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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Margret Sharmila, F. Shree Harini, R. Vityarubbasiri, M. Gopi Krishna, S. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore641008 India

ISBN: (纸本)9798350394702

Early identification and diagnosis of cancerous cells will focus on the symptomatic patients, to give the best way of successful treatment. When the detection of cancer is delayed, then there is a lower chance of survival, high problems with treatment and higher cost estimates. Considering Pancreas Cancer growth, a type of cancer that is difficult to identify because there are less or no symptoms found at the early stage. So, we introduce Artificial Intelligence (AI) technology to identify pancreatic cancer cells. This is the first insight that is provided to detect cancerous cells at the early stage. With the use of technologies, anything is possible, so we use the process of medical imaging analysis, data processing and Machine Learning (ML) for the recognition. Data is collected through MRI and CT scan reports, biomarkers tests, liquid biopsies, endoscopic techniques, early symptom monitoring, and genetic testing which is then categorized into train and test sets. Every training data set is analyzed to provide a standard image to extract the features with the use of OpenCV. Building an AI model with the use of the Convolutional Neural Network (CNN) algorithm with an efficient set of filters. Training the model with the training data set helps to improve the accuracy of predicting results. When it is ready, the testing data set is used to check the accuracy of predictions. Finally, by assessing the model based on the performance metrics from both training and testing datasets, accuracy is achieved. After achieving a certain level of accuracy, and leveraging ML algorithms such as Hybrid models, the system can provide treatment recommendations consistent with a separate training dataset. This training dataset comprises predicted treatments corresponding to the achieved accuracy. After, the system is prepared for deployment. The envisioned process can track the patient's status for future use. © 2023 IEEE.

关键词： Forecasting

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Smart Material Selection: Leveraging Machine Learning for Sustainable and Cost-Effective Building Design 2

Smart Material Selection: Leveraging Machine Learning for Su...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Hashim, Wassan Adnan Khoshaba, Lina Sami Kareem, Shahab Wahhab Khoshaba, Farah Sami Al-Qalam University College Medical Device Engineering Department Kirkuk Iraq Lebanese French University College of Engineering and Computer Science Architecture Engineering Department Erbil Iraq Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq

ISBN: (纸本)9798331539542

In modern-day architecture, the choice of building material plays an important role in sustainability, cost-value engineering, and design integrity. Moreover, the traditional methods for selecting materials tended to make use of manual approaches, which at times led to drawbacks by not focusing on the basics of integration and proper data management. The paper is a very interesting approach of how a complex material selection process can be optimized with simple machine learning algorithms. In that sense, the current research developed a framework from environmental sustainability to economic cost, durability, and aesthetic appeal, with an intention for its use in guiding the design final decision process. The proposed machine learning model suggests ideal prediction for material combination from different material datasets concerning the properties and performance metrics applicable to architecture. The research showed that the approach had a hand in getting thoughtful, analytic-based decisions while assuring, at the same time, that an architect would be able to satisfy the project goals and budget constraints with environmentally safe execution. It elaborates on the cases, or rather case studies, that have applied the model and gives illustrations of great improvements not only in efficiency in design but also in the sustainability of the project coupled with better strategies for saving costs. The current research is opening an entirely new field of innovation in architecture that emphasizes the role of technology for the future form of building practice. © 2024 IEEE.

关键词： Budget control

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A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses

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npj Computational Materials 2023年第1期9卷 2196-2203页

作者： Ziqing Zhou Yinghui Shang Xiaodi Liu Yong Yang Department of Mechanical Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina City University of Hong Kong(Dongguan) 523000 DongguanChina College of Mechatronics and Control Engineering Shenzhen University518060 ShenzhenChina Department of Materials Science and Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina Department of Advanced Design and System Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina

The design of bulk metallic glasses(BMGs)via machine learning(ML)has been a topic of active research ***,the prior ML models were mostly built upon supervised learning algorithms with human inputs to navigate the high dimensional compositional space,which becomes inefficient with the increasing compositional complexity in ***,we develop a generative deep-learning framework to directly generate compositionally complex BMGs,such as high entropy *** framework is built on the unsupervised Generative Adversarial Network(GAN)algorithm for data generation and the supervised Boosted Trees algorithm for data *** studied systematically the confounding effect of various data descriptors and the literature data on the effectiveness of our framework both numerically and *** importantly,we demonstrate that our generative deep learning framework is capable of producing composition-property mappings,therefore paving the way for the inverse design of BMGs.

关键词： inverse metallic entropy

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Machine Learning for Predictive Modeling and Personalized Treatment in Magnesium-based Biomedical Applications 8

Machine Learning for Predictive Modeling and Personalized Tr...

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8th IEEE International Conference on Computational system and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Jnyanadeep, Bandaru Sahana, S. Suresh, Rohit Roopa, T.S. Angadi, Gangadhar Bengaluru India Rv College of Engineering® Department of Computer Science and Engineering Bengaluru India Rv College of Engineering® Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9798331505462

This paper explores the integration of machine learning (ML) techniques with magnesium-based biomedical applications, focusing on predictive modeling and personalized treatment strategies. Magnesium's biocompatibility and biodegradability make it an attractive material for various medical applications, including implants and drug delivery systems. However, optimizing its use for individual patients remains challenging. In this paper it is proposed that ML can bridge this gap by enabling more accurate predictions of treatment outcomes and facilitating personalized approaches. Through a comprehensive review of existing literature and analysis of case studies, it is demonstrated that how supervised learning, unsupervised learning, and deep learning techniques can be applied to improve patient outcomes in magnesium-based treatments. The paper also addresses challenges such as data privacy and model interpretability, proposing solutions for integrating ML into clinical workflows. Our findings suggest that the synergy between ML and magnesium-based biomedical applications has the potential to significantly advance personalized medicine, opening new avenues for research and clinical practice. © 2024 IEEE.

关键词： Self-supervised learning

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Unleashing the Potential of IoT Integration for Energy Optimization in Smart Homes 3

Unleashing the Potential of IoT Integration for Energy Optim...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Tiwari, Shivam Bhushan, Aditya Singh, Ashutosh Kumar Yadav, Rakesh Kumar United College of Engineering & Research Department of Mechanical Engineering Prayagraj India United College of Engineering & Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350309409

This research paper explores the transformative potential of integrating Internet of Things (IoT) devices into smart home technologies, ushering in a new era of energy-efficient living. Focusing on energy optimization, the paper examines the intricate network of interconnected devices shaping efficient residential ecosystems. Beginning with an investigates specific devices such as smart thermostats and intelligent lighting systems. Through meticulous analysis of user behavior, it unveils how these devices influence and mold household energy consumption. Spotlighting data analytics, the paper reveals the power of information processing for personalized, efficient energy management. Addressing challenges like privacy and interoperability, it proposes innovative solutions for seamless IoT integration. The research concludes with an environmental assessment, emphasizing the symbiotic relationship between IoT and smart homes in fostering a sustainable and eco-friendly future. © 2024 IEEE.

关键词： Internet of things

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

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Intelligent Automation & Soft Computing 2023年第3期35卷 3403-3413页

作者： S.Ayshwarya Lakshmi K.Anandavelu Department of Computer Science and Engineering University College of Engineering Panruti607106TamilnaduIndia Department of Mechanical Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer *** suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation *** time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difﬁ*** of the most signiﬁcant requirements is to design optimal solutions to optimize their *** is no particular technique that can answer all optimization *** proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized *** accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization *** total sensitivity and speciﬁcity attained in the proposed system are 99.56%and 99.73%*** proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional *** proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefﬁ*** model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

关键词： Cukoo search optimization technique ﬁtness function cancer

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Attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Karkera, Thejas Singh, Chandra Bekal, Anush Shetty, Shailesh Prajwal, P. Department of Electronics Communication Engineering Sahyadri College of Engineering and Management Mangalore India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Covid-19 pandemic is spreading across the world at a breakneck pace. As of September 2022, JHU CSSE COVID-19 Data and Our World in Data have recorded up to 5.2 M deaths due to Covid-19. One of the essential steps of fighting this pandemic is to detect the disease early. Studies show some abnormalities in the chest X-rays of the Covid-19 patients, and these features help classify the covid-19 positive patients from the negative patients. In recent years in the field of medical x-rays, Deep Learning models have proven to have the ability to learn and identify features that a trained person can only identify. Due to the exponential increase in the covid-19 cases, the waiting time for covid results has increased, resulting in the late diagnosis of patients for Covid-19. Classifying a patient as Covid-19 positive for a genuinely covid positive patient is as essential as classifying a patient Covid-19 negative who is genuinely covid-negative. Here we present a detailed quantitative analysis on the performance of state-of-the-art models like ResNet-50, Dense-Net, Mobile-Net-V2, and MNAS-Net on classifying patients for Covid positive, Pneumonia positive, and Normal by evaluating Accuracy, Precision, Recall, and Jaccard Index. We propose a new deep neural network classification model for low-end devices that uses two Attention mechanisms. Figure 1. shows the attention map with confidence percentage for each classes. Our model uses fewer parameters and FLOPS than other state-of-the-art models and recorded a 2% increase in Accuracy and other evaluation parameters. The dataset used for implementation is a public dataset COVID-DATASET and NIH Chest X-ray from kaggle. It has over 1000 images of Covid Chest X-ray, Pneumothrax, Mass, Pneumonia, Cardiomegaly, Nodule, Effusion, Atelectasis and inflitration. All the model implementations are implemented on PyTorch. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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