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Utilization of MFCC in Conjuction with Elaborated LSTM Architechtures for the Amplification of Lexical Descrimination in Acoustic Milieus Characterized by Pronounced Nocturnal Disturbance

Utilization of MFCC in Conjuction with Elaborated LSTM Archi...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Ramesh Singh Rawat Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

In this pioneering research endeavor, we delved into the intricate realm of speech recognition technology, aiming to surmount the formidable challenges posed by acoustically complex environments, notably those marred by pronounced nocturnal disturbance. Through the synergistic amalgamation of Mel-frequency cepstral coefficients (MFCC) and meticulously crafted Long Short-Term Memory (LSTM) architectures, we charted a transformative path toward the amplification of lexical discrimination. Our multifaceted approach not only enhanced the discernment of subtle linguistic nuances but also showcased unparalleled efficacy in the face of disruptive nocturnal noise. The meticulously calibrated fusion of advanced signal processing techniques and neural network architectures culminated in a novel paradigm, revolutionizing the landscape of speech recognition technology. By pushing the boundaries of computational linguistics, this study not only advances scientific understanding but also paves the way for real-world applications in domains reliant on precise and resilient speech recognition systems.

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Mridul Gupta Devansh Yadav Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life.

关键词： Measurement Deep learning Accuracy Computed tomography Classification algorithms

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Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation

Deep Learning Driven Multilingual System for Recognition and...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： V. R. Azhaguramyaa J. Janet Hariharan A Kamaleshwaran A Albert Joseph A Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu Dept. of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term “deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation” describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-dept. accounts of the recognized therapeutic herbs.

关键词： Deep learning Tongue Flora Decision making Medical services Linguistics Mathematics

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Efficient mmWave Beam Selection using ViTs and GVEC: GPS-based Virtual Environment Capture 14

Efficient mmWave Beam Selection using ViTs and GVEC: GPS-bas...

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

作者： Gupta, Srajan Saxena, Navrati Roy, Abhishek Ravi, Akshay San Jose State University Dept. of Computer Science San Jose United States Mediatek Inc. Engineering San Jose United States

ISBN: (纸本)9798350335095

Millimeter waves (mmWaves) providing higher bandwidth is used by 5G network technology to achieve higher network capacity and faster data transfer. However, the process of beam sweeping across multiple antenna arrays can be time-consuming and inefficient. Machine learning (ML) models can address this issue by predicting the optimal beam pair based on data from on-vehicle light detection and ranging (LIDAR) and global positioning system (GPS) sensors. This paper proposes a new vision transformer (ViT) ML model for beam selection using GPS and LIDAR data. GPS-based virtual environment capture (GVEC) has been introduced to overcome to overcome noise in the LIDAR data caused by adverse weather conditions. The proposed solution demonstrates improved performance compared to previous approaches when tested on noisy LIDAR data, achieving a 92% accuracy in searching among the top 10 beams. © 2023 IEEE.

关键词： Millimeter waves

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The ORIS tool: App, library, and toolkit for quantitative evaluation of non-Markovian systems

The ORIS tool: App, library, and toolkit for quantitative ev...

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作者： Carnevali, Laura Paolieri, Marco Vicario, Enrico Information Engineering Dept. Univ. Of Florence Italy Computer Science Dept. Univ. Of Southern California United States

ORIS is a tool for quantitative modeling and evaluation of concurrent systems with non-Markovian durations. It provides a Graphical User Interface (GUI) for model specification as Stochastic Time Petri Nets (STPNs), validation by interactive simulation, and evaluation by several techniques, computing instantaneous and cumulative rewards. It also provides an open-source Java Application Programming Interface (API) to automate the workflow, and it can be used as a toolkit for derivation and evaluation of STPNs in model driven engineering. As distinguishing features, ORIS implements transient and steady-state analysis of STPNs with underlying Markov Regenerative Process (MRP), and transient analysis of STPNs with underlying Generalized Semi-Markov Process (GSMP). It also implements nondeterministic analysis of Time Petri Nets (TPNs), simulation of STPNs, and solution methods for Continuous-Time Markov Chains (CTMCs) and MRPs with at most one non-exponential timer in each state. The well-engineered software architecture of ORIS supports agile implementation of new STPN features, new modeling formalisms, and new analysis methods. © 2022 is held by the owner/author(s).

关键词： Stochastic systems

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Single Object Tracking in Satellite Videos Through Deep Learning and Motion Detection Techniques

Single Object Tracking in Satellite Videos Through Deep Lear...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Israa Hayder Fattah Alizadeh Dept. of Computer Science and Engineering University of Kurdistan Hawlere Erbil Iraq Dept. of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Single object tracking in satellite videos has recently gained a lot of attention in the field of computer vision. Although the satellite images are very informative, the small size of the objects and limited spatial and temporal resolution might make object tracking difficult and inaccurate. In this paper, we propose a method for accurately tracking small objects in satellite videos. In the initial phase, we applied the Fast Super-Resolution Convolutional Neural Network (FSRCNN), a deep learning-based super-resolution technique, to enhance the resolution of the frames. This procedure serves to sharpen the details and features of the objects, resulting in a clearer representation. The main contribution, however, lies in our incorporation of a robust motion detection technique into our framework, marking a significant step forward in this study. The motion detection approach combines various methods and operations with the primary goal of making moving objects distinctly visible and easily distinguishable from the complex image background. This enhancement significantly contributes to the precision and accuracy of the tracking system. We combined both approaches with Fully Convolutional Siamese Network (SiamFC) due to its robustness in tracking processes across frames in videos, capacity to handle changes in target appearance, and ability to deal with small datasets. The Satellite Video Single Object Tracking (SatSOT) and SV248A10-SOT (SV248S), two satellite video single object tracking datasets, were used to evaluate the proposed approach. When the techniques were applied, the SatSOT dataset showed an 11% precision score increase and a 10% success rate improvement. The SV248S dataset exhibited even more significant improvements, with precision scores increasing by 18% and success scores rising by 9%.

关键词： computer vision Satellites Target tracking Superresolution Motion detection Satellite images Object tracking

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Naveenkumar Jayakumar Netra Patil Roshankumar S. Pokal Rudra Pratap Dev Sidharth Nair School of Computer Science and Engineering Vellore Institute of Technology Vellore Dept. of Computer Engineering College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges.

关键词： Analytical models Accuracy Recurrent neural networks Collaborative filtering Heuristic algorithms Resumes Feature extraction Security Recommender systems Text processing

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3D-CAN: A 3D Convolution Attention Network for Feature Extraction and Classification of Hyperspectral Images

3D-CAN: A 3D Convolution Attention Network for Feature Extra...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Murali Kanthi K. Venkateshwara Rao L. Chandra Sekhar Reddy T. Hitendra Sarma Nuthanakanti Bhaskar Nam Vasundhara Dept. of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad Telangana India Dept. of Computer Science & Engineering (Data Science) CMR College of Engineering & Technology Engineering Hyderabad Telangana Dept. of Information Technology Vasavi College of Engineering Hyderabad Telangana India Dept. of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana India

In recent years, the deep learning method has gained widespread popularity in hyperspectral image classification (HSI) tasks because of remarkable feature extraction capability. However, conventional deep learning approaches often yield suboptimal classification performance due to the limited discrimination ability of the extracted features. Moreover, the challenge of obtaining high classification performance with a limited number of samples in hyperspectral images (HSIs) has emerged as a prominent research area of interest. To address this concern, this letter provides a novel 3D convolution attention network (3D-CAN) for feature extraction and HSIs classification. The attention mechanism employed in this context enables capturing both the long-range spatial dependencies of HSIs in the horizontal and vertical directions and the varying significance levels across distinct spectral channels. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy.

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Performance Analysis of Slicing on a 10-node 5G Architecture for Networked Music Performances

Performance Analysis of Slicing on a 10-node 5G Architecture...

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IEEE Symposium on computers and Communications (ISCC)

作者： Luca Turchet Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Networked Music Performances (NMPs), where geographically displaced musicians play together over a data network, represent a challenging application for today’s wireless communications. This is due to the stringent constraints on latency, throughput, and reliability that need to be obeyed in order to achieve a satisfactory quality of experience for the musicians. Slicing is a promising feature of 5G networks in the context of NMP applications, as it makes it possible to isolate the networking and computing resources allocated to NMP devices. However, the use of slicing has been scarcely investigated for the NMP context so far. Moreover, previous works focusing on NMPs over 5G involved up to 4 nodes. To bridge these gaps, we study 5G performance in support of NMPs involving an architecture with 10 nodes, both with and without slicing. Specifically, we focused on the assessment of the sole wireless link, as the measurements can be easily transferred to a realistic NMP architecture involving a wide area network. Our results show that, in the slicing condition, latency slightly increased due to the realistically different computing specifications of the MEC servers compared to those of the Core Network servers, whereas reliability slightly improved as expected.

关键词： Wireless communication Wide area networks 5G mobile communication computer network reliability Music computer architecture Throughput Reliability Servers Quality of experience

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Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Developing xApps for Rogue Base Station Detection in SDR-Ena...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Huang, Jun-Hong Cheng, Shin-Ming Kaliski, Rafael Hung, Cheng-Feng National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Sun Yat-sen University Dept. of Electrical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665494274

In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E- Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision 'recall' and F1 score. © 2023 IEEE.

关键词： Base stations

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