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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Ahmed, Md. Shihab Uddin Rouf, Nirjhor Tahmidur Hasan, A N M Shahebul Hasan, Md Abid Rozario, Omi Evance Faiyaz Rahman, A.A.M. Al Saleh, Sheikh Raihan Aditto, Shadman Shakib Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

One of the fundamental goals of 'Digital Bangladesh ' is ensuring people's democracy, human rights, transparency, and delivering government services through the maximum use of modern technologies. However, Bangladesh Election Commission is still striving to implement Electronic Voting Machines (EVM) in the parliamentary general election on an extended scale due to the lack of acceptance concerning EVM accuracy, easy accessibility, and security. This paper describes an implementation of a low-budget and securely accessible solution for the Electronic Voting Machine feasible for Bangladesh with the benefit of verifying the registered voters with a central database through a biometric identification process and maintaining the secrecy of the voters. The proposed system uses low profile microcontrollers (Arduino Uno), which reduces the device's cost to approximately half of the price of other available versions of EVM. The system can communicate with the database to store voter information and vote counts that make the system secure against any fraudulence during the election process and eliminate manual vote tallying problems. © 2021 IEEE.

关键词： Biometrics

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Examining Amazon Customer Reviews using PySpark and AWS: A Data Lake Approach

Examining Amazon Customer Reviews using PySpark and AWS: A D...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Remya S Abburi Sai Karthik Maddala H S M Krishna Karthik Marasani Jayasurya Samudrala Yashwanth Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham

In the contemporary world, big data analysis with PySpark and AWS has become quite handy. Large multinational companies, including Walmart, trivago, and countless others are using big data. And with the assistance of AWS, which offers a variety of services like glue, Athena, S3, etc., becoming extremely beneficial for cloud storage purposes and also for creating data migration pipelines. Any RDBMS database server might send tens of gigabytes of data to an Amazon S3 bucket using the ETL architecture. Our primary goal in this project is to use PySpark to construct an ETL pipeline for data migration. Amazon wireless devices review analysis, Amazon watches review analysis, Amazon books review analysis, Amazon shoes review analysis, and Amazon musical instruments review analysis are the five separate datasets on which we conducted our analysis. The datasets considered for the analysis are Amazon watch reviews, book reviews, shoe reviews, wireless device reviews and musical instrument reviews . The goal of the project is to obtain the ratio of the number of trustworthy, real reviews to untrusted reviews and a comparison in done in the form of graphs.

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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From Pixels to Diagnosis: Convolutional Neural Networks in Tuberculosis Screening

From Pixels to Diagnosis: Convolutional Neural Networks in T...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Tanvir Mahtab Zihan Abduz Zami Mohiuddin Ahmed Rakibul Islam Fahim Ahmed Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of CSE UIU Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Tuberculosis (TB) is a chronic lung disease that constitutes one of the top 10 global causes of death. Timely and precise identification of TB is critical, as untreated cases can pose life-threatening risks. Traditional radiographic interpretation by trained professionals is subjective, time-consuming, and prone to human error. This study proposes an automated deep learning-based approach for TB detection from chest X-ray images, based on EfficientNet-B0 architecture. The Convolutional Neural Network (CNN) extracts discriminative features, allowing it to differentiate between normal and abnormal lung patterns indicative of TB. Our choice of the EfficientNet-B0 architecture played a pivotal role in the success of our TB detection model. Training and evaluation were conducted on three distinct datasets: Tuberculosis (TB) Chest X-ray Database, Shenzhen Hospital CXR Set, and Montgomery County CXR Set. The key breakthrough is attaining an accuracy of 99.29% on the Tuberculosis (TB) Chest X-ray Database and 98.50% on the combined dataset. This study presents a promising approach for automated TB screening using readily available chest X-ray images, potentially improving diagnostic accuracy and efficiency, especially in resource-limited settings. We compared our scores with those of other works and found that ours significantly outperforms them.

关键词： Training Tuberculosis Databases Computational modeling computer architecture X-rays Feature extraction

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Fingerprint Recognition System Based on XGBoost Matching Algorithm for Forensic Analysis 2

Fingerprint Recognition System Based on XGBoost Matching Alg...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Adnan, Myasar Mundher Priyanka, K. Lande, Ayapal Swamy, Illa Mahesh Kumar Revathi, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India Anurag University Department of Information Technology Hyderabad India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In modern era, fingerprint authentication systems are used for easy and low-cost establishment of security which are mostly computationally complex and inaccurate. Therefore, this research utilizes highly efficient technique Oriented FAST and Rotated BRIEF with eXtreme Gradient Boosting (ORB-XGBoost) algorithm for fingerprint recognition. The input data is segmented to give clear area of impression, Gabor filter is employed to provide rotation, scale-invariance by retaining necessary information then the data is binarized for easy processing. The features are extracted by Oriented FAST and Rotated BRIEF (ORB) method which utilizes Features from Accelerated and Segments Test (FAST) and Binary Robust Independent Elementary Feature (BRIEF) techniques. The FAST algorithm is used for key point extraction to determine edges of an image and BRIEF algorithm converts the extracted key points into binary feature vector for faster computation. The feature vectors are given as input to XGBoost algorithm for feature matching which is highly efficient in classification with majority voting phenomena and avoids overfitting problem with regularization concept. The performance of proposed ORB-XGBoost method is evaluated by FVC 2002 DB1 dataset which achieved 96.32% accuracy. The proposed method attained accurate results when compared against Support Vector Machine-Genetic Algorithm (SVM-GA), Random Sample Consensus (RANSAC). © 2024 IEEE.

关键词： Gabor filters

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Enhancing Security in Cloud Registration with Multi Dimensional Features Fusion 5

Enhancing Security in Cloud Registration with Multi Dimensio...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mutha, Rakhi Reddy Gantla, Harish Pandey, Sunil Kr Sayeed Ahmad, Sayed Ahuja, Vandana Bhattacherjee, Abhishek Mamodiya, Udit Dept. of Information Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Information Technology Institute of Technology & Science Uttar Pradesh Ghaziabad India School of Engineering and Computing De Montfort University Dubai United Arab Emirates Dept. of CSE Swami Vivekanand Institute of Engineering and Technology Punjab Ramnagar India Department of Computer Science Engineering Lovely Professional University Punjab India Poornima University Jaipur India

ISBN: (纸本)9798400709418

Data mining uses artificial intelligence and other methods to extract hidden potential information from large data sets, which provides an effective way to obtain valuable knowledge from a large amount of information. Data mining is also ubiquitous in the process of using deep learning to solve point cloud registration tasks. Global feature extraction and rigid body transformation estimation are two critical stages of uncorrelated point cloud registration. Mining the rich information hidden in the two stages is one of the essential tasks of point cloud registration. However, the recently proposed methods tend to ignore low-dimensional local features when extracting global features, resulting in the loss of a large amount of point cloud information, which makes the accuracy of solving transformation parameters in the subsequent rigid body transformation estimation stage unsatisfactory. In this paper, a feature mining network based on multi-dimensional information fusion is proposed, which thoroughly mines the high-dimensional global and low-dimensional local information in the point cloud, and effectively makes up for the lack of local features in the worldwide feature extraction stage of point cloud registration. Dual quaternions are used to estimate pose in the rigid body transformation estimation stage, which can simultaneously represent rotation and translation in a standard frame, providing a compact and accurate representation for pose estimation. Finally, extensive experiments on the ModelNet40 dataset show that compared with the existing state-of-the-art point cloud registration methods without corresponding points, the proposed method can achieve higher accuracy and is more robust to noise. © 2023 ACM.

关键词： Data mining

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Plant Disease Detection using Region-Based Convolutional Neural Network

arXiv

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arXiv 2023年

作者： Rehana, Hasin Ibrahim, Muhammad Ali, Md. Haider Dept. of Computer Science and Engineering University of Dhaka Bangladesh

Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. Early detection of plant diseases and proper usage of pesticides and fertilizers are vital for preventing the diseases and boost the yield. Most of the farmers use generalized pesticides and fertilizers in the entire fields without specifically knowing the condition of the plants. Thus the production cost oftentimes increases, and, not only that, sometimes this becomes detrimental to the yield. Deep Learning models are found to be very effective to automatically detect plant diseases from images of plants, thereby reducing the need for human specialists. This paper aims at building a lightweight deep learning model for predicting leaf disease in tomato plants. By modifying the region-based convolutional neural network, we design an efficient and effective model that demonstrates satisfactory performance on a benchmark dataset. Our proposed model can easily be deployed in a larger system where drones take images of leaves and these images will be fed into our model to know the health condition. © 2023, CC BY-NC-ND.

关键词： Object detection

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Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance 63

Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

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63rd IEEE Annual Symposium on Foundations of computer science, FOCS 2022

作者： Das, Debarati Gilbert, Jacob Hajiaghayi, Mohammad Taghi Kociumaka, Tomasz Saha, Barna Saleh, Hamed Pennsylvania State University Dept. of Computer Science and Engineering State CollegePA United States University of Maryland Dept. of Computer Science College ParkMD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany University of California San Diego Halicioglu Data Science Institute Dept. of Computer Science and Engineering San DiegoCA United States

ISBN: (纸本)9781665455190

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann;TALG'10);however, Mao (FOCS'21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an O(n+k2)-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica'86) and Landau and Vishkin (JCSS'88). The existence of an O(n+poly(k))-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP'21), who give a stateof-the-art O(nk2)-time algorithm. In this paper, we answer this question positively. © 2022 IEEE.

关键词： Trees (mathematics)

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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Adding a Tail in Classes of Perfect Graphs

arXiv

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arXiv 2023年

作者： Mpanti, Anna Nikolopoulos, Stavros D. Palios, Leonidas Dept. of Computer Science and Engineering University of Ioannina Greece

Consider a graph G which belongs to a graph class C. We are interested in connecting a node w ∈ V (G) to G by a single edge uw where u ∈ V (G);we call such an edge a tail. As the graph resulting from G after the addition of the tail, denoted G + uw, need not belong to the class C, we want to compute a minimum C-completion of G + w, i.e., the minimum number of non-edges (excluding the tail uw) to be added to G + uw so that the resulting graph belongs to C. In this paper, we study this problem for the classes of split, quasi-threshold, threshold, and P4-sparse graphs and we present linear-time algorithms by exploiting the structure of split graphs and the tree representation of quasi-threshold, threshold, and P4-sparse graphs. © 2023, CC BY.

关键词： Clustering algorithms

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