CNN after the dataset has undergone preprocessing to extract its visual properties. Along with the picture features, the necessary captions are tokenized and supplied to the LSTM decoder. In order to accelerate the caption generation process, the LSTM decoder is trained to utilize beam search and maximum likelihood estimation methods. To assess the effectiveness of the suggested model, several metrics such as BLEU and METEOR are employed. The suggested CNN-LSTM image caption generator has a lot of promise for use in a variety of contexts, such as picture comprehension, information retrieval, and assistive technology for the blind. Combining CNN and LSTM models takes advantage of each architecture's capabilities, allowing the model to produce insightful and contextually appropriate captions for a variety of images. We also explore present difficulties and potential directions for additional investigation in this area. This research study concludes by presenting an innovative method for creating image captions that combines the strength of CNN and LSTM. The experimental outcomes demonstrate the efficacy and reliability of the suggested model, highlighting its potential to advance the field of image captioning and contribute to the creation of intelligent systems that can comprehend visual content and produce precise and insightful textual descriptions. © 2023 IEEE.

关键词： Intelligent systems

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A hybrid technique for selection and minimization of test cases in regression testing

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Recent Advances in computer Science and Communications 2021年第2期14卷 477-488页

作者： Singh, Leena Singh, Shailendra N. Dawra, Sudhir Department of Computer Science and Engineering Amity University Noida India Department of Computer Application Tecnia Institute of Advanced Studies New Delhi India

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, re-testing of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of cov-erage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Co

关键词： Testing

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Impact of Glucocorticoid Toxicity Index on Immunosuppressive Management of Inflammatory Bowel Disease 5th

Impact of Glucocorticoid Toxicity Index on Immunosuppressive...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Yadav, Dhyan Chandra Sharma, Sunil Agarwal, Chhaya Mehta, Deepak Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Glucocorticoid toxicity index (GTI) is a useful degree proposed by using a meta-evaluation to assess the chance of systemic glucocorticoid exposure in sufferers laid low with inflammatory bowels illnesses (IBD). The GTI is computed based totally on cumulative glucocorticoid cumulative dose and duration within the first yr. After a prognosis of IBD. The contemporary take a look at objectives to check the literature to decide the utility of GTI in predicting the danger of glucocorticoid toxicity in IBD patients. A literature search turned into conducted to pick out research that evaluated the predictive price of GTI for toxicity in IBD patients. The retrieved articles suggest that cumulative glucocorticoid dose and length prior to three hundred and sixty five days of the diagnosis of IBD have been massive factors related to the prevalence of glucocorticoid toxicity in IBD sufferers. However, different next elements may additionally have an effect on the toxicity of glucocorticoid exposure in IBD patients, along with age, comorbidities, and genetic polymorphisms. Moreover, some of the research, the outcomes validated that GTI can be a valuable device for immunosuppressive management in IBD sufferers to be able to discover patients who're much more likely to revel in systemic toxicity at some stage in immunosuppressive treatment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cumulative Exposure Glucocorticoid

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Advancements in User Interfaces and Immersive Technologies in Virtual Reality

Advancements in User Interfaces and Immersive Technologies i...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Banerjee, Debarati Sharma, Jatin Kaur, Amandeep Sharma, Vandana Tripathy, Hrudaya Kumar Alkhayyat, Ahmed Deemed to Be University Kalinga Institute of Industrial Technology Bhubaneswar India Baba Farid College of Engineering and Technology Department of Computer Application India CHRIST University Computer Science Department Bengaluru India Deemed to Be University Kalinga Institute of Industrial Technology Odisha Bhuabneswar India The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350390049

Virtual reality and immersive environments are key research areas with wide applications in scientific and educational fields. These technologies require new approaches for interactive features, demanding innovative narratives and user relationships. As ICT advances, it is crucial to develop new methods for information representation and user interaction within immersive environments. Virtual reality and related technologies on the virtuality continuum are emerging as powerful media for constructing and representing ideas. This review examines the latest capabilities of immersive digital technologies and their future potential. We discuss the evolution of these technologies, including virtual reality, and their applications in various scientific, artistic, informational, and educational contexts. We conclude that virtual reality is both a technological innovation and a concept that can integrate with user experience (UX) and interaction design, aiding in the creation of new experiences and expressive models for diverse research and educational domains. © 2024 IEEE.

关键词： Virtual environments

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Optimization and multi-functional predictive performance of advanced superhydrophobic cementitious materials for water-resistant infrastructure

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Asian Journal of Civil engineering 2025年第5期26卷 2023-2036页

作者： Upadhye, Sachin Bagde, Prafulla Sange, Sanjay R. Rokade, Achal Chandak, Mayuri A. Patil, Tejas R. Kakade, Niteen T. Shelke, Nilesh Department of Computer Science and Application School of Computer Science and Engineering Ramdeobaba University Maharashtra Nagpur 440013 India Department of Mathematics Shri Ramdeobaba College of Engineering and Management Ramdeobaba University Maharashtra Nagpur 440013 India Shah And Anchor Kutchhi Engineering College Mumbai University Maharashtra Mumbai 400088 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India Department of Civil Engineering Priyadarshini Bhagwati College of Engineering Maharashtra Nagpur 440024 India Department of Mechanical Engineering Tulsiramji Gaikwad Patil College of Engineering and Technology Maharashtra Nagpur 441108 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India

The development of water-resistant infrastructure faces critical challenges due to water ingress, which compromises durability and structural integrity levels. Traditional approaches to hydrophobic concrete often fail to address long-term performance under dynamic environmental conditions, with limitations in durability, scalability, and multi-functionality. To overcome these shortcomings, this study introduces advanced superhydrophobic cementitious materials integrating innovative methodologies that simultaneously enhance hydrophobicity, durability, and functionality. Functionalized nanoparticles with long-chain alkyl silanes offer dual benefits of superior hydrophobicity (contact angle > 150°) and enhanced mechanical strength (> 50 MPa), addressing the challenge of balancing water repellency with structural properties. Gradient porosity via 3D printing enables hierarchical hydrophobic textures, reducing capillary absorption by 95% and extending durability in wet-dry cycles. Stimuli-responsive surface modifications embed microcapsules with hydrophobic agents that ensure self-healing, achieving up to 80% recovery in water repellency post-damage. Machine learning optimizes mix formulations, reducing experimental iterations by 70% while ensuring predictive accuracy (> 95%) for targeted performance. Multi-functional composites incorporating piezoelectric nanoparticles harvest ~ 10 µW/cm² energy while maintaining hydrophobicity, demonstrating potential for smart infrastructure applications. Freeze–thaw resistance is enhanced through cryo-protective agents, achieving > 300 cycles without degradation. The proposed methodologies validate their efficacy through a real-world application testing framework, ensuring scalability with minimal loss of water repellency (> 90%) and compressive strength retention (> 85%). This work not only offers groundbreaking solutions for water-resistant infrastructure but also paves the way for sustainable, resilient, and smart const

关键词： Gradient porosity Hydrophobic nanoparticles Self-healing cement Smart infrastructure Superhydrophobic concrete

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