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Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

computer Systems science & engineering 2023年第1期44卷 945-959页

作者： G.Ravikumar K.Venkatachalam Mohammed A.AlZain Mehedi Masud Mohamed Abouhawwash Department of Computer Science and Engineering Coimbatore Institute of Engineering and TechnologyCoimbatore641109India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Sleep apnea syndrome(SAS)is a breathing disorder while a person is *** traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a *** typically provides accurate results,but it is expensive and time ***,for people with Sleep apnea(SA),available beds and laboratories are ***,it may produce inaccurate ***,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)*** paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen *** diagnosis information is securely stored in the IoMT fog computing *** can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing *** proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

关键词： Sleep apnea polysomnography IOMT fog node security neural network KNN signature encryption sensor

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Streamlining Attendance with Fast Face Recognition Model 8

Streamlining Attendance with Fast Face Recognition Model

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundar, Koushik Ravikumar, S. Jeyalakshmi, J. Eugene Berna, I. Samuel, Prithi Citibank Na Jersey CityNJ United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India

ISBN: (纸本)9798350376425

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment. © 2024 IEEE.

关键词： Database systems

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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Hybrid CNN Classifier for Accurate Detection of Oral Cysts in Panoramic Dental Radiograph 9

Hybrid CNN Classifier for Accurate Detection of Oral Cysts i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivagami, S. Senthilvadivu, S. Geetha, P. Yukthavarshika, J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

In dentistry, machine learning approaches play a significant role in the early detection and diagnosis of oral cysts. When planning accurate medical treatment, oral and maxillofacial surgeons can benefit from preoperative detection of these cysts. Surgical planning is directly affected by classifying oral cysts as dentigerous, radicular, ameloblastoma, odontogenic carcinoma, and odontogenic keratocyte. In dentistry, the outcomes of differential diagnosis based solely on imaging are unacceptable. This research proposes a three-step framework for autonomous cyst classification, segmentation, and detection based on a Convolutional Neural Network (CNN). To begin, DENT-CNN, a modified CNN architecture is proposed to classify the oral cyst as normal or abnormal. Second, UNet is a CNN variant used to segment the categorized cyst regions on panoramic dental images. Third, the Gray Level Co-occurrence Matrix (GLCM) is combined with CNN to identify segmented cysts as benign or malignant. The proposed work is extensively evaluated using 1500 real-time dental panoramic radiographic images of the oral cyst collected from a dental hospital. To alleviate overfitting, the data augmentation approach is used. The performance of the proposed work is analyzed in terms of a cyst classification rate of 96.8 %, a Dice Score of 96.3 % for cyst segmentation, and an accuracy for the Identification phase of 96.5%. © 2024 IEEE.

关键词： Dentistry

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Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity

arXiv

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arXiv 2024年

作者： Nguyen, Quan Mehta, Nishant A. Guzmán, Cristóbal Department of Computer Science University of Victoria Canada Institute for Mathematical and Computational Engineering Faculty of Mathematics School of Engineering Pontificia Universidad Católica de Chile Chile

The minimax sample complexity of group distributionally robust optimization (GDRO) has been determined up to a log(K) factor, where K is the number of groups. In this work, we venture beyond the minimax perspective via a novel notion of sparsity that we dub (λ, β)-sparsity. In short, this condition means that at any parameter θ, there is a set of at most β groups whose risks at θ all are at least λ larger than the risks of the other groups. To find an ϵ-optimal θ, we show via a novel algorithm and analysis that the ϵ-dependent term in the sample complexity can swap a linear dependence on K for a linear dependence on the potentially much smaller β. This improvement leverages recent progress in sleeping bandits, showing a fundamental connection between the two-player zero-sum game optimization framework for GDRO and per-action regret bounds in sleeping bandits. We next show an adaptive algorithm which, up to log factors, gets a sample complexity bound that adapts to the best (λ, β)-sparsity condition that holds. We also show how to get a dimension-free semi-adaptive sample complexity bound with a computationally efficient method. Finally, we demonstrate the practicality of the (λ, β)-sparsity condition and the improved sample efficiency of our algorithms on both synthetic and real-life datasets. © 2024, CC BY.

关键词： Adaptive algorithms

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PFAS Incineration: Known Unknowns, Reaction Simulation, and ML to Predict Product Properties

PFAS Incineration: Known Unknowns, Reaction Simulation, and ...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Soiffer, Duncan Pyorre, Tuomas Kenney, David Teixeira, Andrew Worcester Polytechnic Institute Department of Computer Science and Mathematical Sciences WorcesterMA United States Worcester Polytechnic Institute Department of Robotics Engineering WorcesterMA United States Worcester Polytechnic Institute Department of Chemical Engineering WorcesterMA United States

ISBN: (纸本)9798350308600

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis. © 2023 IEEE.

关键词： Machine learning

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XAI approach to drug dosage optimization and review-based prediction of side effects 1

XAI approach to drug dosage optimization and review-based pr...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Oumlaz, Maroua Kamdar, Iram Oumlaz, Yassine Oukaira, Aziz Benelhaouare, Amrou Zyad Lakhssassi, Ahmed University of Québec in Outaouais Department of Engineering and Computer Science GatineauQCJ8X 3-7 Canada SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Faculty of Medicine and Pharmacy of Casablanca Casablanca Morocco

ISBN: (纸本)9798350351736

One of the major advancements in medicine is 'personalized medicine': a new approach with a promising future that takes into account the unique makeup of each individual patient instead of broad populations. One of the challenges of personalized medicine is the complexity: the more variables to account for, the more complex the adaptation of a certain treatment to a certain patient becomes;thus, the need for artificial intelligence: a tool that can be used to account for the various variables, and perform the computational and mathematical analyses required to succeed in the task of personalizing a certain treatment modality. This study explores explainable artificial intelligence (XAI) for drug dosage optimization based on individual patient data, utilizing advanced machine learning techniques: Decision Trees and Random Forests to be specific. Our approach applies XAI using ML models to provide clear and interpretable dosage recommendations, as well as predicting potential side effects. The methodology proves a high adaptability to diverse patient profiles, achieving an accuracy of 70% in dosage optimization. These results showcase the efficacy of XAI and the utilized algorithms in improving personalized treatment modalities, providing clinicians with a clearer vision, and understandable dosage recommendations for tailored treatment plans. © 2024 IEEE.

关键词： Personalized medicine

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Practical Phase Retrieval Using Double Deep Image Priors 21

Practical Phase Retrieval Using Double Deep Image Priors

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IS and T International Symposium on Electronic Imaging: 21st computational Imaging, COIMG 2023

作者： Zhuang, Zhong Yang, David Hofmann, Felix Barmherzig, David A. Sun, Ju Electrical & Computer Engineering University of Minnesota Minneapolis United States Department of Engineering Science University of Oxford Oxford United Kingdom Center for Computational Mathematics Flatiron Institute New York City United States Computer Science & Engineering University of Minnesota Minneapolis United States

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality. Our paper is also available at: https: // arxiv. org/ abs/ 2211. 00799 . © 2023, Society for Imaging science and Technology.

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A Comunication Framework for Compositional Generation

arXiv

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arXiv 2025年

作者： Elberg, Rafael Petrache, Mircea Parra, Denis Department of Computer Science PUC Chile Centro Nacional de Inteligencia Artificial CenIA Chile Department of Mathematics PUC Chile Institute for Mathematical and Computational Engineering PUC Chile

Compositionality and compositional generalization—the ability to understand novel combinations of known concepts—are central characteristics of human language and are hypothesized to be essential for human cognition. In machine learning, the emergence of this property has been studied in a communication game setting, where independent agents (a sender and a receiver) converge to a shared encoding policy from a set of states to a space of discrete messages, where the receiver can correctly reconstruct the states observed by the sender using only the sender’s messages. The use of communication games in generation tasks is still largely unexplored, with recent methods for compositional generation focusing mainly on the use of supervised guidance (either through class labels or text). In this work, we take the first steps to fill this gap, and we present a self-supervised generative communication game-based framework for creating compositional encodings in learned representations from pre-trained encoder-decoder models. In an Iterated Learning (IL) protocol involving a sender and a receiver, we apply alternating pressures for compression and diversity of encoded discrete messages, so that the protocol converges to an efficient but unambiguous encoding. Approximate message entropy regularization is used to favor compositional encodings. Our framework is based on rigorous justifications and proofs of defining and balancing the concepts of Eficiency, Unambiguity and Non-Holisticity in encoding. We test our method on the compositional image dataset Shapes3D, demonstrating robust performance in both reconstruction and compositionality metrics, surpassing other tested discrete message frameworks. © 2025, CC BY.

关键词： Encoding (symbols)

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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