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Diet and Workout Recommendation Using ML 2

Diet and Workout Recommendation Using ML

2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Sadhasivam, S. Sarvesvaran, M.S. Prasanth, P. Latha, L. Kumaraguru College of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350306811

Many individuals suffer from a variety of different types of ailments and illnesses in today's developed modern world where technology plays a significant role and has the opportunity to determine the majority of our everyday activities. It is believed that maintaining a healthy diet and exercise routine is crucial for keeping the body strong and fit, however it can be challenging to recommend a diet and exercise regimen fast because it depends on a number of different variables. The majority of people in today's world are extremely concerned with their health and fitness and alternate between needing to lose weight, gain weight, or stay healthy. Therefore, we suggest a diet and exercise recommendation system that offers a list of foods and exercise suggestions based on user inputs like height, weight, and age used to determine their BMI. Along with other pertinent information including proteins, fats, calcium, iron, potassium, sodium, sugar, vitamin D, carbohydrates, and fibre, various nutrients and calories are taken into account. Several food items, including popular vegetarian and non-vegetarian cuisines, are taken into account. The three main ingredients that are taken into consideration when preparing food are nutrients, fats, and carbohydrates. And utilising these food items, we compute the number of calories consumed and the number of calories needed to be expended to maintain their fitness so that later workout recommendations can be made. © 2023 IEEE.

关键词： Machine learning

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Credit Card Fraud Detection using Machine Learning 14

Credit Card Fraud Detection using Machine Learning

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sushma, V. Neelamma, Shriya Machaiah, Yathin Fathima, Suroor ATME College of Engineering Computer Science Department Mysuru India

The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this process we mined the transaction of each data and it finally predict the fraud detection of data. © Grenze Scientific Society, 2023.

关键词： Machine learning

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Domain Adaptation based on Attention-Weighted Optimal Transport and Cluster Alignment 4

Domain Adaptation based on Attention-Weighted Optimal Transp...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhu, Yanan Qi, Yangyang College of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350326444

Optimal transport (OT) as an efficient way to measure differences in distribution. provide new ideas for solving the unsupervised domain adaptation (UDA) problem. However, existing work based on OT ignores the multi-modal structure of the domain and does not adequately reflect the true data distribution. Therefore, this paper proposes a new deep UDA framework, namely attention-weighted optimal transport and cluster alignment (AWOT). Specifically, the model first proposes a weighting strategy based on a self-attentive mechanism to reduce the bias caused by mini-batch selection in training. The strategy calculates weights by correlating the iterative prediction results of the source and target domains to weight the optimal in-transmission cost function. A confidence thresholding technique is also proposed to filter out low confidence target samples to enhance the robustness of pseudo-label. Moreover, AWOT is also equipped with a clustering centroid alignment loss to learn discriminative features. Comprehensive experiments have shown that AWOT outperforms existing state-of-the-art methods on several domain adaptation benchmarks. © 2023 IEEE.

关键词： Alignment

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Two-Stream Deep Learning Architecture-Based Human Action Recognition

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computers, Materials & Continua 2023年第3期74卷 5931-5949页

作者： Faheem Shehzad Muhammad Attique Khan Muhammad Asfand E.Yar Muhammad Sharif Majed Alhaisoni Usman Tariq Arnab Majumdar Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah CampusPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science Bahria UniversityIslamabadPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai50200Thailand

Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer *** breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture *** HAR techniques are introduced in the ***,the challenge of redundant and irrelevant features reduces recognition *** also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among *** this work,a deep learning and improved whale optimization algorithm based framework is proposed for *** proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final *** pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition *** fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best *** best selected features are finally classified usingmachine learning(ML)*** publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%*** with state of the art techniques(SOTA),the proposed method showed the improved accuracy.

关键词： Human action recognition deep learning transfer learning fusion of multiple features features optimization

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MCARNet: Mixed Convolution-Attention-Residual Network for Hyperspectral Image Classification of Cholangiocarcinoma 12

MCARNet: Mixed Convolution-Attention-Residual Network for Hy...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Xue, Jiaqi He, Wangquan Ye, Songxin Liang, Hong Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9798400707988

Hyperspectral is a well-established form of data in the medical field and plays an irreplaceable role, reflecting not only the size, shape and structure of the target pathological areas, but also the differences in spectral between the chemical composition of the targets. Therefore, hyperspectral image(HSI) can provide more abundant information for medical practitioners when performing pathology analysis. In recent years, while two powerful representational learning methods, which have been fully developed in the long term in deep learning, namely convolution and attention mechanism, have both made significant contributions to the pathological analysis of hyperspectral medical images. However, they have also revealed respective shortcomings in previous works, such as insufficient support of the biological basis of convolution and its weak ability to capture global information, the insufficient ability of attention mechanism to acquire spatial texture features, and both have over-reliance on sample quantity as well as quality. Therefore, this article proposes a Mixed Convolution-Attention-Residual(MCAR) model and a corresponding MCARNet network for the HSI classification task of cholangiocarcinoma by effectively combining multi-branch convolution, attention mechanism and residual structure. We also effectively validate that the proposed model can perform better in the task by parallel extraction of global with local features as well as the construction of residual structure under the condition of few training samples. © 2023 ACM.

关键词： Convolution

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A Single Residual Network with ESA Modules and Distillation

A Single Residual Network with ESA Modules and Distillation

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yucong Cai, Minjie Hunan University College of Computer Science and Electronic Engineering China

ISBN: (纸本)9798350302493

Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent studies have found that simply relying on reducing parameters or reducing the theoretical FLOPs of the model does not speed up the inference time of the network in a practical sense. Actual speed on the device is probably a better measure than FLOPs. In this work, we propose a new single residual network (SRN). On the one hand, we try to introduce and optimize an attention mechanism module to improve the performance of the network with a relatively small speed loss. On the other hand, we find that residuals in residual blocks do not have a positive impact on networks with adjusted ESA. Therefore, the residual of the network residual block is removed, which not only improves the speed of the network, but also improves the performance of the network. Finally, we reduced the number of channels and the number of residual blocks of the classic model EDSR, and removed the last convolution before the long residual. We set this tuned EDSR as the teacher model and our newly proposed SRN as the student model. Under the joint effect of the original loss and the distillation loss, the performance of the network can be improved without losing the inference time. Combining the above strategies, our proposed model runs much faster than similarly performing models. As an example, we built a Fast and Efficient Network (SRN) and its small version SRN-S, which run 30%-37% faster than the state-of-the-art EISR model: a paper champion RLFN. Furthermore, the shallow version of SRN-S achieves the second-shortest inference time as well as the second-smallest number of activations in the NTIRE2023 challenge. Code will be available at https://***/wnxbwyc/SRN. © 2023 IEEE.

关键词： Deep learning

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A Review on Food Waste Management and Donation App 14

A Review on Food Waste Management and Donation App

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Raghuram, A.S. Aishwarya, R.K. Kulkarni, Anup G. Calvyn, Asher A. Jathin, C.S. Computer Science Department ATME College of Engineering Mysuru India

A drastic increase can be seen in food waste. as per data given by food and agriculture organization, 1/3rd of food produced for human consumption is wasted globally, which accounts for almost 1.3 billion tons per year. on the other hand, also as per who 20% of the population face extreme food shortages. hence there is a need to come up with a solution that can avoid food waste & can help feed the needy. The web-based food waste management system can assist in collecting the leftover food from hotels, restaurants, marriage halls to distribute among those in need. ngos that are helping poor communities to battle against starvation & malnutrition can raise a request for food supply from restaurants through this application. once the request is accepted, the ngos and it’s volunteers can collect the food from the restaurants for its distribution. in this way this web-based food waste management and donation system will help restaurants to reduce food waste and will help in feeding the poor and needy people. © Grenze Scientific Society, 2023.

关键词： Food supply

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Exploring the Importance of Security in Mobile Health (mHealth) Data Management 1

Exploring the Importance of Security in Mobile Health (mHeal...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Kulkarni, Mukund Kashinath, Amruta Garagate Momin Irshad, Anjuman Sekhar, G Chandra Patil, P.B. Phatak, Pratiksha Institute of Management Kolhapur India Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Krishna Vishwa Vidyapeeth Deemed to Be University Department of Anaesthesiology Karad India Jaywant College of Engineering and Polytechnic Department of Electrical Engineering Karad India

ISBN: (纸本)9798350360707

Clinical the healthcare and awareness towards it is now most prominent among the living beings in the world. The mobile health and healthcare are now increasing among the populations over the worlds. Thus, the focus of study is to know the importance of mobile healthcare and its data management, storage and security. The utilization of IoT gadgets and mHealth apps is only two examples of how digital medicine is becoming more and more common in daily medical practice. It has the ability to promote improved patient health outcomes, facilitate care coordination, and increase communication with lower costs. The security to healthcare helps to protect the health-related data from the hackers by providing high security to the devices and the applications. Thus, with time hacking will be getting more intense and will provide more damage so it is necessary to improve the security and data management through mHealth. © 2024 IEEE.

关键词： Electronic health record

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An Efficient Virtual Channel Scheme Based on Multi-hop Payment Channel 10

An Efficient Virtual Channel Scheme Based on Multi-hop Payme...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Li, Feng Liu, Yaqin Liang, Wei Xie, Songyou Yang, Yang Li, Yuhui Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350312461

The transaction throughput and confirmation time of the current mainstream blockchain cryptocurrency platform are far lower than those of traditional centralized trading platforms, which is difficult to meet the growing needs of users. Therefore, this paper proposes an efficient multi-hop off-chain Virtual Channel (VC) payment scheme to improve the confirmation speed of transactions by performing high-frequency and small transactions off-chain. The proposed scheme constructs a virtual channel scheme with dynamic selection [1], [2] through Hash Time-Lock Contract (HTLC) and constant collateral, and closes the virtual channel through on-chain settlement to ensure the balance security and atomicity of transactions. The proposed VC scheme is suitable for multi-hop Payment Channel (PC) networks, after the VC construction is completed, the transaction initiator and receiver directly conduct transactions through the virtual channel, ensuring the privacy of the transaction. Experimental analysis shows that the proposed scheme can resist a variety of malicious attacks and realize a rapid and safe transaction process. © 2023 IEEE.

关键词： Network security

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security 2

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vivekanandan, Karthi Kaleeswari, B. Natchiammai, P.L. Anthonisamy, Arun Vijayabharathi, R. Alagarsamy, Manjunathan Builders Engineering College Department of Electrical and Electronics Engineering Tirupur India Department of Electronics and Communication Engineering Chennai India Mookambigai College of Engineering Department of Electronics and Communication Engineering Pudukkottai India Panimalar Engineering College Department of Computer Science and Business System Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices' energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security. © 2024 IEEE.

关键词： Smart manufacturing

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