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Design and Evaluation of Speech Processing Systems for Meetei/Meitei Mayek 1st

Design and Evaluation of Speech Processing Systems for Meete...

引用

1st International Conference on Sci/Tech and engineering, ICSTE 2023

作者： Pangsatabam, Hoomexsun Chanu, Yambem Jina Singh, Naorem Karline Department of Computer Science and Engineering National Institute of Technology Manipur Manipur Imphal795004 India

ISBN: (纸本)9789819947126

The paper focuses on Manipuri, an under-resourced language, and is written in a way that will help eager researchers build a robust speech processing system. To improve the quality of speech processing and language processing systems, the tools and models need to be properly designed and evaluated. Various metrics are considered in the areas of syllabification, speech recognition, speech synthesis, machine translation, etc. Multiple works have demonstrated that authors value accuracy, precision, and scores. Tests are designed to assess data perplexity, naturalness of speech, and intelligibility. Furthermore, the challenges and difficulties associated with the development of various systems are compared. The evaluation metrics, both subjective and objective, are thoroughly discussed, as are various modifications designed to address specific issues arising during data collection, design, and evaluation, mainly in speech recognition and speech synthesis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech synthesis

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Global Context-Aware Method for Infrared Image Non-Uniformity Correction 3

Global Context-Aware Method for Infrared Image Non-Uniformit...

引用

3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Ma, Zhiwei Zhang, Shuiping Wang, Lei Rao, Qiang School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Infrared imaging systems are often affected by non-uniformity, which can significantly reduce the quality of captured images and hinder subsequent image-processing tasks. Existing infrared image non-uniformity correction methods mainly suffer from incomplete detail recovery and easy ghosting. This paper proposes a global context-aware infrared image non-uniformity correction method, which effectively captures long-distance dependencies and complex feature patterns by combining residual networks and self-attention mechanisms. To verify the effectiveness of our method, we conducted extensive experiments on multiple public datasets. Experimental results show that compared with the existing state-of-the-art technology, our method performs better in both qualitative and quantitative evaluations and can effectively remove infrared image stripe noise in complex scenes while retaining key details of the image. © 2024 IEEE.

关键词： Infrared imaging

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Smart Agricultural Greenhouse System: A Context-Aware Application

Smart Agricultural Greenhouse System: A Context-Aware Applic...

引用

International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Swain, Sujata Niyogi, Rajdeep School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science IIT Roorkee Roorkee India

ISBN: (纸本)9783031513374

Smart applications are getting more powerful and cheaper cost due to the advancement in sensor technology. In this chapter, we have considered a smart greenhouse application. The important parameters of the smart greenhouse are to control light, temperature, humidity, and water. Greenhouse can provide a favourable growth environment for plants. This smart greenhouse system is a context-aware application. When the context changes in the environment, the application automatically adapts it and does the changes seamlessly without user intervention. In this chapter, we propose a model for the smart greenhouse system. The proposed model can adapt to context changes such as temperature rises, and it automatically turns on the AC seamlessly. The proposed model is based on the machine learning approach. The model maintains the greenhouse temperature, humidity, and sunlight duration in the suitable ranges of the plant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context Greenhouse Machine learning NLP Sensors Web service

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Vitamin Deficiency Detection Using Neural Networks 9

Vitamin Deficiency Detection Using Neural Networks

引用

9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Krishna Chaithanya, T. Gupta, Jiniya Roobini, M.S. Department of Computer Science and Engineering Chennai600119 India Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350350845

Vitamin deficiency is a pervasive health issue affecting millions globally, often leading to severe health complications if undiagnosed. Various vitamin deficiencies can be identified by identifiable symptoms manifesting in numerous areas across the human body. Blood tests are most commonly used to detect deficiencies. However, blood tests are expensive and have limited availability. The project aims to develop an automated system to detect vitamin deficiencies using Mobilenet, convolutional neural networks (CNN), NasNet-mobile, inception modules, and artificial neural networks (ANN). The program allows users to detect potential vitamin deficiencies without needing blood samples by examining images of the eyes, lips, tongue, and nails. Here we have considered the dataset of abnormalities caused by vitamin deficiencies like glaucoma eyes, bluish nails, and other abnormalities of lips and tongue. We have trained the model on these image datasets, by leveraging image recognition and data analytics, the proposed system offers a quick, efficient, and non-invasive method of detection thereby streamlining the diagnostic process and facilitating early intervention. The aim is to provide an economical, accessible, and efficient clinical screening method. Moreover, the platform furnishes tailored dietary suggestions to tackle pinpointed inadequacies, thus alleviating health hazards linked with insufficient nourishment. By furnishing an easily reachable resource for prompt identification and action, our system aids in tackling a prevalent worldwide health concern. The program functions as a beneficial aid for individuals to combat a widespread global challenge, impacting millions due to inadequate comprehension of nutrition. Serving as a valuable resource, it addresses an international issue, assisting those facing nutritional uncertainties worldwide. © 2024 IEEE.

关键词： Blood

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Network Intrusion Detection with Feature Elimination and Selection Using Deep Learning

Network Intrusion Detection with Feature Elimination and Sel...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Mann, Aryam Kiran, M Vamsi Nithya, B. Das, Rishav National Institute of Technology Trichy Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350372502

In the contemporary digital landscape, marked by an ever-increasing number of individuals seeking to exploit vulnerabilities in systems and cause immense damage to users, it becomes imperative for users to safeguard their systems, detect threats correctly, and carry out an appropriate response. Implementing Intrusion Detection Systems (IDS) is vital for monitoring network traffic, spotting threats, and swiftly alerting users to suspicious activities. It is categorized into two parts: Network-based (NIDS) and Host-based (HIDS). NIDS is strategically placed and monitors data flow across devices. A major NIDS challenge is the prevalence of false positives and, more critically, false negatives, potentially allowing undetected threats to cause significant harm. To improve intrusion detection, a new approach using Feature Selection and Deep Learning framework is used for Network Intrusion Detection System. The study analyzes metrics like accuracy, precision, F1 score, and confusion matrix. © 2024 IEEE.

关键词： Intrusion detection

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The integration of IoT in learning environments: Assessing the impact of educational empowerment and addressing its challenges 4

The integration of IoT in learning environments: Assessing t...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350307757

This article explores the diverse impact of the Internet of Things (IoT) on education. This research investigation examines the possible uses of the IoT in several educational domains, with a particular focus on the significant impact of IoT on student engagement, learning methodologies, and administrative efficacy. While explaining the considerable advantages, this discourse also acknowledges the significant obstacles pertaining to integration, security, and privacy. The study finishes by presenting a forward-looking model for smart campuses, providing a holistic viewpoint on the IoT impact on transforming education. The article examines the impact of the IoT on educational practices and emphasizes the significance of institutional adaptation to effectively incorporate this technological paradigm shift. The article presents an elaborate framework for the prospective development of intelligent campuses and higher education institutions. This article offers a comprehensive guide for effectively using the whole capabilities of the IoT within the realm of education. © 2024 IEEE.

关键词： Internet of things

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Deep Neural Network Based Multi-Object Detection for Real-time Aerial Surveillance 11

Deep Neural Network Based Multi-Object Detection for Real-ti...

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11th International Symposium on Electronic Systems Devices and Computing, ESDC 2023

作者： Dey, Rebanta Pandit, Binit Kumar Ganguly, Anirban Chakraborty, Anirban Banerjee, Ayan Indian Institute of Engineering Science and Technology Electronics and Telecommunication Engineering Howrah Shibpur India University of Engineering and Management Computer Science and Technology Kolkata India

ISBN: (纸本)9781665455725

Aerial surveillance is one of the widely used modern days surveillance methodologies, finding applications in many important fields including military and civilian. This article presents a comprehensive study of Deep Neural Network (DNN) based solutions for real-time object tracking from Unmanned Aerial Vehicle (UAV) using a modified version of the state-of-the-art object detection algorithm YOLOv5 model. The modified YOLOv5 architecture is achieved by changing the activation function to Rectified Linear Unit (ReLU) and fine-tuning the network's hyperparameter. A comparative analysis was then done on a subset of the AU-AIR dataset by comparing the different YOLOv5 models based on the network depth to determine the improvements in training speed and accuracy. The modified network was also compared in terms of mean average precision (mAP) to the original paper, a performance gain of almost 2.9 times was achieved in the best-case scenario. © 2023 IEEE.

关键词： Object detection

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs

PythonSaga: Redefining the Benchmark to Evaluate Code Genera...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yadav, Ankit Beniwal, Himanshu Singh, Mayank Department of Computer Science and Engineering Lingo Research Group Indian Institute of Technology Gandhinagar India

ISBN: (纸本)9798891761681

Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two popular benchmarks for Python code generation, analyzing their diversity and difficulty. Our findings unveil a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. Furthermore, we uncover a worrying prevalence of easy tasks that can inflate model performance estimations. To address these limitations, we propose a novel benchmark, PythonSaga, featuring 185 hand-crafted prompts in a balanced representation of 38 programming concepts across diverse difficulty levels. The robustness of our benchmark is demonstrated by the poor performance of existing Code-LLMs. The code and data set are openly available to the NLP community at https://***/PythonSaga/PythonSaga. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Predicting Traffic Flow with Deep Learning 12th

Predicting Traffic Flow with Deep Learning

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Srivastava, Nishtha Devarakonda, Raja Ruthwik Krishna, Vamsi Bharadwaj, Bhavan Gohil, Bhavesh N. Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat395007 India

ISBN: (纸本)9789819732913

In transportation systems, a vast volume of traffic data is generated on a daily basis. The contributing factors for this traffic include expanding urban population, aging infrastructure, uncoordinated traffic timings, etc. Since traffic congestion costs valuable time and fuel every day, it needs to be monitored every day to avoid accidents and to enable the proper flow of traffic. This study aims to predict traffic flow using advanced forecasting techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and XGBoost models. Both the LSTM (Long-Short-Term Memory) and GRU (Gated Recurrent Unit) networks are used to predict the vehicle traffic flow and their prediction errors over different road junctions are compared to know which network works better. Experiments demonstrate that the proposed GRU model performs slightly superior to the LSTM model. The evaluation also shows that XGBoost-based methods perform the best in short-term and long-term traffic flow prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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