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Predictive Modeling of Shear Capacity for FRCM-Strengthened RC Beams using MLP Neural Networks and Global Sensitivity Analysis for Sustainable Construction

Predictive Modeling of Shear Capacity for FRCM-Strengthened ...

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BIT Conference (BITCON), International

作者： Owais Ahmad Shah Rupesh Kumar Tipu Satvik Vats Shardha Purohit Shivangi Bansal Anupam Kumar Yadav Aditya Verma Department of Electronics & Communication Engineering Dayanand Sagar University Bengaluru India School of Engineering & Technology K. R. Mangalam University Gurugram India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India School of Journalism & Mass Communication Noida International University India Department of Electronics & Communication Engineering Noida International University India Department of Electronics & Communication Engineering NIET Deemed to be University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

This work, focuses on the development and prediction ability assessment of a multilayer perceptron (MLP) neural network model to forecast the FRCM-RC beam’s shear strength. The dataset includes 89 samples of the experiment and has been pre-processed with the MinMax Scaling for better model convergence. The MLP model again demonstrates quite robust performance, reaching a high R 2 score of 0.938, which is yet more indicative of this model’s capability to cover 93.8% of shear capacity variability. The accuracy of the model was further validated by model evaluation metrics: MSE, RMSE, MAE, and MAPE. An Actual vs Predicted plot was further used to substantiate this. Global sensitivity analysis pointed out that the highly important role of reinforcement ratios (ρ l , ρ w , ρ f , ρ cm ) on predictions of shear capacity is brought towards valuable insight to optimization. This study would, therefore, provide a reliable predictive tool for engineers to further enhance the understanding and improvements to FRCM-strengthened RC beam design.

关键词： Measurement Analytical models Sensitivity analysis Neural networks Predictive models Multilayer perceptrons Optical fiber networks Reliability engineering Structural beams Optimization

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Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

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computers, Materials & Continua 2022年第8期72卷 3651-3663页

作者： Osama S.Faragallah Ashraf Afifi Ibrahim F.Elashry Ensherah A.Naeem Heba M.El-Hoseny Ahmed I.Sallam Hala S.El-sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering Kafrelsheikh UniversityKafrelsheikh 61519Egypt Electrical Department Faculty of Industrial EducationSuez UniversitySuez 43527Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology 3036Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt

In the last few years,videos became the most common form of information transmitted over the internet,and a lot of the traffic is confidential and must be protected and delivered safely to its intended *** introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same *** paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map(LASM)in the Fractional Fourier Transform(FrFT).In the presented opto-video LASM-based FrFT scheme,the encoded video is split into distinct frames and transformed into optical signals utilizing an optical *** of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame,the first in the time-domain and the second in the FrFT *** the end,the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a *** test the proposed opto-video LASM-based FrFT scheme using various security *** outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video *** addition,it encrypts videos with a high level of encryption qualitywithout sacrificing its resistance to noise ***,the test outcomes demonstrate that the presented scheme is immune to known attacks.

关键词： Optical encryption FrFT logistic-adjusted sine map

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Unleashing the Potential of Artificial Intelligence and Deep Learning in Pneumonia Detection Systems 9

Unleashing the Potential of Artificial Intelligence and Deep...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Prakash, V.S. Aiyyasamy, Velu Shobana, D. Jayadurga, R. Kandaswamy, Vijay Anand Sundara Bala Murugan, P. Kristu Jayanti College Department of Computer Science Karnataka Bengaluru India Meenakshi Sundararajan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St.Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Bacteria, viruses, and fungi are all examples of microorganisms that have the potential to infect the lungs and cause pneumonia. This infection causes the alveoli of the lungs to become inflamed, which makes it difficult for people to breathe. The severity of the condition ranges from mild to severe. Blood tests, pulse oximetry, and chest X-rays are three of the diagnostic procedures that are utilized. Artificial intelligence (AI) and deep learning have been utilized for image analysis in a variety of medical sectors throughout the course of the past few years. They are pretty helpful when it comes to extracting complicated information from the photographs that are loaded into the system. When it comes to the detection and classification of lung disorders, deep learning is very necessary. This research provides a comprehensive analysis of the medical image pneumonia identification strategies that are utilized in deep learning. The goal of this study is to introduce a novel approach called Artificial Intelligence based Learning for Pneumonia Detection (AILPD), in which it is used to explain its implementation in a system for detecting the pneumonia illness, to present a review of recent advancements in the field, and to provide ideas for future possibilities along similar lines. The purpose of this research is to evaluate the effectiveness of the proposed technique by contrasting the proposed algorithm with the traditional Convolutional Neural Network (CNN) approach. Additionally, this research offers a full review of the deep learning technologies that are utilized in the prevention and treatment of pneumonia. © 2023 IEEE.

关键词： Convolutional neural networks

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STAR-RIS Assisted Covert Wireless communications with Randomly Distributed Blockages

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IEEE Transactions on Wireless communications 2025年第6期24卷 4690-4705页

作者： Li, Xingwang Zhao, Jingwei Chen, Gaojie Hao, Wanming Da Costa, Daniel Benevides Nallanathan, Arumugam Shin, Hyundong Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shenzhen518107 China 5GIC 6GIC University of Surrey United Kingdom Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS. 2002-2012 IEEE.

关键词： Numerical methods

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An Improved Hybrid Deep Learning Approach with Explainable AI for COVID-19 Detection

An Improved Hybrid Deep Learning Approach with Explainable A...

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International Conference on Electrical, computer and communication engineering (ECCE)

作者： Farhana Ahmmed Afia Jahin Fatema Tuz Zohora M. Jamshed Alam Patwary Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Institute of Information & Communication Technology Chittagong University of Engineering & Technology Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

COVID-19 has posed a significant global challenge since its outbreak. Numerous studies have explored machine learning models to detect COVID-19 from CT scan images, with most relying on generic Convolutional Neural Network(CNN) architectures. However, these models often struggle to capture important features in medical images. In response to this limitation, a customized CNN is developed and combined with pre-trained models such as ResNet, DenseNet and MobileNet. This hybrid model enables more detailed feature extraction. To ensure transparency in decision-making, Explainable AI techniques such as Grad-CAM and LIME are applied. The best model has achieved an accuracy of 98% on the publicly available SARS-CoV-2 CT Scan Dataset, demonstrating its potential for reliable COVID-19 detection in clinical settings. This approach not only improves diagnostic accuracy but also provides clearer insights into the model’s predictions, making it a valuable tool in medical image analysis.

关键词： COVID-19 Deep learning Accuracy Explainable AI Computed tomography Computational modeling Predictive models Data models Convolutional neural networks Medical diagnostic imaging

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Development Of an Intelligent Traffic Navigation System by Wireless Sensor Network System

Development Of an Intelligent Traffic Navigation System by W...

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2023 International Conference on New Frontiers in communication, Automation, Management and Security, ICCAMS 2023

作者： Kaushal, Rajanish Kumar Kumar, B.Anjanee Chauhan, Krupesh A. Munagala, Madhu Singh, Pavitar Parkash Hazela, Bramah Chandigarh University Department of Electrical Engineering Punjab Mohali India Dadi Institute of Engineering and Technology Anakapalle Electronics and Communication Engineering Visakhapatnam India Sardar Vallabhbhai National Institute of Technology Department of Civil Engineering Surat India Koneru Lakshmaiah Education Foundation Department of English Vaddeswaram India Lovely Professional University Department of Management Phagwara India Amity University Amity School of Engineering & Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350317060

The use of automobiles is becoming more important worldwide, especially in densely populated cities. Current transportation infrastructure technologies include wired sensors, inductive loops, and satellite navigation. The installation and upkeep of surveillance equipment (cameras, etc.) are quite costly. In addition, environmental factors affect these instruments' precision. Traditional methods typically aim to optimize traffic light management for a given traffic density and layout. One key draws back all these methods, however, it may be challenging to represent the dynamic behaviour of traffic volumes and configurations, which is always changing. Therefore, in an effort to solve the aforementioned problem, this study proposes methods for adaptive traffic light management via the use of a distributed sensor network. The purpose of this study is to examine approaches to developing a smart system that can integrate and support some of the current technologies of traffic management, therefore decreasing the typical wait time for cars at a junction. The suggested algorithms are flexible enough to adjust to the particulars of traffic at every given crossroads. (GLD) is a traffic simulation tool used to model real-world traffic conditions and provide graphs of average waiting time vs cycle counts. The collected findings validate the efficacy of the suggested strategy for traffic regulation at a real-world road junction. © 2023 IEEE.

关键词： Wireless sensor networks

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Analyzing the Potential Benefits of Data Mining for Customer Relationship Management 1

Analyzing the Potential Benefits of Data Mining for Customer...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Aravind, A.R. Katikar, Shilpa M. Acharjya, Kalyan Ezhilarasan, G. Yadav, Surendra Mishra, Abhinav Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350359756

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty. © 2023 IEEE.

关键词： Data mining

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VIKAS: A Multimodal Framework to Aid in Effective Disaster Management 13th

VIKAS: A Multimodal Framework to Aid in Effective Disaster M...

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13th International Conference on Applications and Techniques in Information Security, ATIS 2022

作者： Prabhu, Gautham Manuru Gupta, Tanay Srujan, Metta Venkata Soumya, A.R. Palorkar, Anshita Chowdhury, Anurag Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819922635

In the event of a disaster, social media is often used to draw attention to affected areas and distressed people. The massive population and diversity in Indian languages warrant a novel real-time, big-data solution that can increase situational awareness, reduce special forces’ response time, and expedite decision-making. The proposed solution, VIKAS, streams text, images, videos, and audio from posts on microblogging platforms using keywords. It then uses the Google Translate and Transliteration APIs to handle multilingual and macaronic hybrid text, including Hinglish. An Apache Kafka event pipeline processes the sheer volume of posts asynchronously. Duplicate, uninformative, or bot-posted data (checked using the Botometer machine learning algorithm) is discarded. Scraped data is also verified through Google’s FactCheck Explorer API. Audio and video clips are processed leveraging speech-to-text methods. The solution incorporates a BERT pre-trained model and word embeddings for natural language processing tasks, including sentiment analysis and classification of textual data. Image classification and object identification are implemented using a ResNet deep learning model. This multimodal approach pinpoints locations using nearby landmarks, severity, and type of support needed, ranging from humanitarian aid and rescue relief to infrastructure damage. Easy-to-interpret visualizations on an accessible dashboard consolidate many details that can streamline resource distribution and personnel deployment. VIKAS was presented to the National Disaster Response Force at the national-level finals of the Smart India Hackathon 2022. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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End-to-End Classification of Transmission Lines in Fault Detection Using SW and Extreme Learning Based Approach

End-to-End Classification of Transmission Lines in Fault Det...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Cholla, Ravindra Raman Kakkar, Sushma Natarajan, Chidambararaj Gulati, Chahat Dwivedi, Vijay Kumar Rajput, Akshay Department of Cse Karnataka Bengluru India Sharda School of Engineering and Technology Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Chennai India Birla Institute of Technology and Science Department Ece Pilani Campus Pilani Rajasthan Vidyavihar India Vishwavidyalaya Engineering College Department of Mathematics Surguja Chhattisgarh Ambikapur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300857

The reliability of the power grid is dependent on transient stability. In order to swiftly restore transient stability, large disturbances, such a fault in a transmission line, must be separated. Faults in a transmission network can be located, identified, and categorized with the use of erroneous current and voltage signals. When an aberrant signal is detected, the relay immediately disables the transmission line carrying the bad news. Fault detection systems are crucial for transmission lines. The proposed technique for preprocessing makes use of a solid-state drive (SSD) detection model, a quicker RCNN detection model, and a logical reasoning in parallel mechanism. To choose features, SWELM is employed. The envisioned model is trained using SWELM in the given technique. The suggested model is tested, and the ELM and SWELM models are used for comparison. When compared to the other two varieties, its performance is far superior. © 2023 IEEE.

关键词： Electric lines

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Re-construction of original face images from plastic surgery images using harmony search algorithm and IRBFN model

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Measurement: Sensors 2024年 31卷

作者： S K, Shiji Krishnaveni, S.H. Suresh, L. Padma Department of Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamilnadu India Department of Computer Science and Engineering Baselios Mathews II College of Engineering Kollam India Department of Electrical and Electronics Engineering Baselios Mathews II College of Engineering Kollam India

Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images. © 2023

关键词： Radial basis function networks

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