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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Yadav, Gyan Singh Nehra, Ajay Vashistha, Aman Gupta, Adarsh Deshmukh, Omkar Indian Institute of Information Technology Kota Department of Computer Science and Engineering India

ISBN: (纸本)9798350376876

This paper proposes a novel optimized histogram equalization technique using a genetic algorithm (GA) for brightness-preserved contrast enhancement. This method optimizes the histogram equalization technique to extract detailed data from distorted images. The principal objective is to enhance histogram equalization by fixing a trade-off between contrast improvement and mean-brightness preservation. A genetic algorithm is used to generate the new target histogram. The optimized histogram is used in the histogram equalization (HE) process to generate the final enhanced image. The performance of the proposed method is checked and validated with qualitative and quantitative assessments against well-known state-of-the-art techniques. The result section depicts that the proposed method significantly enhances the visual effect of distorted images based on human subjective and objective evaluation indices. 3-5 percent enhancement can be observed if we compare the standard Histogram Equalization method to the proposed method. © 2024 IEEE.

关键词： Genetic algorithms

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Labor Safety Analysis Using Object Recognition and Deep Learning 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Evangeline, D. Jatin, B. Manoj, S. Pannaga, N. Sanjeev, G. Parkavi, A. Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Occupational safety is a major concern in any industry or workplace, and the use of advanced technologies such as object recognition and deep learning can greatly enhance safety measures. This paper proposes a labor safety analysis system that utilizes object recognition and deep learning algorithms to detect potential hazards and ensure safe working conditions. The proposed system can recognize various types of safety gear and analyze their positions and movements to detect potential safety in real time. The system can also provide alerts and warnings to workers and supervisors in case of the absence of safety gear. Experimental results demonstrate the effectiveness and efficiency of the proposed work. This can significantly improve the safety and productivity of the workplace. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object recognition

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Centralized Platform for Sensor Integration and AI Models in Precision Agriculture 2

Centralized Platform for Sensor Integration and AI Models in...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Chowdary, Ch Srinivasa Akshith Likith, S. Maya, B.S. Lokesh, S.V. Manish, Y. Asha, T. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India Bangalore Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A centralized platform for integrating various sensors and AI models in precision agriculture. The platform connects soil moisture sensors with AI models to generate crop recommendations and irrigation alerts, aiming to enhance resource utilization and crop yield. Key outcomes include real-time monitoring, actionable insights, and data-driven decision support for farmers. The practical implications for farmers and the agricultural industry are profound, as this system enables them to adopt precision agriculture techniques, reducing waste, increasing efficiency, and ultimately improving profitability © 2024 IEEE.

关键词： Smart agriculture

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Targeted Influence Maximization Using Centrality and Diminishing Weight

Targeted Influence Maximization Using Centrality and Diminis...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Baruah, Khanjan Changmai Tripathi, Rohit Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350350821

Social Networks are an integral part of our daily life. These networks are tools for sharing information among different users. In such a network, the most influential users become the source for disseminating information over the network. Hence, social network analysis was done to understand and identify the importance of a node in information diffusion. These influential nodes help to achieve maximum influence throughout the network. However, in certain cases, only a subset of nodes are needed to be influenced. This is called targeted influence maximization. In this paper, we use different centrality measures and diminishing weights to identify influential nodes and study the influence of these nodes on the target nodes. Our simulation shows that the seed selection scheme using centrality and diminishing weights activates more target nodes in a network than the existing schemes. © 2024 IEEE.

关键词： Centrality Measures Independent Cascade Model Social Network Analysis Target Influence Maximization

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YOLOv7-ConvTrans: Hybrid Vision Transformer with YOLOv7 for Underwater Object Detection 4

YOLOv7-ConvTrans: Hybrid Vision Transformer with YOLOv7 for ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Delsey, M.J. Bibal Benifa, J.V. Indian Institute of Information Technology Department of Computer Science and Engineering Kottayam India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

Underwater object detection plays a significant role in marine exploration activities such as ecological monitoring, conservation of undersea ecosystems, and underwater robotics. In contrast to detection in the atmosphere on land, underwater detection is challenging due to color distortion and attenuation, which render the detection process more difficult and imprecise. To address these issues, this paper proposes an improved detection framework, YOLOv7-ConvTrans. The YOLOv7 architecture is optimized by integrating a convolution vision transformer in the neck region to enhance the detection accuracy of small and occluded objects. The proposed method reduces redundancy, computational cost, and recognition time to extract significant feature maps from the imagery. In the experimental assessment on the URPC2019 dataset, a mAP of 80.39 and on the Brackish dataset, 92.61, indicate high detection accuracy. Finally, our proposed approach provides a valuable framework for designing underwater vision systems that can be utilized in various scientific and exploratory domains. © 2024 IEEE.

关键词： Machine vision

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieval in Congested Road Networks Using Predicted Speeds 10

Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieva...

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10th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2024

作者： Sondhi, Kartikey Dalapati, Poulami Kumar, Saurabh Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789897587030

Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addresses the critical issue of identifying the fastest, least congested path in road transport networks, aiming to enhance overall travel efficiency and reduce the negative impact of traffic congestion. The study employs an improved version of the Lifelong Planning A∗ (LPA∗) that helps find the fastest route between two points in dynamic changing environments. The proposed methodology is called the Lifelong Dynamic Timed A∗(LTA∗) algorithm with an optimal bound weight factor integrated with it to make the search more guided and efficiently predict optimized traffic paths to provide real-time recommendations. To validate the effectiveness of the developed algorithm, extensive simulations and case studies are conducted on a small area inWashington as well as on GridWorlds. The experimental results show that LTA∗, within accurate weight bounds, always managed to find the fastest path, and in some cases, the time taken was close to half of that produced by A∗. Copyright © 2024 by SCITEPRESS - science and technology Publications, Lda.

关键词： Traffic congestion

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Analysis of optical networks in presence of nodes noise and crosstalk

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Journal of Optical Communications 2024年第3期45卷 547-554页

作者： Shukla, Rahul Deo Pratap, Ajay Suryavanshi, Raghuraj Singh Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Kanpur India

Optical packet switching has gained lot of momentum in last decade due to the advantages of optical fiber over copper cables. Optical switching is beneficial in optical networks which form connections of links and switching nodes. In these high speed networks minimum delay and high throughput are two important parameters which are considered. To minimize network delay shortest path algorithm is used for route selections. In previous studies while choosing shortest path distance among various nodes is considered. In this work we have shown that it is necessary to consider both distance and number of hops while choosing path from source to destination to minimize power per bit used for the transmission. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Optical switches

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Dimensionality Reduction and Gradient Boosting for In-Vivo Hyperspectral Brain Image Classification

Dimensionality Reduction and Gradient Boosting for In-Vivo H...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Raj Bahadur Datta, Aloke The Lnm Institute of Information Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350376876

Hyperspectral imaging (HSI) datasets contain hundreds of contiguous narrow spectral bands, which can create challenges in data analysis due to the curse of dimensionality. However, much of this data is redundant, necessitating the application of dimensionality reduction techniques to extract the most relevant features before the classification task. Two feature extraction methods, principal component analysis (PCA) and non-negative matrix factorization (NMF), are explored. Then, the reduced feature sets are used to train three gradient-boosting algorithms, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), for the classification of in-vivo hyperspectral brain images. The experimental results show the effectiveness of gradient boosting over support vector machine (SVM) and random forest (RF) in performance measures like accuracy, sensitivity, specificity, and F1-score. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Ethical Dimensions in Data-Driven Business: Balancing Fairness, Accountability, Transparency, and Explainability 15

Ethical Dimensions in Data-Driven Business: Balancing Fairne...

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

作者： Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

In the modern business landscape, the widespread adoption of data-driven decision systems underscores the importance of integrating ethical principles such as fairness, accountability, transparency, and explainability (FATE). These principles are critical for ensuring that decision-making processes are conducted ethically and responsibly. Fairness prevents biases and discrimination, accountability ensures organizations are held responsible for outcomes, transparency fosters trust by making processes accessible, and explainability enables users to comprehend algorithmic decisions. This paper examines the significance of FATE principles in business contexts, emphasizing their role in promoting ethical decision-making and societal welfare. Through case studies and best practices, it highlights the practical implementation of these principles to ensure the responsible use of data-driven technologies in business operations. © 2024 IEEE.

关键词： Data assimilation

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