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26th International Conference on computer and Information technology, ICCIT 2023

作者： Hossain, S M Riad Zinnurine, Khaled Sadad, Nahin Ul Mondal, Md. Nazrul Islam Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

In order to accurately and quickly recognize Bengali handwritten digits and characters, this paper suggests an FPGA-based hardware accelerator design of ANN for handwritten Bengali character recognition applications. To discover the best FPGA design for the various categorization rates, a study of network designs is first undertaken. Sigmoid as well as ReLU neurons integrated by Xilinx Vivado workspace and an axi DMA controller created in Verilog Hardware Description Language (HDL) make up the majority of its components. It is clear that sigmoid-based function implementation exceeds ReLU-based implementation for very tiny data widths (such as 4 and 8 bits). The maximum detection accuracy for Sigmoid implementation is 94.00%, and the maximum for ReLU is 87.00%. Finally, this hardware upgrade considerably increased processing speed by 4 times compared to software implementation and about 27 times faster in terms of existing CPU-based hardware implementation, in addition to maintaining recognition accuracy the same as software equivalence. Also, the power consumption is reduced by more than half of its existing solution as discussed before. As a result, the fast and accurate recognition of Bengali handwritten numbers with low power consumption was made possible by our specialized ANN models with FPGA capabilities. © 2023 IEEE.

关键词： and ReLU Artificial Neural Network (ANN) Bengali Digit Field Programmable Gate Array Hardware Accelerator HDL Recognition Sigmoid

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Seeds Classification Using Deep Neural Network: A Review 6th

Seeds Classification Using Deep Neural Network: A Review

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Al Fahim, Hafiz Hasan, Md. Abid Bijoy, Md. Hasan Imam Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031503290

In the past few years, agricultural production studies have gained popularity, showing signs of rapid development. Using different kinds of computer technology, the newest thing to come along makes it easier for farmers to do their work. Many things affect agricultural output, but the efficiency of the seeds is the most important. Seed classification could give us more information about quality work, controlling seed quality, and finding impurities. Automated classification of seeds has been generally done based on factors like colour, texture, and size. Most of the time, specialists do this by looking at the samples, which is very much time-consuming. Adaptation of technologies for automated classification of seeds can be helpful in this regard. Fortunately, there are good number of research already carried out using Deep Neural Network (DNN) around the globe. In this paper, we provide a review of seed classification techniques with a strong focus on DNN. The goal of this research is to create a system for categorizing seeds based on visual and morphological traits. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

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Digital Communications and Networks 2023年第5期9卷 1230-1241页

作者： Jingyuan Duan Ling Tian Jianqiao Mao Jiaxin Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Shenzhen Institute of Information Technology China Department of Electronic and Electrical Engineering University College LondonGower StreetLondonUnited Kingdom Amazon(China)Investment Ltd China

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and *** can achieve maximal data value and prepare data for smart city *** to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading ***,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social *** solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

关键词： Data trading Double auction Social welfare Game theory Smart city

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A Clustering Based Niching Method for Effectively Solving the 0-1 Knapsack Problem 1st

A Clustering Based Niching Method for Effectively Solving t...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Sarker, Md. Meheruzzaman Islam, Md. Jakirul Hossain, Md. Zakir Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur Bangladesh

ISBN: (纸本)9783031346217

The 0-1 knapsack problem (01-KP) is a NP-hard combinatorial optimization problems (COPs) with several applications. Because of its non-convexity, its search space contains several local and/or global optimum solutions. In this situation, both the classical and metaheuristic global optimization approaches often failed to locate the optimal solution to the 01-KP. Therefore, this research develops a clustering-based niching (CBN) method for maintaining and locating multiple solutions within an optimization run, increasing the possibility of locating the global optimal solution to the 01-KP. To do this, CBN method divides the population individuals into a number of clusters (similar to niches in a biology or ecology system) by measuring the Hamming distance between individuals. During the optimization, the individuals in the formed clusters independently explore different regions of the combinatorial search space in order to find promising solutions. For simplicity, the proposed CBN method is implemented using a modified binary PSO (lbest-BPSO) method. The numerical results show that the CBN method is superior than the well-known metaheuristic methods on the basis of solution quality, with a better success rate and average function evaluations, over widely used sets of 0-1 knapsack instance. This guarantees that CBN method is a suitable optimization method for determining the optimal solution to combinatorial optimization problems. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Combinatorial optimization

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Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

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computers, Materials & Continua 2023年第3期74卷 5545-5557页

作者： Mutiullah Jamil Hafeez ul Rehman SaleemUllah Imran Ashraf Saqib Ubaid Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Wheat species play important role in the price of products and wheat production *** are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent *** task of wheat species identification is challenging both for human experts as well as for computer vision-based *** the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life *** this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)*** thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat *** addition,deep neural networks are also *** results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%*** suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

关键词： Mathematical model wheat crop estimation landsat8 remote sensing machine learning random forest deep neural network

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A Comprehensive Review on Family Budget Management 6th

A Comprehensive Review on Family Budget Management

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Akash, Rashik Shahriar Ullah, Mohammad Islam, Radiful Nahid, Sayed Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031501500

In the modern economic era, households have become one of the most significant corporate entities, and personal finances are essential management-related topics. Effective management of personal finances at a family level is really important to address all the financial related issues in an organized way. Any automated budget management system can help a family to utilize their incoming income in an efficient way. In this paper, we try to address the potential researches that were carried out considering the budget management related issues with a strong focus on family budget management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Finance

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Automated Recognition of Wild Animal Species in Camera Trap Images Using Deep Learning Models 3

Automated Recognition of Wild Animal Species in Camera Trap ...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Thangaraj, Rajasekaran Rajendar, Sivaramakrishnan Sanjith, M. Rithick Saran, K. Sasikumar, Sudev Chandhru, L. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494007

Wild animal monitoring is one of the essential factors in balancing the ecosystem in protecting the animal habitat. Camera traps are used to capture the images, which play a vital role in the automatic monitoring of wildlife owing to their effectiveness in collecting wildlife data. The images captured from the camera traps are large volumes in nature, and they are inexpensive. However, the manual processing of such a large number of images is a time-consuming, expensive, and challenging task. Such a kind of process provides major hurdles to ecologists in monitoring wildlife. This paper evaluates deep learning models such as DeneseNet169, InceptionResNetV2, ResNetV2-50, and Xception for the automatic identification and recognition of individual animals in different patterns. The effectiveness of the models is measured using precision, recall, and F1 score indicators. Finally, the experimental findings show that the InceptionResNetV2 model outperforms the other models with an accuracy of 99%. © 2023 IEEE.

关键词： Cameras

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Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

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SN computer science 2023年第1期4卷 91页

作者： Kumar, Yogesh Koul, Apeksha Kaur, Sukhpreet Hu, Yu-Chen Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Engineering Punjabi University Patiala India Department of Computer Science and Engineering Chandigarh Engineering College Mohali Landran India Department of Computer Science and Information Management Providence University Taichung Taiwan

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R² score values. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 Facebook Prophet Holt model Prediction Random forest regressor RANSAC regressor Stacked gated recurrent units Stacked long short-term memory XG Boost

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LoRaWAN: A Boon for the Remote Region IoT Applications

LoRaWAN: A Boon for the Remote Region IoT Applications

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Sharma, Anamika Muhuri, Samya Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350393415

IoT has paved the way for enhancing and transforming the quality of life through remote automation and monitoring via various short-range, long-range, and cellular network connectivity technologies. These technologies are very efficient in urban regions but struggle in remote geographically challenging environments hence stopping the implementation of IoT devices in such regions. In such a scenario, LoRaWAN emerges as a revolutionary technology, providing efficient connectivity and reliable communication for remote region IoT application devices. This paper aims to provide valuable insights into the network architecture capabilities of LoRaWAN for remote region IoT applications. © 2023 IEEE.

关键词： Network architecture

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Nature-inspired whale optimization technique for efficient information exchange in vehicular networks 12

Nature-inspired whale optimization technique for efficient i...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Jaypee University of Information Technology Department of Computer Science and Engineering Solan India

ISBN: (纸本)9781665462617

Vehicle Ad-Hoc Networks (VANETs) offer roadside connectivity to facilitate user information exchange while driving. It connects other vehicles and hierarchical infrastructure components for applications including real-time traffic and vehicle control. Concord information sharing is needed for application answers because of the increase in vehicle and information density. In this paper, a secure information exchange model has been proposed using nature-inspired whale optimisation algorithm as a way to provide faster data transmission speeds and security. The proposed model makes use of a social behavior model of humpback whales to explore and exploit the search space for finding the optimal solution. Successful information selection and traffic information retrieval depend on the prior and present judgements. This keeps extra data from clogging up the traffic and vehicle management application while the driver is using it. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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