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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Gad, Abhay S Bhamidipati, Kishore Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798350361537

Rice is a staple cereal grain for more than half of the world's population, segregating rice is a crucial for food security and economic reasons. Traditional methods of rice segregation are time consuming, labor intensive and subjected to human error. By implementing this methodology we can ensure that the cost paid by the consumer is justified and accurately reflects the quality of the rice. This research presents a comprehensive methodology for the automated segregation of rice grain based on detection of broken rice using Watershed algorithm. Detection of number of rice grains. For detection of broken rice we implement using Threshold algorithm. These all methodology (RAQ) (Rice Grain Segregation and Quality Assessment) make it a valuable tool for enhancing food security and quality control in the rice industry. Using the proposed (RAQ) method, we have achieved promising results in the detection of broken rice and segregation of rice. This method (RAQ) offers a powerful method for segregation of rice it can be used in rice industry for enhancing food security and quality control processes. Its efficiency, accuracy and scalability make it a valuable asset. This method can also be implemented on other grains too. © 2024 IEEE.

关键词： Grain (agricultural product)

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Enhancing Stockpile Management Through Deep Learning with a Focus on Demand Forecasting and Inventory Optimization

Enhancing Stockpile Management Through Deep Learning with a ...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Sheela, S. Latha, A.P. Jyothi, S. vidyarani, H.J. Komala, M. Sharmila, N. Mallikarjunaswamy, S. Pavithra, G.S. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bengaluru India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Rns Insitute of Technology Dept of Computer Science and Alml Bengaluru India

ISBN: (纸本)9798350388602

Stockpile management is essential for industries to efficiently meet customer demands while minimizing costs. Traditional methods like the Economic Order Quantity (EOQ) model and ARIMA often struggle with adaptability and accuracy, leading to inefficiencies such as overstocking or stockouts. This study proposes using Long Short-Term Memory (LSTM) networks to improve demand forecasting in stockpile management. LSTMs are designed to handle sequential data, capturing complex patterns and long-term dependencies that conventional methods miss. The LSTM-based approach achieved up to a 0.30% improvement in forecasting accuracy, resulting in more precise inventory control and better alignment with actual demand. The proposed system, termed Deep Inventory Management (DIM), incorporates LSTM models that adapt to changes in demand by considering factors such as seasonality and trends. This adaptive approach allows for proactive inventory predictions, enhancing decision-making in procurement and stock levels. Key abbreviations include LSTM (Long Short-Term Memory), EOQ (Economic Order Quantity), and DIM (Deep Inventory Management). By aligning inventory more closely with demand, this deep learning approach enhances operational efficiency, reduces costs, and improves customer satisfaction, making it a valuable advancement over traditional inventory management methods. © 2024 IEEE.

关键词： Semantic Segmentation

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Synchronization and implementation of real-time traffic signal optimization regulator

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Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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Detection and Classification of Blood Cancer using an Auto Encoder and a Regularised Extreme Learning Machine 14

Detection and Classification of Blood Cancer using an Auto E...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sharath, M.N. Rajesh, T.M. Surabhi, K.R. Sanjay, M. Nikshepa, T. Sowmya, S.R. Department of Computer Science and Engineering Rajeev Institute of Technology Hassan India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India

Leukemia is by far the most common type of blood cancer in humans. The length of time it takes to recover from an illness is determines by how quickly it is recognized and treated. Leukemia develops when the bone marrow generates an abnormally large amount of white blood cells. Because images of human blood samples are studied under the microscope, hematologists use techniques such as microscopic color imaging, image segmentation, grouping, and classification to diagnose patients as quickly and precisely as possible. Microscopy can identify blood cancer in visible and immature white blood cells in a variety of methods. Early and correct diagnosis of leukemia is critical for clinicians to properly treat patients. Following the submission of the input image, Image processing in image. © Grenze Scientific Society, 2023.

关键词： Image segmentation

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DALR: A Dynamically Adjustable Linear Regression Model for Handling Stream Data 4

DALR: A Dynamically Adjustable Linear Regression Model for H...

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

作者： Mondal, Rishita Mandal, Sourab Dutta, Paramartha Pal, Arindrajit Visva-Bharati Department of Computer & System Sciences WB Santiniketan India Academy of Technology Department of Computer Science and Engineering Adisaptagram WB India

ISBN: (纸本)9798350389128

In response to the escalating demand for machine learning techniques capable of handling real-time data streams, particularly in applications like stock markets, this research dives deep into the domain of stream regression. The objective is to adeptly navigate challenges posed by evolving data over time, differentiating it from conventional static regression. Existing models, such as Fast and Incremental Model Trees with Drift Detection (FIMT-DD), k-Nearest Neighbours (KNN), and Adaptive Linear Regression (ALR) and some other studies, though competent, exhibit certain limitations like learning based on certain portions of data, using a significantly large memory and not capable of handling each data in every timestamp it arrives. We propose the Dynamically Adjustable Linear Regression Model (DALR) to address these shortcomings. DALR introduces an adaptive regression model that dynamically evolves based on historical knowledge and newly acquired data, providing an innovative solution to the intricacies of dynamic data streams. This endeavor has also highlighted that the DALR algorithm consistently demonstrates strong performance across various datasets according to the RMSE metric, surpassing other state-of -the-art algorithms in most instances. © 2024 IEEE.

关键词： Financial markets

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Using Machine Learning to Forecast the Risk of Suicide Attempts over Time 1

Using Machine Learning to Forecast the Risk of Suicide Attem...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Shetty M, Swara Praveen, Varsha Chinmayi, H.S. Bhavitha, K.S. Kamal, Shoaib Rao, Trupthi Global Academy of Technology Department of Computer Science Engineering Bengaluru India Global Academy of Technology Department of Electonics and Communication Engineering Bengaluru India Global Academy of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350339727

Suicide is recognized as a grave public health concern affecting individuals of all ages, from children and adolescents to young adults and beyond. It represents a significant cause of mortality within this population, influenced by various factors. Conventional methods for predicting suicide attempts have restricted the accuracy and scope of detecting these perilous behaviors. To enhance the accuracy and scope of detecting suicide attempts, we utilized machine learning on electronic health records from a vast medical database. Our study involved 469 adults exhibiting suicidal tendencies, among whom 85 patients made suicide attempts (cases). Additionally, 384 participants who did not attempt suicide but had depression showed promising signs of suicidal behavior, as confirmed by expert record review. This approach surpasses the limitations of traditional methods in predicting suicide attempts. Through the development of machine learning algorithms, we achieved accurate predictions of future suicide attempts (AUC = 0.80, precision= 0.30, recall = 0.54, Fl-score = 0.32, support=13). Our findings are a significant stride towards precise and scalable detection of suicide risk. Additionally, they offer valuable insights into how the risk of suicide attempts fluctuates over time. © 2023 IEEE.

关键词： Machine learning

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Effects of different scattering components in speckle autocorrelation imaging

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Optics Letters 2025年第3期50卷 880-883页

作者： Xie, Gengchen Zhang, Xuyu Zhang, Dawei Han, Shensheng Liu, Honglin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Hangzhou Institute for Advanced Study Hangzhou310024 China

Various methods of imaging through scattering media suffer a rapid degradation in image quality as medium thickness increases. To address this issue, based on speckle autocorrelation imaging we designed a double-layer diffuser experiment to study the effects of different scattering components on imaging. The results show that both singly scattered light and doubly scattered light can transmit information. However, the difference in memory effect range leads to diverse abilities in information extraction, thereby affecting image recovery. This study may provide insights into the mechanisms of information transmission within thick scattering media and methods to extract information from multiply scattered light to reconstruct images. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Speckle

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A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Srichandan, Suresh Kumar Jena, Sudarson Majhi, Santosh Kumar Mishra, Kaushik Veer Suredra Sai University of Technology Department of Computer Science and Engineering Odisha Sambalpur Burla India Sambalpur University Institute of Information Technology Sambalpur Univesity Department of Computer Science and Engineering Odisha India Guru Ghasidash Vishwavidyalaya Department of Computer Science and Information Technology C. G. Birlaspur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350379525

The Internet of Things (loT) is rapidly advancing, but its limited resources are often supplemented by integrating fog computing to address these constraints. Despite this, fog devices encounter challenges such as heterogeneity, widespread distribution, dynamic nature, and resource limitations. To tackle these issues, a robust task scheduling strategy is crucial for efficiently utilizing fog resources and enhancing Quality of Service (QoS). Moreover, tasks generated are dynamic in nature with disparate requirements such as deadline, cost, priority, etc. Fog resources could be utilized effectively with proper mapping of tasks based on ingress traffic on each node to meet the requirements. This research formulates the task mapping problem (TMP) with the goal of minimizing cost while improving QoS by reducing response times and deadline violations for loT tasks. It uses SVM (Support Vector Machine) to predict fog node traffic and categorizes nodes into peak-traffic and low-traffic groups. The Quantum-inspired PSO-based TM Algorithm (QPSO) has been implemented to map the tasks on these nodes. Simulation results reveal that the proposed method offers significant improvements, achieving up to 19.66% better response times, 30% cost reduction, and a 26% deadline violation rate i.e., an increase in meeting task deadlines compared to other approaches. © 2024 IEEE.

关键词： Cost reduction

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Comparative Study on the Analysis of the Performance of Transfer Learning and the Customized Convolutional Neural Network to Detect Eyewear 4

Comparative Study on the Analysis of the Performance of Tran...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Varun Prakash, J.M. Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Dept of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350305258

In this paper, we propose a method for eyewear detection using Convolutional Neural Networks (CNNs). The objective of the study is to accurately classify images into two classes: "Glasses" and "No Glasses." The proposed approach is evaluated on a dataset comprising a total of 5000 images, with a training set consisting of 1947 images in the "Glasses" class and 1554 images in the "No Glasses" class. The testing set includes 834 images in the "Glasses" class and 665 images in the "No Glasses" class. We evaluate the eyewear detection performance using various evaluation metrics, including accuracy, precision, and recall. The outcomes demonstrate how well the suggested CNN-based technique for eyeglasses detection works. The trained model achieves an overall accuracy of 99.17%, with a precision of 0.76% and a recall of 0.44% on the testing set. The discovery advances the field of computer vision and creates new opportunities for improving eyeglasses detection methods through further additional study. © 2023 IEEE.

关键词： Accuracy Evaluation computer Vision Convolutional Neural Network Eyewear detection Feature Extraction Image Classification

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Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

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