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Hybrid Deep Learning for Breast Cancer Diagnosis: Evaluating CNN and ANN on BreakHis_v1_400X

Hybrid Deep Learning for Breast Cancer Diagnosis: Evaluating...

2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ogundokun, Roseline Oluwaseun Abdullahi, AbdulRahman Tosho Adenike, Ajiboye Raimot Awoniyi, Charles Akande, Hakeem Babalola Falajiki, Funmilola Adedayo-Ajayi, Victoria Landmark University Department of Computer Science Omu Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Kwara State Polytechnic Institute of Information and Communication Technology Department of Computer Science Nigeria Kwara State Polytechnic Institute of Applied Sciences Department of Statistics Nigeria Bupa Insurance Services London United Kingdom Telecommunication Science University of Ilorin Nigeria

ISBN: (纸本)9798350358155

Recent medical research has significantly emphasized the need for early illness diagnosis due to the fast rise of the world's population. Since there are now more people in the world, the danger of dying from breast cancer is rising at an exponential rate. Breast cancer is currently the second deadliest form of cancer out of all the forms that are known. When it comes to illness diagnosis, having access to an automated disease detection system is very helpful to medical professionals since it enables them to provide dependable, effective, and quick reactions while also lowering the mortality rate. This research assesses the hybridization of two deep learning models, a convolutional neural network (CNN) and an artificial neural network (ANN) - the well-known BreakHis_v1_400X dataset (a dataset for breast cancer histopathological images) to work with. The performance of these approaches is evaluated based on several criteria, including accuracy, sensitivity, specificity, precision, false-positive rate, and F1 score. In addition, we evaluate these methods by calculating the area under the curve for accuracy and recall, as well as the receiver operating characteristic curve. According to the findings, the hybridized CNN-ANN achieved the most remarkable accuracy, precision, AUC, and F1 score, with respective values of 89.47%, 86.18%, 89.46%, and 89.08%. © 2024 IEEE.

关键词： Lung cancer

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Trusting Computing as a Service for Blockchain Applications

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IEEE Internet of Things Journal 2023年第13期10卷 11326-11342页

作者： Li, Wen-Wei Meng, Weizhi Yeh, Kuo-Hui Cha, Shi-Cho National Taiwan University of Science and Technology Department of Information Management Taipei10607 Taiwan Technical University of Denmark Department of Applied Mathematics and Computer Science Copenhagen2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

Blockchain and smart contracts have become one of the most popular technologies to establish decentralized applications. Due to public visibility of data stored in blockchain, the information processed by a smart contract is visible to every party in a blockchain. To improve data confidentiality, this study proposes a trusted computing as a service (TCaaS) framework based on the blockchain. The framework includes a trustworthy node to perform trusted computing. Using the proposed framework, users of blockchain systems can integrate trustworthy node into blockchain systems. Users can also upload general-purposed programs and associated parameters and discover trustworthy node for execution via related smart contracts. The programs and parameters are encrypted so that only selected trustworthy node can decrypt the data. The execution environment returns computed results to the corresponding blockchain system. We evaluate our concept with the ESP32 microcontroller with the ATECC508A security chip and the Quorum blockchain system. Therefore, the study contributes to construct a trusted computing environment for blockchain system and preserve confidentiality at the same time. © 2014 IEEE.

关键词： Smart contract

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Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

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computer Systems science & Engineering 2023年第3期44卷 1869-1880页

作者： S.Siamala Devi K.Venkatachalam Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced *** diagnosis of many diseases will improve the patient *** cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation *** data generated through these technologies will demand the bandwidth,data rate,latency of the *** this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the *** nodes that are far away from edge node will forward the data to its neighbor cluster head using *** scheme will reduce the number of transmissions over the *** aggregated data are preprocessed at edge node to remove the noise for better *** node will reduce the overhead of cloud *** aggregated data are forward to cloud server for central storage and *** proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the *** cost for proposed system for 300 nodes is 0.34μ*** energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ *** optimization approaches and encryption method will ensure the data privacy.

关键词： Discrete grey wolf optimization data aggregation cloud computing IoT WSN smart healthcare elliptic curve elgamal energy optimization

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Predicting Chess Puzzle Difficulty with Transformers

Predicting Chess Puzzle Difficulty with Transformers

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IEEE International Conference on Big Data

作者： Szymon Miłosz Paweł Kapusta Institute of Applied Computer Science Lodz University of Technology Lodz Poland

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study addresses the challenge of quantifying chess puzzle difficulty - a complex task that combines elements of game theory and human cognition and underscores its critical role in effective chess training. We present GlickFormer, a novel transformer-based architecture that predicts chess puzzle difficulty by approximating the Glicko-2 rating system. Unlike conventional chess engines that optimize for game outcomes, GlickFormer models human perception of tactical patterns and problem-solving *** proposed model utilizes a modified ChessFormer backbone for spatial feature extraction and incorporates temporal information via factorized transformer techniques. This approach enables the capture of both spatial chess piece arrangements and move sequences, effectively modeling spatio-temporal relationships relevant to difficulty *** evaluation was conducted on a dataset of over 4 million chess puzzles. Results demonstrate GlickFormer’s superior performance compared to the state-of-the-art ChessFormer baseline across multiple metrics. The algorithm’s performance has also been recognized through its competitive results in the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty competition, where it placed *** insights gained from this study have implications for personalized chess training and broader applications in educational technology and cognitive modeling.

关键词： Training Measurement Accuracy Games Predictive models Transformers Feature extraction Data models Problem-solving Context modeling

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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computer Systems science & Engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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AI-Enabled Predictive Maintenance for Industrial Equipment in the Era of IoT 9

AI-Enabled Predictive Maintenance for Industrial Equipment i...

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9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Subbiah, Muthu Raman Devi, Kabirdoss Dhanamalar, M. Shareef, Ashraf Mohammed Ai-Shaikhli, Taha Raad Nidamanuri, Sudheer University of Technology And Applied Sciences Mie Section Department of Engineering Muscat Oman School of Management Studies Vels Institute-of Science Technology And Advanced Studies Chennai Tamil Nadu Pallavaram600117 India Kristu Jayanti College Department of Computer Science Karnataka Bengaluru India College of Pharmacy National University of Science And Technology DhiQar Iraq AL-Nisour University College Department of Medical Laboratories Technology Baghdad Iraq Madanapalle Institute of Technology And Science Computer Science And Technology Andhra Pradesh Madanapalle517325 India

ISBN: (纸本)9798350365092

Modern digital technology breakthroughs include machine learning. It's used in medical, image processing, manufacturing, aviation, autonomics, and more. Our main goal is to find an automated system that can accurately rate production machines at any given moment. The critical energy meter characteristics needed to create power reports in automation system to solve production difficulties at defined intervals were also not captured. We apply machine learning to estimate manufacturing machine accuracy in this study. We employed supervised machine learning of binary decision tree using CART method and power report, obtained data using RS232 to RS485 convertor through Modbus communication protocol, to overcome these challenges. This study presents issue formulation, energy meter data analysis and retrieval, and ML approaches used to estimate production machine accuracy. After retrieving the parameters, we provide power reports for each machine and a graphical warning of deteriorating performance. © 2024 IEEE.

关键词： Internet of things

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Body Weight-Based Animal Laboratory Classification Utilizing Load Cell and IoT

Body Weight-Based Animal Laboratory Classification Utilizing...

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International Conference of Information and Communication technology (ICoICT)

作者： Giva Andriana Mutiara Gita Indah Hapsari Muhammad Rizqy Alfarisi Periyadi Lisda Meisaroh Nadya Nanda Adisty Hadian Computer Technology Applied Science School Telkom University Bandung Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Animal laboratory experiments have important role in the development of specialist science related to health, biology, and pharmacy. To carry out the trial of medicines, the researchers require an animal laboratory experiment. In this case, mice and white rat which have the same basic properties, mammals as human used to find out whether the medicine reacts and suitable for curing various diseases. However, the research still experiences challenges in the process of selecting rats or mice that are suitable for experiments because it is still conducted manually, which still requires expertise from the researcher. This research proposes a system that can detect laboratory animals based on body weight, therefore it can help researchers in sorting out animal laboratory. This system is designed using load cell sensor, HX711 module, ESP32, LCD, motor servo, photodiode sensors as counter circuit. The system is linked to desktops and smartphones by utilizing IoT technology for reporting, monitoring, and manual control. The test results indicate that the accuracy of the weight measurement system still has a difference in the weight measurement range of 0.01 grams to 0.5 grams. Meanwhile, the percentage of success of the animal weight classification system reached 95% in 20 tests.

关键词： Weight measurement Animals Rats Mice Vaccines Motion measurement Monitoring Smart phones Testing Sorting

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Transfer learning in application to semantic segmentation of 3D indoor scenes scanned with a depth camera

Transfer learning in application to semantic segmentation of...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Ewelina Rosiak Artur Klepaczko Institute of Applied Computer Science Lodz University of Technology Lodz Poland

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

Recent achievements in the domain of computational intelligence and robotics trigger development and automation in various industry branches. One such domain is the architecture and construction, where the challenges related to efficient measurement, progress monitoring, maintenance, or verification of the agreement between a design and a built object, remain unsolved and may benefit from modern image and signal processing technology. Recognition of objects such as, e.g., walls, doors, or furniture in 3D scenes, allows the creation of so-called digital twins of the real-world environment. Within this study, we developed a series of deep learning models dedicated to semantic segmentation of point clouds representing interiors of buildings. Such representations are usually collected with expensive high-precision LiDAR scanners. The available neural network models, published in the literature, were, accordingly, trained using either such laser data or, alternatively, by the help of the synthetic ones. Our motivation was thus to enable semantic segmentation of RGBD point clouds acquired with a much cheaper sensor, i.e. a depth camera. In the absence of open real datasets, we propose to perform transfer learning and show that it is feasible to achieve reasonable segmentation accuracy on depth-camera data using a model pretrained on LiDAR scans.

关键词： Point cloud compression Laser radar Three-dimensional displays Semantic segmentation Transfer learning Signal processing algorithms computer architecture Signal processing Cameras Data models

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Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

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computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

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Recent Advances in Analysis and Detection of Tuberculosis System in Chest X-Ray Using Artificial Intelligence (AI) Techniques: A Review

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Current Materials science 2023年第1期16卷 43-51页

作者： Suchitra, Shanmugam Ibrahim, S. Jafar Ali Sathya, Mariappan Sahini, Varsha Chakravarthy, N. Surya Kalyan Kumar, Vaneet Saruchi Department of Computer Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT SCOPE Vellore Institute of Technology Tamilnadu Vellore India AMC Engineering College Karnataka Bengaluru India Department of Computer Science Engineering CT Institute of Engineering Management and Technology Punjab Jalandhar India Data Science QIS College of Engineering and Technology Andhra Pradhesh Ongole India Department of Applied Sciences CT Institute of Engineering Management and Technology Shahpur Campus Punjab Jalandhar India Department of Biotechnology CT Institute of Pharmaceutical Sciences Shahpur Campus Punjab Jalandhar India

Mycobacterium tuberculosis causes tuberculosis (TB), a bacterial illness. Although germs are most typically found in the lungs, they can affect other sections of the body as well. Tuberculosis is one of the primary causes of mortality in both developed and developing nations, necessitating worldwide attention. Even though TB may be prevented in the majority of instances if discovered and treated early, the number of deaths caused by the disease is quite high. There has been a significant increase in interest and research activity in TB detection in recent years. The new advancement in the field of AI technology may be able to assist them in overcoming these development gaps. computer-Aided Detection and Diagnosis (CADD) aids in the diagnosis of diseases by analysing symptoms and X-ray images of patients. Many solutions are currently be-ing developed to improve the effectiveness of TB diagnosis classification using AI and DL ap-proaches. Although a variety of TB detection techniques have been developed, there is no common-ly acknowledged method. The purpose of this study is to give a survey on Tuberculosis Detection. It also emphasises the difficulty and complexity of the Tuberculosis Detection System's design. © 2023 Bentham science Publishers.

关键词： computer vision

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