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Nirnayaak: Revolutionizing Legal Research 4

Nirnayaak: Revolutionizing Legal Research

引用

4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Datta, Soumyajit Chakraborty, Arya Bhattacharya, Saptarshi Samui, Amit Chakraborty, Anurag Dasgupta, Kousik Kalyani Government Engineering College Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350389128

Nirnayaak is a novel and advanced legal software application, that is meticulously designed to expedite the retrieval of crucial legal information in an Indian Context, addressing the challenges posed by a growing caseload and inaccuracies in the legal system. Employing a groundbreaking architectural framework, it leverages OCR, Smart Text preprocessing, keyword extraction, a sophisticated LLM-based summarization and information retrieval and a feedback-based keyword ranking technique. This amalgamation of techniques enhances the accuracy and accessibility of legal data, enabling legal professionals, scholars, and the public to access pertinent information seamlessly just using natural language queries. We conducted benchmarks on popular keyword extraction techniques, demonstrating that our preprocessing techniques enhance their performance. Additionally, we evaluated LLMs to ascertain their proficiency in understanding contextual nuances. © 2024 IEEE.

关键词： Application programs

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Ontology-Driven Reinforcement Learning for Personalized Student Support

Ontology-Driven Reinforcement Learning for Personalized Stud...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hare, Ryan Tang, Ying Rowan University Dept. of Electrical and Computer Engineering GlassboroNJ United States

ISBN: (纸本)9781665410205

In the search for more effective education, there is a widespread effort to develop better approaches to personalize student education. Unassisted, educators often do not have time or resources to personally support every student in a given classroom. Motivated by this issue, and by recent advancements in artificial intelligence, this paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system such as a serious game or an intelligent tutoring system. To fit any educational situation, we apply ontologies for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of a Brain Controlled Robotic Arm: Initial Experimental Testing 21

Design of a Brain Controlled Robotic Arm: Initial Experiment...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Aljedaani, Lama Taha Abdelhedi, Fatma Aldahasi, Renad Abdullah Batheeb, Amal Abdullah Engineering King Abdulaziz University Dept. of Electrical and Computer Jeddah Saudi Arabia

ISBN: (纸本)9798350374131

In recent years, a significant leap has been made in various techniques to control robotics to assist people with upper limb amputations. Individuals with limb amputations have been facing significant challenges in their daily lives, where the simple and daily tasks become much more complex and take longer time, or even impossible, without depending on other people. Furthermore, traditional assistive devices often require significant physical effort, and they may be unable to perform different tasks efficiently, thus restricting the amputee's independence. Consequently, there is a need for an advanced assistive device to provide greater control and functionality to improve upper limb amputee independence. The Brain-Controlled Robotic Arm project aims to develop an advanced assistive device that allows individuals with disabilities to control a robotic arm using their brain signals. The project applies brain-computer interface technology (BCI) that allows the human brain to interact with a digital computer through electroencephalogram (EEG) technology. In this project, the robotic arm utilizes an electroencephalogram (EEG) to capture brain signals and then process those signals to get the required signal in order to control the robotic arm. © 2024 IEEE.

关键词： Brain computer interface

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Pandya, Kushagra Hathi, Heli Ravikumar, R.N. Nadarajan, Sivakumar Praveen, Manoj Pal, Vinod Kumar Tss Consultancy Pvt. Ltd. Rajkot India Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Velalar College of Engineering and Technology Dept. of Ai & Ds Erode India Marwadi University Dept. of Computer Science Rajkot India

ISBN: (纸本)9798350379716

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance. © 2024 IEEE.

关键词： Contrastive Learning

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BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Semantic Segmentation of X-Ray Images of Human Hand Bone 3

BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Seman...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Venkatesh Nagaraju, Y. Darshan, D. Sahanashree, K.J. Nagamani, P.N. Basapur, Satish B University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

Multiclass segmentation of hand bones in X-ray images is crucial for various medical applications, including diagnostic assistance and surgical planning. This study presents BoneSegNet, a deep learning-based segmentation model designed to classify hand bones into seven distinct anatomical classes: Distal phalanges, Intermediate phalanges, Proximal phalanges, Metacarpals, Carpals, Ulna, and Radius. Our model, BoneSegNet leverages the strengths of both convolutional neural networks (CNNs) and transformer encoders incorporating the UNet architecture. Our model architecture begins with patch and positional embeddings of the input image, followed by a series of transformer encoder layers to capture long-range dependencies and contextual information. The transformer encoder is equipped with multi-head attention mechanisms and multi-layer perceptron's (MLPs) with GELU activation and dropout for regularization. Skip connections at specific layers are stored and later utilized in the decoder. The decoding path consists of a series of deconvolution and convolution blocks designed to progressively upscale the feature maps while incorporating skip connections from the encoder. This hierarchical structure ensures detailed spatial information is preserved and enhanced. The final output layer employs a 1x1 convolution with a sigmoid activation function to produce the multiclass segmentation map. The model is trained and evaluated on a dataset of hand X-ray images. It achieves an average accuracy of 99.25%, an average Dice score of 0.81, an average mIoU score of 0.70, sensitivity of 0.79, and specificity of 0.997. These results demonstrate the model's effectiveness in accurately segmenting and classifying hand bones, making it a valuable tool for medical professionals. © 2024 IEEE.

关键词： Semantic Segmentation

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Prospects and Challenges of Inertia Emulation Methods for Low Inertia Power Systems 4

Prospects and Challenges of Inertia Emulation Methods for Lo...

引用

4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Umar, Muhammad F. Nazari, Amirhosein Gohari Shadmand, Mohammad B. University of Illinois Dept. of Electrical & Computer Engineering ChicagoIL United States

ISBN: (纸本)9798350306262

Due to massive incorporation of the renewable energy resources into the upcoming power grid, the share of power generation from synchronous generator (SG) is being decreased and is being replaced by distribution generation (DG). This change has led the significant decrease in the overall power system's inertia and transforming the future power system into low inertia power systems (LIPS). Because of the huge rotational mass of SG, it intrinsically provides system inertia that is critical for the stable and resilient operation of the power system under different types of disturbances. To cope with the challenges of stable operation of LIPS, several inertia emulation schemes has been proposed in the previous literature. However, each scheme has its specific benefits, limitations, and applications. Thus, this paper provides a comprehensive insight to the inertia emulation schemes for DG and compares each scheme in terms of implementation challenges and experimental effectiveness in enhancing the overall system's inertia. Moreover, this comparison is supported with experimental results and analysis to validate virtual inertia emulation schemes. © 2024 IEEE.

关键词： Renewable energy

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IMU-Based Model for Precise Toothbrush Stroke and Orientation Tracking

IMU-Based Model for Precise Toothbrush Stroke and Orientatio...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Islam Bhuyan, Mohammad Ariful Uddin, Md Mayen Kim, Hyeon Cheol University of Ulsan Dept. of Electrical Electronic and Computer Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350350821

This paper presents a model designed for precise orientation estimation and stroke distance tracking of toothbrush activity. The model leverages Inertial Measurement Unit (IMU) technology, which includes accelerometers and gyroscopes, to capture detailed motion data during the activity. The proposed Corrective Fusion Filter Technique (CFFT) helps integrate accelerometer and gyroscope data with several corrective steps to estimate orientation precisely, and the combined sensor data accurately measures stroke distance. This filtering method also minimizes sensor noise and drifting issues, enhancing the model's reliability and performance. © 2024 IEEE.

关键词： Accelerometers

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Multi-Objective Phase Shift Control Optimization for Dual Active Bridge Converters

Multi-Objective Phase Shift Control Optimization for Dual Ac...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Davila, Antonella Haro Hasan, Syed Imam Uzum, Alper Elrayyah, Ali Sozer, Yilmaz The University of Akron Dept. of Electrical and Computer Engineering AkronOH United States

ISBN: (纸本)9798350376067

This comprehensive analysis evaluates multiobjective optimization of the phase shift modulation techniques to evaluate the optimized phase shift at various operating points for a Dual Active Bridge converter. By simulating scenarios to understand the behavior of a system under various conditions for different modulation techniques, this study provides insights into different outcomes to optimize the system and identify the most effective phase-shift combinations. The system's performance is assessed based on two key metrics: the inductor RMS current and Zero Voltage Switching (ZVS) utilization. The analysis focuses on predicting the RMS inductor current for various phase-shift modulations, including Single-Phase Shift, Dual Phase Shift, Extended Phase Shift, and Triple Phase Shift modulation. Besides, peak current is crucial for assessing ZVS conditions, facilitating the optimization of the final set of phase-shift modulations. © 2024 IEEE.

关键词： Zero voltage switching

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Kinematic Cycle Consistency Using Wearables and RF Data for Improved Human Skeleton Estimation 58

Kinematic Cycle Consistency Using Wearables and RF Data for ...

引用

58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Salehin, Sultanus Kearney, Sean Gurbuz, Z. The University of Alabama Dept. of Electrical & Computer Engineering TuscaloosaAL United States

ISBN: (纸本)9798350354058

Radar-based health monitoring has the potential to enable 24/7 in-home ambient sensing to increase accessibility to care in a privacy-protective, low-cost fashion. This paper focuses on the assessment of mobility and gait for early warning, post-treatment monitoring, and emergency response to health conditions affecting human movement. Current technology has focused on wearables for monitoring personalized health, but wearables are not effective if not worn. In this case radar can provide a backup source of health information, that can be utilized standalone or in conjunction with the information provided by a wearable. This paper explores the estimation of gait parameters and human pose (i.e. skeleton) using RF sensing alone as well as how wearables can be utilized with RF to improve estimation accuracy. In particular, we propose the concept of kinematic cycle consistency using RF and wearable-estimated kinematics to improve skeleton estimation accuracy. Our results are validated using a gold standard Vicon optical tracking system. © 2024 IEEE.

关键词： Musculoskeletal system

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Improving Timing Quality Through Net Topology Optimization in Global Routing 37

Improving Timing Quality Through Net Topology Optimization i...

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Yang, Jayoung Kim, Taewhan Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350377569

Net topology construction is a major task in global routing of physical design since its outcome impacts the overall performance of the chips. The conventional global routers optimize the net topology considering wirelength, congestion, and timing. In this work, we propose a systematic and practical net topology refinement method to reinforce the timing robustness throughout the whole design process. Precisely, we develop a couple of effective techniques of net topology refinement that can be tightly linked to any of existing commercial and academic global routing tools: (1) a fast net topology exploration and (2) a parallel acceleration of net topology tuning. Through experiments, it is shown that our proposed technique is able to considerably enhance the timing metrics with no degradation in power consumption and wirelength. © 2024 IEEE.

关键词： Topology

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