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28th International computer Science and engineering Conference, ICSEC 2024

作者： Samngamnoi, Kruntarat Pomsiri, Sutinun Kamput, Apipath Dechsupa, Chanon College of Computing Khon Kaen University Intelligent Software Engineering Research Group Department of Computer Science Khon Kaen40002 Thailand

ISBN: (纸本)9798350366860

In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochastic Petri Net (CGSPN), a formal language, facilitate correctness checks of such systems. This study proposes a verification approach by translating a C++-based system controller of a self-service machine into a CGSPN models and validating it using the Snoopy Tool. Mapping techniques employed to transform components in the controller into CGSPN models are provided. Results demonstrate the method's efficacy in verifying system safety properties, simulating system events, and enabling quantitative verification. © 2024 IEEE.

关键词： Stochastic systems

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Breaking the Boundaries of Oncological Diagnosis: A Holistic Framework for Multi-Cancer Identification and Comprehensive Diagnostic Reporting Utilizing Advanced AI and NLP

Breaking the Boundaries of Oncological Diagnosis: A Holistic...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Sai Santhosh, V.C. Ponraj, Riya Nikhil Eshwar, T. Kiran, K. Easwari Engineering College Computer Science and Engineering Department Chennai India Sri Krishna College of Technology Electrical and Electronics Engineering Department Coimbatore India

ISBN: (纸本)9798350306927

Innovative deep learning models for cancer classification, including VGG-19, DenseNet201, MobileNetV3, ResNet50V2, YOLOv5, and GPT-2, have completely changed the way that doctors diagnose cancer. This study introduces a multi-modal technique for an accurate and speedy cancer diagnosis. With the aid of cutting-edge technology, this system combines object identification (YOLOv5), natural language processing (GPT-2), and picture classification models (VGG-19, DenseNet201, MobileNetV3, ResNet50V2) to provide scientists and medical professionals with a flexible toolkit. It generates thorough reports with tumour images and spots malignant irregularities. Diagnostic accuracy is improved by real-time application, which benefits laboratories by reducing turnaround times and medical practitioners by providing an important decision support tool. Reports with tumour photos enhance the capacity to comprehend results. This study is important because it has the potential to improve cancer detection by using cutting-edge algorithms and models. This research also promises to improve patient care, prediction, and therapy. The practical method presented in this paper ushers in a new age in medical diagnostics by empowering laboratories and doctors. © 2023 IEEE.

关键词： Learning algorithms

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Interoperability and Standardization in the Internet of Things 9

Interoperability and Standardization in the Internet of Thin...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Saidala, Ravi Kumar Haseeb, V.V. Padmapriya, G. Jayabalan, S. Priya, V. Kalai Shaikh, Anwar Ahamed Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Nam College Department of Computer Science Kerala Tamil Nadu Kallikkandy 670693 India School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Panimalar Engineering College Department of Computer Science And Business System Tamil Nadu Chennai600123 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Puduvoyal601206 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350365092

This research highlights the importance of standards and interoperability in the IoT. An essential component of IoT ecosystems is interoperability, which refers to the ability for various systems and devices to converse and share data. Formats for data, methods of communication, and safeguards are all outlined by standards. Widespread adoption and scalability are made possible through interoperability and standards, which allow devices from many manufacturers to communicate with one another. This research investigates the alliances, regulatory authorities, and commercial associations that cooperate in the creation and advancement of open standards that can be embraced by other entities. The main objective of the research is to showcase the construction of an Internet of Things device capable of communicating with various embedded peripheral protocols utilizing Plug and Play (PnP) technology. Moreover, the research examines the impact of regulatory frameworks on the IoT ecosystem and their role in fostering interoperability. An IoT ecosystem necessitates a standardized and open environment to provide optimal performance in terms of connectivity, data exchange, and innovation. This research contributes to the progress of harmonizing and achieving interoperability among World Wide Web Things. © 2024 IEEE.

关键词： Interoperability

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Towards Interpretable, Attention-Based Crime Forecasting

Towards Interpretable, Attention-Based Crime Forecasting

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Ma, Yujunrong Qi, Xiaowen Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering College Park United States University of Maryland Department of Criminology and Criminal Justice College Park United States Institute for Advanced Computer Studies University of Maryland Department of Electrical and Computer Engineering College Park United States

ISBN: (纸本)9798350337020

While the use of machine learning techniques in high stake fields, such as medical diagnosis and criminal justice, has been increasing in recent years, concerns have been raised regarding the lack of transparency and interpretability of the algorithms used. In this paper, we propose the use of interpretable attention-based ConvLSTM models for crime forecasting application. This approach combines the power of ConvLSTM models in capturing spatio-temporal patterns with the interpretability of attention mechanisms, allowing for the identification of key geographic areas in the input data that contribute to the prediction. We demonstrate the effectiveness of this approach through experiments on real-world crime data, showing that our model demonstrates high accuracy in crime predictions while providing insightful visualization that enhances the interpretability of prediction results. © 2023 IEEE.

关键词： ConvLSTM Interpretable machine learning Self attention

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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computer Systems Science & engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

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Deep Learning Based Multiclass Tumor Identification and Classification Using Fusion of CNN Models 17

Deep Learning Based Multiclass Tumor Identification and Clas...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Saeed, Zubair Torfeh, Tarraf Aouadi, Souha Ji, Jim Xiuquan Bouhali, Othmane Texas A&m University Department of Electrical and Computer Engineering College StationTX47080 United States Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar Hamad Medical Corporation National Center for Cancer Care and Research Department of Radiation Oncology Doha3050 Qatar Hamad Bin Khalifa University Qatar Center for Quantum Computing College of Science and Engineering Doha34110 Qatar

ISBN: (纸本)9798350387384

The rising incidents of brain tumors pose significant challenges in diagnosis and treatment. Tumorous brain cancers present unique difficulties due to their critical locations and functional implications. Magnetic Resonance Imaging (MRI) offers safe and precise imaging, free from the risks associated with radiation-based methods. Deep learning, particularly Convolutional Neural Networks (CNN), has emerged as a valuable tool in identifying brain tumors in MRI scans, enabling more accurate diagnoses by fusing two well-known DL models. This study explores a fusion strategy combining AlexNet and VGG16 models, outperforming individual models trained with different learning rates. While VGG16 and AlexNet achieved accuracies of 79% and 77% respectively. Our fusion approach potentially achieved an accuracy of 81%, underscoring its efficacy in enhancing brain tumor classification accuracy. This study helps medical professionals to accurately and timely classify tumors for better outcomes for patients. © 2024 IEEE.

关键词： Convolutional neural networks

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DataFreeShield: Defending Adversarial Attacks without Training Data 41

DataFreeShield: Defending Adversarial Attacks without Traini...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Hyeyoon Choi, Kanghyun Kwon, Dain Park, Sunjong Jaiswal, Mayoore Selvarasa Park, Noseong Choi, Jonghyun Lee, Jinho Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of NVIDIA United States School of Computing KAIST Daejeon Korea Republic of

Recent advances in adversarial robustness rely on an abundant set of training data, where using external or additional datasets has become a common setting. However, in real life, the training data is often kept private for security and privacy issues, while only the pretrained weight is available to the public. In such scenarios, existing methods that assume accessibility to the original data become inapplicable. Thus we investigate the pivotal problem of data-free adversarial robustness, where we try to achieve adversarial robustness without accessing any real data. Through a preliminary study, we highlight the severity of the problem by showing that robustness without the original dataset is difficult to achieve, even with similar domain datasets. To address this issue, we propose DataFreeShield, which tackles the problem from two perspectives: surrogate dataset generation and adversarial training using the generated data. Through extensive validation, we show that DataFreeShield outperforms baselines, demonstrating that the proposed method sets the first entirely data-free solution for the adversarial robustness problem. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Novel Face Mask Detection Technique using Machine Learning to control COVID’19 pandemic

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Materials Today: Proceedings 2023年 80卷 3714-3718页

作者： Gupta, Sandeep Sreenivasu, S.V.N. Chouhan, Kuldeep Shrivastava, Anurag Sahu, Bharti Manohar Potdar, Ravindra Department of Electrical Engineering JECRC University Jaipur India Department of Computer Science and Engineering Narasaraopeta Engineering College Andhra Pradesh Narasaraopet India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Lakshmi Narain College of Technology and Science Indore India Department of Computer Science and Engineering Dr. D.Y. Patil Institute of Technology Pimpri Pune India Department of Electronics and Telecommunication Engineering Bhilai Institute of Technology Chhattisgarh Durg India

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of antiviral drugs is one of the reasons for the spreading of COVID-19 virus. This disease is spreading continuously and easily due to some common mistakes by people, like breathing, coughing and sneezing by infected persons. The main symptom is the normal flu. Therefore, in the present condition, the best precaution for this disease is the face mask, which covers both areas of mouth & nose. According to the government and the World Health Organization, everyone should wear a face mask in busy places like hospitals and marketplaces. In today's environment, it's difficult to tell if someone is wearing a mask or not, and physical inspection is impractical since it adds to labour costs. In this research, we present a mask detector that uses a machine learning facial categorization system to determine whether a person is wearing a mask or not, so that it may be connected to a CCTV system to verify that only persons wearing masks are allowed in. © 2021

关键词： COVID-19

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Power Domain Multiplexing Waveform for 5G Wireless Networks

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computers, Materials & Continua 2022年第1期70卷 2083-2095页

作者： Korhan Cengiz Imran Baig Sumit Chakravarty Arun Kumar Mahmoud A.Albreem Mohammed H.Alsharif Peerapong Uthansakul Jamel Nebhen Ayman A.Aly Department of Electrical-Electronics Engineering Trakya University22030EdirneTurkey Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Sultanate of Oman Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA Department of ECE JECRC UniversityJaipur303905India Department of Electronics and Communication Engineering A’Sharqiyah UniversityIbra400Oman Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006Korea School of Telecommunication Engineering Suranaree University of TechnologyNakhonRatchasimaThailand Prince Sattam bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia

Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier(NOMA-UFMC)has the potential to cope with fifth generation(5G)unprecedented *** employs powerdomainmultiplexing to support several users,whereasUFMC is robust to timing and frequency ***,NOMA-UFMC waveform has a high peak-to-average power(PAPR)issue that creates a negative affect due to multicarrier modulations,rendering it is inefficient for the impending 5G mobile and wireless ***,this article seeks to presents a discrete Hartley transform(DHT)pre-coding-based NOMA enabled universal filter multicarrier(UFMC)(DHT-NOMA-UFMC)waveform design for lowering the high ***,DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate(BER)*** the recommended arrangement,the throughput of the systemis improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned *** simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional ***,it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC *** key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting ***,a huge count of mobile devices could be supported under specific ***-UFMC can be regarded as the most effective applications for 5G Mobile andWireless ***,the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.

关键词： NOMA-UFMC 5G PAPR BER DHT-NOMA-UFMC

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