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machine learning in Motion: Enhancing Human Activity Recognition with Smartphone Sensor Data and Performance Metrics 15

Machine Learning in Motion: Enhancing Human Activity Recogni...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Shrish Gagandeep Department of Mathematics Chandigarh University Mohali India

ISBN: (纸本)9798350370249

The goal of this undertaking course, is to enhance the efficiency and outcome performance in Human Activity Recognition (HAR) using smartphone sensor data and machine learning (ML) approaches. This field of study is located at the interface between pioneering computational techniques and practical technical implementations. The rising demand for high-performance, robust and user friendly HAR systems is the driver behind this initiative. The importance of such systems is increasing not only in the fields of medicine, training, and cities development but also aided by these areas. This investigation will have a primary goal for appraising many ML models. The models include different types of neural network architectures, including support vector machine (SVM), decision trees (DT), convolutional neural networks (CNN) and long short-term memory (LSTM). In training and validation procedures, the models were given datasets based on information from sensors in smartphones – accelerometers and gyroscopes. Precisely, the goal was to provide a precise identification and value of wide range human behaviors. One of the key components that make this course meaningful is its clear-cut inclination towards practical application in real world situation. To do this, giving priority to the accuracy of models is paramount while simultaneously taking into consideration parameters such as speed of processing and practical aspects when using most smartphones. For HAR tasks, CNN and LSTM achieve better results as opposed to other deep learning approaches. They have amazing ability to harness more or less characteristics of the vibrations, which naturally happen in sensor data. The findings from the studies show that deep learning approaches perform better than traditional ML models. ©2024 IEEE.

关键词： Convolutional neural networks

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Adversarial machine learning for Privacy-Preserving Space Situational Awareness 9

Adversarial Machine Learning for Privacy-Preserving Space Si...

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9th international conference on Mobile and Secure Services, MOBISECSERV 2024

作者： Chauhan, Neha Salinas, Sergio A. School of Computing Wichita State University WichitaKS United States

ISBN: (纸本)9798350390896

Satellite operators rely on space situational awareness (SSA) data to avoid collisions with other space objects. However, SSA data can also be used by adversaries to launch both kinetic and non-kinetic attacks against satellites. An existing approach to prevent these attacks consists in labeling a sensitive satellite as a debris in the SSA data. Its objective is to hide the satellite's true label from potential adversaries. Unfortunately, adversaries can easily determine the true label of a satellite based on the orbit information provided by the SSA system. In this paper, we propose a novel adversarial machine learning approach to conceal sensitive satellites in SSA data from potential adversaries. Our method conceals the satellite by changing both its label and the orbit information. The resulting SSA data entry cannot be used to determine that the space object has been mislabeled. Our experimental results show that our proposed method reduces the ability of an adversary to determine the true label a of a concealed satellite to less than 50%. © 2024 IEEE.

关键词： Adversarial machine learning

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Privacy-Preserving Trainer Recruitment in Model Marketplace of Federated learning

Privacy-Preserving Trainer Recruitment in Model Marketplace ...

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Pan, Yanghe Su, Zhou Wang, Yuntao School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350304602

Federated learning (FL) technologies enable trainers to collaboratively train machine learning (ML) models while maintaining data privacy, making them a crucial component of the next-generation model marketplace. However, several issues arise from the non-independent and identically distributed (non-IID) data among trainers, as well as the customers' diverse model orders. Consequently, it is necessary to design a trainer recruitment mechanism to select trainers that meet the customer's model requirements and improve the performance of purchased models. In this paper, we propose a privacy-preserving trainer recruitment scheme in a model marketplace of FL, which aims to recruit optimal trainers that meet the customer's requirements. Specifically, we propose a hierarchical recruitment mechanism to select trainers based on their task preferences, data distributions, and data sizes. Additionally, we prove the NP-hardness of the optimal trainer recruitment problem and propose a heuristic selection algorithm to provide an approximate solution. Extensive experiments demonstrate that our proposed scheme effectively improve the performance of purchased models, particularly in scenarios with highly non-IID data and limited budgets. © 2023 IEEE.

关键词： learning systems

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machine learning Algorithm for Predicting Soil Classification using Smart Agriculture 3

Machine Learning Algorithm for Predicting Soil Classificatio...

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3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Ponkumar, D. David Neels Kumar, Kolipaka Pushpa Krupa Himesh Ramesh, S. Kumar, J. Arun Nallasivan, G. Jayachandra, A. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The advent of Internet of Things (IoT) technology and data analytics has brought about a significant transformation in contemporary agricultural practices, sometimes referred to as smart agriculture. One of the fundamental uses of this technology is the prediction of soil classification, a critical factor in the optimization of crop management strategies and the allocation of resources. The present work investigates the use of machine learning techniques to achieve precise and efficient soil categorization in the context of smart agriculture. The study utilizes a comprehensive dataset consisting of several soil characteristics, including pH levels, moisture content, texture, and nutrient composition. These attributes were obtained via the use of Internet of Things (IoT) sensors and Unmanned Aerial Vehicles (UAVs). Several machine learning techniques, such as Random Forest, Support Vector machine, and Neural Networks, are assessed in terms of the classification accuracy in identifying soil types using the given characteristics. The findings indicate that machine learning models has the capability to accurately forecast soil categorization, hence enabling the use of precision agricultural techniques. These models assist farmers in making informed choices based on data analysis pertaining to crop selection, irrigation, and fertilization, resulting in enhanced agricultural productivity and the adoption of sustainable resource management practices. Additionally, this research investigates the interpretability of the chosen machine learning models, with the aim of ensuring that farmers are able to grasp and place faith in the forecasts. The proper implementation of smart agricultural systems also involves addressing ethical problems related to data privacy and security. This study makes a valuable contribution to the progress of sustainable agriculture via the use of machine learning and Internet of Things (IoT) technology. By leveraging these technologies, the research a

关键词： Soils

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Predicting Stress Levels in Sleep Patterns using machine learning Models and a Smart Yoga Pillow 7

Predicting Stress Levels in Sleep Patterns using Machine Lea...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Joshi, Kireet Mitra, Dipra Madaan, Vijay Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Amity University Department of Cse Jharkhand India

ISBN: (纸本)9798350350067

This research endeavours to predict stress levels in sleep patterns through the innovative utilization of a Smart Yoga Pillow (SaYoPillow) combined with machine learning models. In today's fast-paced lifestyle, stress management is paramount for overall well-being. However, people often overlook the significance of sleep in mitigating stress. The SaYoPillow is an innovative gadget that gathers many physiological measurements while a person is sleeping, such as the frequency of snoring, the rate of breathing, body temperature, the speed of limb movement, blood oxygen levels, eye movement, duration of sleep, and heart rate. Leveraging this data, our objective is to predict stress levels for the subsequent day, thereby materializing the concept of 'Smart-Sleeping'. The research addresses the gap in existing stress management methodologies by offering a proactive and personalized approach rooted in real-time physiological monitoring. Our findings demonstrate the efficacy of various machine learning models, with classifiers such as KNN, Logistic Regression, and Gaussian Naive Bayes achieving 100% accuracy in stress level prediction. The results underscore the potential of the SaYoPillow system in revolutionizing stress management practices, enabling individuals to make informed decisions to enhance their sleep quality and overall well-being. © 2024 IEEE.

关键词： Sleep research

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Optical Sensor for Water Bacteria Detection using machine learning 18

Optical Sensor for Water Bacteria Detection using Machine Le...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Chirchi, Vanajaroselin Chirchi, Emmanvelraj Khushi, E.C. Bairavi, S.M. Indu, K.S. The Oxford College of Engineering Dept. of Ise Bangalore India Don Bosco Institute of Technology Dept. of Ise Bengaluru India Dayanadasagar University Dept. of Cse Bengaluru India

ISBN: (纸本)9789380544519

The proposed method focuses on the detection of Escherichia coli bacterial contamination in water supplies. The proposed method uses a photonic-based sensor to examine photonic crystals in order to identify bacterial contamination in water. The system accurately predicts and diagnoses water contamination caused by bacteria by integrating an optical sensor with a graphical user interface (GUI). By attentively scrutinizing the early sensor-generated output graphs, we are able to discover variations in drainage patterns that are signaling the presence of bacteria in water samples. These graphs offer crucial insights into the unique characteristics of many samples, such as their refractive index. The sensor's sensitivity, specificity, and accuracy have all been thoroughly evaluated, and its results have been compared to those of established reference methods. A 95% accuracy rate is attained in the effective detection of the bacterium. These comparisons demonstrate the effectiveness of our AI-driven model and its intuitive graphical user interface (GUI), while also highlighting the potential of our machine learning techniques, such as Decision tree and Naive Bayes algorithm, for effective water bacterial detection. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy Bacteria Decision Tree machine learning algorithm Naïve Bayes Algorithm Sensitivity Sensor

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Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement learning 41

Revisiting Scalable Hessian Diagonal Approximations for Appl...

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41st international conference on machine learning, ICML 2024

作者： Elsayed, Mohamed Farrahi, Homayoon Dangel, Felix Rupam Mahmood, A. Department of Computing Science University of Alberta Edmonton Canada Alberta Machine Intelligence Institute Canada Vector Institute Toronto Canada CIFAR AI Canada

Second-order information is valuable for many applications but challenging to compute. Several works focus on computing or approximating Hessian diagonals, but even this simplification introduces significant additional costs compared to computing a gradient. In the absence of efficient exact computation schemes for Hessian diagonals, we revisit an early approximation scheme proposed by Becker and LeCun (1989, BL89), which has a cost similar to gradients and appears to have been overlooked by the community. We introduce HesScale, an improvement over BL89, which adds negligible extra computation. On small networks, we find that this improvement is of higher quality than all alternatives, even those with theoretical guarantees, such as unbiasedness, while being much cheaper to compute. We use this insight in reinforcement learning problems where small networks are used and demonstrate HesScale in second-order optimization and scaling the step-size parameter. In our experiments, HesScale optimizes faster than existing methods and improves stability through step-size scaling. These findings are promising for scaling second-order methods in larger models in the future. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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A Novel DL Structure for Brain Tumor Identification Using MRI Images 5

A Novel DL Structure for Brain Tumor Identification Using MR...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kollem, Sreedhar Harika, Pati Vignesh, Janagam Sairam, Peddoju Ramakanth, Adunuthula Peddakrishna, Samineni Samala, Srinivas Prasad, Ch. Rajendra Sr University Dept. of Ece Warangal India VIT-AP University School of Electronics Engineering Andhra Pradesh India

ISBN: (纸本)9798350383522

The multimodal MRI scans described in this article are used to categorize brain tumors based on their location and size. Brain tumors need to be categorized in order to assess the tumors and choose the appropriate course of treatment for each class. Many different imaging methods are used to detect brain tumors. However, because MRI does not use ionizing radiation and generates better images, it is commonly used. Using deep learning (DL), a branch of machine learning has recently demonstrated impressive results, particularly in segmentation and classifiable tasks. This paper proposes a convolutional neural network-based deep learning model (DL) that uses transfer learning and EfficientNet to classify various kinds of brain cancers using publically accessible datasets. The first divides cancers into three categories: glioma, meningioma, and pituitary tumor. Compared to conventional deep learning techniques, the suggested approach produces superior results. The Python platform can be used to complete the task. © 2024 IEEE.

关键词： Brain tumors CNN Deep learning Pre-processing Transfer learning

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Multiple disease prediction using machine learning algorithms 5th

Multiple disease prediction using machine learning algorithm...

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5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and computing, ICABEC 2023

作者： Dayal, Parth Sharma, Deepansh Agarwal, Aman Chaudhary, Himanshu Gautam, Ruchita Kumar, Praveen Sharma, Abhishek KIET Group of Institutions Delhi NCR UP Ghaziabad India

ISBN: (纸本)9781032642987

The prediction of the diseases was performed by taking different symptoms as input data from the user. In this paper, we have analyzed the presence of many diseases such as Heart diseases, CKD, Liver disease, and many more using basic body parameters such as blood sugar, heart rate, etc. We have applied the most used classification algorithms such as Logistic Regression, KNN, and Random Forest Classifier to anticipate the illness. Finally, the algorithm with the most accuracy is used to train the model. For the prediction of malaria and pneumonia, we have used Deep learning (CNN) which extracts important information, and patterns from the images, match it against a large dataset it already has, and makes the prediction. All the models have good accuracy and have been combined into a single website for easy access. © 2024 The Author(s).

关键词： Forecasting

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Software Startups Success Prediction: machine learning Methods of Comparative Analysis 1

Software Startups Success Prediction: Machine Learning Metho...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Gracy, J Anitha Parthasarathy, S. Hirthick, S. Nandhini, P. Applied Mathematics and Computational Science Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331518943

In recent years, there has been a significant rise in the number of software startups globally, driven by advances in technology and increasing reliance on digital solutions. These startups are crucial in shaping the future of industries. This study examines several machine learning models - Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and XGBoost - to predict the success of software startups based on key factors such as team dynamics and organizational characteristics. Using 473 data records and the top 19 features, the models were tested with an 80-20 train-test split, yielding strong performance in terms of accuracy, precision, recall, and F1 scores. Logistic Regression achieved the highest accuracy at 0.97, followed closely by KNN at 0.96. The findings reveal essential factors for startup success, such as team size, company age, and industry type, providing valuable insights for entrepreneurs, investors, and startup accelerators to adopt datadriven strategies that improve their chances of success. © 2024 IEEE.

关键词： Business Forecasting Data analysis machine learning software startups success prediction

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