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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sai Surya Vigneshwar, M. Kiran Kumar, L. Sathyabama University Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

This paper proposes the development of a voice assistant integrated into an augmented reality (AI) environment. Voice assistants have become increasingly popular in recent years, with the widespread adoption of smart speakers and mobile devices. Meanwhile, AI technology has also advanced rapidly, offering new ways to interact with digital information and objects in the real world. The proposed system aims to combine the convenience and ease of use of a voice assistant with the immersive and interactive nature of AI. The user will be able to access the voice assistant through a wearable device, such assmart glasses or a headset. The voice assistant will respond to the user's voice commands and provide relevant information and assistance, displayed as virtual overlays in the AI environment. The implementation of the system will require overcoming technical challenges related to the integration of voice recognitionand AI technology, as well as ensuring user privacy and security. However, the potential benefits of a voice assistant in any environment make it a promising area for future research and development © 2024 IEEE.

关键词： Augmented reality

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Exploration of Tomato Leaves Diseases Classification Through Advanced Machine Learning and Deep Learning Models

Exploration of Tomato Leaves Diseases Classification Through...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Chaudhary, Anjali Raheja, Neeraj Department of Computer Science and Engineering Haryana Mullana Ambala India

ISBN: (纸本)9798331542344

This research paper defines and analyses the transformation where 'ML and DL techniques are employed for detecting and classifying tomato leaf diseases'. This paper aims to investigate and compare different ML and DL models. The goal is to understand whether the majority of these models is efficient and reliable and help to identify the disease accurately. The problem of the crop disease is far from new, and it has always influenced the yield;in particular tomato plants are usually affected by different biotic and abiotic factors. Therefore, the research explores the most widespread ML models such as k-NN, SVM, DT and RF with a reference to newer models, DL and CNNs and their variants, which are used in the early detection and management of such issues. The researched method is rather vital in this case;according to Hokianga, this approach will be useful when there is'a need to forensically isolate which process or category of polls caused the differing predictions'. The method of research will be quite useful as it is also supported by a detailed literary survey, which helps to understand the ideas and findings of other authors and identify strengths and weaknesses in the approaches applied. Besides, the research results of many sources that are published after the year 2022, and until now, are presented with comparisons and a certain conclusion. In particular, it assists in understanding 'the AUC as a critical measure of evaluating models'. In this case, a certain number of bar and line charts can be provided to illustrate how different models are efficient and reliable. They prove that with recent findings, different ML and especially DL models, as well as CNNs are very reliable and accurate while detecting sick tomato leaves. Therefore, this research can be regarded as rather vital since it identifies the most dependable models for applying them in real-world scenarios, which will help improve the yield. © 2024 IEEE.

关键词： Plant diseases

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Machine Learning in Financial Markets: Analyzing Predictive Accuracy for Stock Prices

Machine Learning in Financial Markets: Analyzing Predictive ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Jha, Amit Kumar Shiwani, Savita Department of Computer Science and Engineering Poornima University Jaipur India

ISBN: (纸本)9798350374933

Researchers understand that investigating the prediction of stock market movements is an important and productive area of research. Astute investors have the capacity to create substantial financial profits by taking advantage of swings in stock prices. Investors face a significant challenge when attempting to predict the stock market: the data they have access to is either unchanging or unpredictable. Predicting the performance of the stock market is a major challenge for investors aiming to maximize their profits. Cutting-edge mathematical techniques and advanced learning systems produce forecasts for the stock market. This paper's review encompasses various topics, including computational methodologies, Machine Learning (ML) algorithms, performance metrics, and reputable references cited in various research works. As a result, the selected publications contribute to the development of datasets and ML algorithms used to forecast future stock market movements. Typically, we generate stock market forecasts using Multilayer Perceptron (MLP), Gaussian Naive Bayes (GNB), Decision Tree (DT), Logistic Regression (), and Gradient Boosting (GB) approaches. Regrettably, the current methods for predicting stock market trends still suffer from numerous issues. © 2024 IEEE.

关键词： Adversarial machine learning

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Covid-19 Detection From CT Scan Images Using Deep Learning Models 6

Covid-19 Detection From CT Scan Images Using Deep Learning M...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Alam, Syeda Sadia Habib Munna, Kawsar Shahriyar Rafee, Md. Mushtaq Metropolitan University Department of Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350350180

Since 2020, the whole world has been dealing with a pandemic called coronavirus (COVID-19). Compared to automated methods, detecting the presence of the virus in the human body by manual methods is more expensive and time-consuming. Therefore detection of the virus by automated method has further stimulated our research interest. One of the most popular and important methods for automated detection of any disease is CT scanning of the lungs. Our study presents a novel technique that uses a state-of-the-art computer analysis to accurately classify and isolate lung infections by meticulously analyzing lung CT scan images. Our research uncovers minor differences among classes. So, it indicates the need for ongoing development through collaboration with medical specialists to address the minor differences. This study presents a comprehensive technique that uses sophisticated computer analysis to accurately classify and isolate lung infections from Lung CT scan images. The technique, which is a complex combination of some classical CNN architectures, performs admirably in terms of accuracy and is especially good at identifying spots of infection in lung pictures. Our study integrates advanced techniques such as EfficientNetB4, VGG19, VGG16, NASNetMobile, Xception and proposed a new model. The latters obtain an in-depth understanding of complex lung architecture and infection patterns by using several masks. We get 99.96% accuracy on our proposed model compared to other evaluation. This work highly benefits the field of COVID-19 image analysis and offers a thorough framework for real-world use in healthcare sectors. We also find that this proposed model is appropriate for the used dataset. © 2024 IEEE.

关键词： COVID-19

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Improving Drone Agility and Stability with Advanced Flight Control Systems Using MPU-6050 IMU and High-Fidelity Simulation

Improving Drone Agility and Stability with Advanced Flight C...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sing Kang, Sandeep Singh, Nongmeikapam Thoiba Grover, Richa Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798331542344

The study intends to improve drone capabilities for recreational use by creating a strong flight control system. The effort aims to educate drone enthusiasts and specialists on a novel approach to assessing and testing UA V flight control systems using high-fidelity simulation and MATLAB SimulinklFPGA Hardware-in-the-Loop (HIL). The MPU 6050 IMU and other sophisticated sensors and control algorithms enable the system to achieve exceptional flight stability, accuracy, and agility. When compared to conventional drone designs, extensive testing shows enhanced trajectory tracking, agility, durability, and reliability. The accomplishments of the research highlight the possibility of additional developments in autonomous drone systems, such as the incorporation of sophisticated algorithms such as computer vision and swarm intelligence. With planned improvements ranging from intelligent navigation to energy efficiency optimizations for extended flight endurance, the study's open-sourcing approach fosters cooperation and creativity in the development of future drone technology. © 2024 IEEE.

关键词： Drones

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Design Space Exploration of Partial Product Reduction Stage on 8x8 Approximate Multipliers

Design Space Exploration of Partial Product Reduction Stage ...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Papatheodorou, Georgios Rizos, Ioannis Efthymiou, Aristides University of Ioannina Department of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350318845

In the nano-scale era, the main objective of integrated circuits is to enhance speed while minimizing power consumption and area. This demand has given rise to the concept of approximate computing, which holds great significance in error-tolerant applications such as multimedia, machine learning, signal processing, and scientific computing. This research delves into the characteristics of various approximate reduced complexity Wallace multipliers, assessing their introduced error and area usage. In the present study, the approximation of the calculations is performed at the stage of reduction of partial products. This stage comprises several full adders, which were substituted with approximate full adders to introduce approximation. The study's findings emphasize that the introduced error and required area vary depending on the combination of approximate adders used, as well as on the placement of these adders in the partial product reduction stage. Utilizing a Genetic Algorithm (GA), we achieve a size reduction of our approximate multiplier to 54% of the exact multiplier. Additionally, by allowing the expansion of the size to 72% of the exact multiplier's area, we enhance the accuracy of the approximation by 32%. The suggested approach could assist designers in choosing the most appropriate amongst the available configurations of approximate adders for the partial product reduction stage in a reduced complexity Wallace multiplier, tailored to the specific requirements of their application. © 2024 IEEE.

关键词： Adders

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Analyzing the Computational Efficiency of LLM Models for NLP Tweet Classification During Emergency-Crisis 7th

Analyzing the Computational Efficiency of LLM Models for NLP...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Raja, Sabarish Raja Ramesh Antony Vigil, M.S. Pattaiah, Muthukumar Sudarson, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9783031699818

Social media platforms which include Twitter, Reddit, Instagram, and YouTube have emerged as key information sources during emergencies or natural disasters. However, in the case of Twitter, it might be challenging to distinguish the tweets that are genuinely informative due to the overwhelming number of tweets that are sent during these occurrences. This study proposes a method for classifying the tweets as "informative" and "non-informative" using natural language processing. The technique considers the tweets’ subjects, hashtags segmentation, and locations for pre-processing. BERT, RoBERTa, LSTM, and DistilBERT are four different NLP models that have been trained and tested. The findings will indicate the highest classification accuracy for determining if a tweet is informative or not. Our findings reveal that DistilBERT achieved the highest accuracy of 92.0% in testing and 89% in precision of the Twitter dataset. © IFIP International Federation for Information Processing 2024.

关键词： Tweets

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Scheduling a Single-Arm Multi-Cluster Tool With a Condition-Based Cleaning Operation

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1965-1983页

作者： Qinghua Zhu Hongpeng Li Cong Wang Yan Hou School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102-1982 USA

As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase scheduling complexity in cluster tools. In this paper, we focus on scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning ***, algorithms for a feasible schedule are also *** an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.

关键词： Chamber cleaning multi-cluster tools scheduling semiconductor manufacturing

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Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR Approach with Domain Adaptation for Banking Applications 1

Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Khalid, Ayesha Adeeba, Farah Sehar, Najm Ul Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Department of Computer Science University of Engineering and Technology New Campus Pakistan

ISBN: (纸本)9798891762015

The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to higher Word Error Rates (WER). This paper presents the development of a large vocabulary Urdu ASR system for telephonic speech, based on a corpus of 445 speakers from diverse domains. The corpus, annotated at the sentence level, is used to train and evaluate GMM-HMM and chain Time-Delay Neural Network (TDNN) models on a 10-hour test set. Results show that the TDNN model outperforms GMM-HMM. Mixing narrowband and wideband speech further reduces WER. The test sets are also evaluated for the pre-trained model Whisper for performance comparison. Additionally, system adaptation for the banking domain with a specialized lexicon and language model demonstrates the system’s potential for domain-specific applications. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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