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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning-Based Wine Quality Prediction 2

Machine Learning-Based Wine Quality Prediction

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Dhondiyal, Shiv Ashish Kumar, Rohit Verma, Himanshu Dhyani, Ashish Singh, Sumeshwar Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India GNIOT Institute of Professional Studies Greater Noida India Dept. of Hospitality Management Graphic Era Deemed to be University India Dept. of Computer Science and Engineering Jaypee Institute of information technology sector 62 India

ISBN: (纸本)9798350383867

The main goal of this attempt is to assess the quality of wine and determine if it is excellent or terrible. Humans have been engaging in the act of tasting for generations and have consistently made predictions using their sensory organs. A number of industries have recently embraced new technology and used it to a variety of uses. However, human knowledge is required in a number of businesses, such as product quality assurance. With the increasing popularity of the product, modern innovations have increased the cost of the treatment. To ensure the quality of the output, this study looks at a number of machines learning algorithms, including Random Forest Classification, Decision Tree classifiers, and Support Vector Machines (SVM). These methods make use of the product's current attributes to simplify the quality control procedure. © 2024 IEEE.

关键词： Adversarial machine learning

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REAL-TIME DETECTION OF NEGATIVE ROAD OBJECTS FOR AUTONOMOUS VEHICLES TO IMPROVE SAFETY

REAL-TIME DETECTION OF NEGATIVE ROAD OBJECTS FOR AUTONOMOUS ...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Ahmed, Yousif Hag Li, Wenyao Anwar, Sohel Dept. of Computer and Information Science Purdue University IndianapolisIN United States Dept. of Mechanical Engineering Purdue University IndianapolisIN United States

ISBN: (纸本)9780791888636

This paper presents the preliminary results of our investigation in detecting negative road objects such as potholes and cracks in real-time for autonomous vehicles in order to improve safety. It is important to not only detect the existence of the negative road object from a safe distance, but also to identify the size, dept., and shape of such objects which plays a critical role in determining if the object must be avoided for safety or it can be ignored. We utilized open-source image databases such as ROBOFLOW (pothole) and RDD2020 of negative road objects for training a single layer convolutional neural network (CNN), such as YOLO. To test the trained algorithm, a small data set taken on the city streets in Indianapolis, IN was used in order to evaluate the accuracy of detection of the negative road objects. While the initial results show the ability of detecting the negative road objects through the proposed algorithm, more work needs to be done to improve the overall accuracy of detection with respect to size and shape of these negative road objects. Copyright © 2024 by ASME.

关键词： Convolutional neural networks

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Evaluating the Performance of Machine Learning Models in Handwritten Signature Verification

Evaluating the Performance of Machine Learning Models in Han...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Chakma, Rishita Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Handwritten signatures remain a widely used method for personal authentication in various official documents, including bank checks and legal papers. The verification process is often labor-intensive and time-consuming, necessitating the development of efficient methods. This study evaluates the performance of machine learning models in handwritten signature verification using the ICDAR 2011 Signature and CEDAR datasets. The investigation involves preprocessing, feature extraction using CNN architectures, and optimization techniques. The most effective models undergo a rigorous evaluation process, followed by classification using supervised ML algorithms, such as linear SVM, random forest, logistic regression, and polynomial SVM. The results indicate that the VGG16 architecture, optimized with the Adam optimizer, achieves the satisfactory performance metrics. This study demonstrates the potential of ML methodologies to enhance the efficiency and accuracy of signature verification, offering a robust solution for document authentication. © 2024 IEEE.

关键词： Decision trees

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Wearable Sensor-Based Gait Analysis: Advanced Feature Extraction Techniques 2

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Karthik Salian, Preethi K. Ranjith, H.D. Salian, Supriya Jayashree Shetty, K. Annapoorneshwari NMAM Institute of Technology Udupi Nitte India NMAM Institute of Technology Udupi Nitte India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Mangalore India

ISBN: (纸本)9798350383867

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-dept. analysis, enabling effective feature extraction for comprehensive gait assessment. © 2024 IEEE.

关键词： Accelerometers

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Real-time Arm Motion Tracking and Hand Gesture Recognition Based on a Single Inertial Measurement Unit 11

Real-time Arm Motion Tracking and Hand Gesture Recognition B...

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11th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2024

作者： Chang, Tien-Chiao Wu, Yu-Chi Han, Chin-Chuan Chang, Chao-Shu National United University Dept. of Electrical Engineering Miao-Li Taiwan National United University Dept. of Computer Science and Information Engineering Miao-Li Taiwan National United University Dept. of Information Management Miao-Li Taiwan

ISBN: (纸本)9798350366501

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. This paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial Measurement Unit) sensor embedded in a wristband. This approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow's IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist's pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. This model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. The angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%. © 2024 IEEE.

关键词： Gesture recognition

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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model 14

Stock Market Prediction Using Genetic Algorithm Assisted LST...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nikhil, Shreyas Sah, Rahul Kumar Kumar Parki, Santosh Tamang, Til Bikram Somashekhara Reddy, D. Mahesh, T.R. Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values. © 2023 IEEE.

关键词： Genetic algorithms

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Smart City Surveillance: Survey on Current Status and Future Trends 14

Smart City Surveillance: Survey on Current Status and Future...

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

作者： Neyaz, Hera Paul, Hrituparna Srivastava, Anima Singh, Shiksha Dept. of Computer Science and Engineering Shambhunath Institute of Engineering and Technology India

This paper presents a comprehensive survey on surveillance and security aspects of a smart city. Multiple dimensions of a smart city surveillance system have been discussed. The current trends and methodologies are thoroughly researched and presented. The algorithms of novel methods are reviewed along with analytics and methodologies. Impact of XAI in the smart city surveillance is discussed. Advantages and disadvantages of current infrastructures have been included in the broad literature survey. Lastly, the challenges and future trends are conferred to. © Grenze Scientific Society, 2023.

关键词： Smart city

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Physical-Design Aware Scan Cell Reordering for Low Power Testing

Physical-Design Aware Scan Cell Reordering for Low Power Tes...

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2024 IEEE East-West Design and Test Symposium, EWDTS 2024

作者： Badjatya, Rohit Vinay Kumar, Rahul Tudu, Jaynarayan T Fujita, Masahiro Singh, Virendra Indian Institute of Technology Bombay Dept. of Electrical Engineering Mumbai India Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Tirupati India University of Tokyo Dept. of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798331515768

Scan chain architecture is widely employed in modern VLSI design for test applications. However, it often leads to high power consumption during testing. The architecture experiences elevated simultaneous switching activity due to non-functional input patterns applied during test time, resulting in significant peak power in the circuits. This increased power consumption can cause IR drop issues, negatively impacting the chip's yield. This paper proposes an innovative approach to reduce both peak power and routing length by effectively reordering the scan chains. Experimental results show that the proposed approach is effective for the ISCAS'89 and ITC'99 benchmark circuits and this method achieves lower peak power and routing length compared to the state of the art. © 2024 IEEE.

关键词： low power testing peak power routing constraint scan chain reordering voltage drop

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