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All Journal Bulletin of Electrical Engineering and Informatics
Ara, Iffat
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Bangla handwritten word recognition using YOLO V5 Hossain, Md. Anwar; Abadin, AFM Zainul; Faruk, Md. Omar; Ara, Iffat; Rashidul Hasan, Mirza AFM; Fatta, Nafiul; Asraful, Md; Hossen, Ebrahim
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6953

Abstract

This research paper presents an innovative solution for offline handwritten word recognition in Bengali, a prominent Indic language. The complexities of this script, particularly in cursive writing, often lead to overlapping characters and segmentation challenges. Conventional methodologies, reliant on individual character recognition and aggregation, are error-prone. To overcome these limitations, we propose a novel method treating the entire document as a coherent entity and utilizing the efficient you only look once (YOLO) model for word extraction. In our approach, we view individual words as distinct objects and employ the YOLO model for supervised learning, transforming object detection into a regression problematic to predict spatially detached bounding boxes and class possibilities. Rigorous training results in outstanding performance, with remarkable box_loss of 0.014, obj_loss of 0.14, and class_loss of 0.009. Furthermore, the achieved mAP_0.5 score of 0.95 and map_0.5:0.95 score of 0.97 demonstrates the model’s exceptional accuracy in detecting and recognizing handwritten words. To evaluate our method comprehensively, we introduce the Omor-Ekush dataset, a meticulously curated collection of 21,300 handwritten words from 150 participants, featuring 141 words per document. Our pioneering YOLO-based approach, combined with the curated Omor-Ekush dataset, represents a significant advancement in handwritten word recognition in Bengali.
Distributed denial-of-service attack detection short review: issues, challenges, and recommendations Ahasan Habib, AKM; Imtiaz, Ahmed; Tripura, Dhonita; Omar Faruk, Md.; Anwar Hossain, Md.; Ara, Iffat; Sarker, Sohag; Zainul Abadin, A F M
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8377

Abstract

An attacker can attack a network in several methods when there are a lot of device connections. Distributed denial-of-service (DDoS) attacks could result from this circumstance, which could damage resources and corrupt data. Therefore, irregularity in traffic data must be detected to identify malicious behavior in a network, which is critical for maintaining the integrity of current cyber-physical systems (CPS) as well as network security. This article attempts to study and compare various approaches to detecting DDoS attacks and expresses data paths for packet filtering for high-speed networks (HSN) performance, using machine or deep learning techniques used in intrusion detection systems (IDSs) and flow-based IDSs. The study presents a comprehensive DDoS attack taxonomy, categorizes detection strategies, and highlights the HSN accuracy assessment features. By exposing the problems and difficulties associated with DDoS attacks on HSN, several investigation paths are proposed to assist researchers in determining and developing the best solution.
Co-Authors Abadin, AFM Zainul Ahasan Habib, AKM Anwar Hossain, Md. Asraful, Md Faruk, Md. Omar Fatta, Nafiul Hossain, Md. Anwar Hossen, Ebrahim Imtiaz, Ahmed Omar Faruk, Md. Rashidul Hasan, Mirza AFM Sarker, Sohag Tripura, Dhonita Zainul Abadin, A F M