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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin md.al mehedi hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of Computer Science and Engineering The University of AizuAizuwakamatsu965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

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Computers, Materials & Continua 2022年第9期72卷 5179-5195页

作者： md.Maniruzzaman Jungpil Shin md.al mehedi hasan Akira Yasumura School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Faculty of Humanities and Social Science Kumamoto UniversityChuo-kuKumamotoJapan

Attention deficit hyperactivity disorder(ADHD)is one of the most common psychiatric and neurobehavioral disorders in children,affecting 11%of children *** study aimed to propose a machine learning(ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography(EEG)*** study included 61 children with ADHD and 60 healthy children aged 7–12 *** morphological and time-domain features were extracted from EEG *** t-test(p-valueal features of children with ADHD and enhance the classification *** selected potential features were used in four ML-based algorithms,including support vector machine(SVM),k-nearest neighbors,multilayer perceptron(MLP),and logistic regression,to classify ADHD and healthy *** overall prevalence of boys and girls with ADHD was 48.9%and 56.5%,*** average age of children with ADHD was 9.6±1.8 *** results illustrated that the combination of LASSO with SVM classifier achieved the highest accuracy of 94.2%,sensitivity of 93.3%,F1-score of 91.9%,and AUC of *** results also illustrated that MLP was the second-best ML-based classifier,which gave 93.4%accuracy,91.7%sensitivity,91.1%F1-score,and 0.960 *** findings indicated that the combination of the LASSO-based feature selection method and SVM classifier can be a useful tool for selecting reliable/potential features and classifying ADHD and healthy *** proposed ML-based algorithms could be useful for the early diagnosis of children with ADHD.

关键词： ADHD feature extraction feature selection classification machine learning

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Rotation,Translation and Scale Invariant Sign Word Recognition Using Deep Learning

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Computer Systems Science & Engineering 2023年第3期44卷 2521-2536页

作者： Abu Saleh Musa Miah Jungpil Shin md.al mehedi hasan md Abdur Rahim Yuichi Okuyama School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science and Engineering Pabna University of Science and TechnologyPabnaBangladesh

Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious *** of the main functions of sign language is to communicate with each other through hand *** of hand gestures has become an important challenge for the recognition of sign *** are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance *** resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word ***,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version *** we have applied the gesture segmentation *** segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed ***,our designed CNN model has been trained to classify the hand gesture as well as the sign *** model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these *** achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset ***,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.

关键词： Sign word recognition convolution neural network(cnn) rotation translation and scaling(rts) otsu segmentation

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Zoonotic human liver flukes, a type 1 biocarcinogen, in freshwater fishes: genetic analysis and confirmation of molluscan vectors and reservoir hosts in Bangladesh

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贫困所致传染病(英文) 2024年第3期13卷 1-16页

作者： Labony Sharmin Shahid alim md. Abdul hasan Muhammad mehedi Hossain md. Shahadat Akter Sharmin Paul Joydeep Farjana Thahsin ali md. Haydar alam Mohammad Zahangir Hatta Takeshi Kawada Hayato Mizutani Keiko Tsuji Naotoshi Anisuzzaman Department of Parasitology Faculty of Veterinary Science Bangladesh Agricultural University Mymensingh Bangladesh Department of Fisheries Technology Faculty of Fisheries Bangladesh Agricultural University Mymensingh Bangladesh Department of Biotechnology School of Life Sciences &amp Biotechnology Adamas University Barasat - Barrackpore Rd Jagannathpur Kolkata West Bengal India Department of Pathology and Parasitology Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Parasitology and Tropical Medicine Kitasato University School of Medicine-15-Kitasato Minami Sagamihara Kanagawa-0374 Japan Department of Molecular and Cellular Parasitology Kitasato University Graduate School of Medical Sciences-15-Kitasato Minami Sagamihara Kanagawa-0373 Japan

Background Opisthorchiid flukes, particularlyOpisthorchis viverrini,Opisthorchis felineus,Clonorchis sinensis, andMetorchis spp. are the most common fish-borne zoonotic human liver flukes (hLFs). Liver fluke infections are more prevalent in resource-deprived and underprivileged areas. We herein estimated the prevalence of the metacercariae (MC) of major hLFs in common large freshwater fishes (lFWF) marketed for human consumption from some selected areas of Bangladesh along with detection of their molluscan vectors and *** The current status of fish-borne zoonotic hLF infections in lFWF was investigated along with their molluscan vectors and mammalian reservoir hosts in Mymensingh and Kishoreganj in Bangladesh from July 2018–June 2022 using conventional and multiple molecular techniques, such as PCR, PCR-restriction fragment length polymorphism (RFLP), sequencing, and bioinformatic analyses. The infection rate of fishes was analyzed using theZ-test and the loads of MC were compared using the chi-squared (χ2) *** The MC ofC. sinensis,Opisthorchis spp., andMetorchis spp. were detected in 11 species of common and popular lFWF. In lFWF, the estimated prevalence was 18.7% and the mean load was 137.4 ± 149.8 MC per 100 g of fish. The prevalence was the highest (P almost equally (P > 0.05) distributed between the head and body of fishes. The infection rate was slightly higher in cultured (19.6%) fishes. The MC ofC. sinensis,O. felineus,O. viverrini, andMetorchis orientalis in fishes were confirmed using PCR, PCR-RFLP and bioinformatics. The cercariae of opisthorchiid (Pleurolophocercus cercariae) flukes were only recovered fromBithynia spp. (3.9%, 42 out of 1089). The ova of hLFs from dogs (4.3%, 5 out of 116) and cats (6.0%, 6 out of 100), and adult flukes (***) from ducks

关键词： Human liver fluke Freshwater fish Molluscan vector Reservoir host Bangladesh

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Insights into Manipulation: Unveiling Tampered Images Using Modified ELA, Deep Learning, and Explainable AI

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Journal of Computer and Communications 2024年第6期12卷 135-151页

作者： md. mehedi hasan md. Masud Rana Abu Sayed md. Mostafizur Rahaman Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task for maintaining the integrity of digital content. This thesis explores the use of Modified Error Level Analysis (ELA) in combination with a Convolutional Neural Network (CNN), as well as, Feedforward Neural Network (FNN) model to detect digital image forgeries. Additionally, incorporation of Explainable Artificial Intelligence (XAI) to this research provided insights into the process of decision-making by the models. The study trains and tests the models on the CASIA2 dataset, emphasizing both authentic and forged images. The CNN model is trained and evaluated, and Explainable AI (SHapley Additive exPlanation— SHAP) is incorporated to explain the model’s predictions. Similarly, the FNN model is trained and evaluated, and XAI (SHAP) is incorporated to explain the model’s predictions. The results obtained from the analysis reveals that the proposed approach using CNN model is most effective in detecting image forgeries and provides valuable explanations for decision interpretability.

关键词： IFD DIF ELA CNN FNN XAI SHAP CASIA2.0

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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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Computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin md.almehedi hasan md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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Gradient Optimizer algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.mehedi Shaikh Abdul Latif md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Genome-wide identification,characterization and functional prediction of the SRS gene family in sesame(Sesamum indicum L.)

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Oil Crop Science 2024年第2期9卷 69-80页

作者： Farjana Afroz Susmi Tasmina Islam Simi md Nahid hasan md Abdur Rahim Department of Genetics and Plant Breeding Sher-e-Bangla Agricultural UniversityDhaka1207Bangladesh

Sesame(Sesamum indicum L.)is an ancient oilseed crop of the Pedaliaceae family with high oil content and potential health *** RELATED SEQUENCE(SRS)proteins are the transcription factors(TFs)specific to plants that contain RING-like zinc finger domain and are associated with the regulation of several physiological and biochemical *** also play vital roles in plant growth and development such as root formation,leaf development,floral development,hormone biosynthesis,signal transduction,and biotic and abiotic stress ***,the SRS gene family was not reported in sesame *** this study,identification,molecular characterization,phylogenetic relationship,cis-acting regulatory elements,protein-protein interaction,syntenic relationship,duplication events and expression pattern of SRS genes were analyzed in *** identified total six SiSRS genes on seven different linkage groups in the *** genome by comparing with the other species,including the model plant Arabidopsis *** SiSRS genes showed variation in their structure like2–5 exons and 1–4 *** other species,SiSRS proteins also contained‘RING-like zinc finger'and‘LRP1'***,the SiSRS genes were clustered into subclasses via phylogenetic analysis with proteins of ***,***,and some other plant *** cis-acting regulatory elements analysis revealed that the promoter region of SiSRS4(SIN_1011561)showed the highest 13 and 16 elements for light-and phytohormone-responses whereas,SiSRS1(SIN_1015187)showed the highest 15 elements for *** ABREs,or ABA-responsive elements,were found in a maximum of 8 copies in the SiSRS3(SIN 1009100).Moreover,the available RNA-seq based expression of SiSRS genes revealed variation in expression patterns between stress-treated and non-treated samples,especially in drought and salinity conditions *** SiSRS genes like SiSRS1(SIN_1015187)and SiSRS5(SIN_1021065),also exhibited variabl

关键词： SiSRS gene family SHI Transcription factor Characterization Sesamum indicum

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AutoML: A systematic review on automated machine learning with neural architecture search

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Journal of Information and Intelligence 2024年第1期2卷 52-81页

作者： Imrus Salehin md.Shamiul Islam Pritom Saha S.M.Noman Azra Tuni md.mehedi hasan md.Abu Baten Department of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan 47011Republic of Korea Department of Computer Science and Engineering Daffodil International UniversityDhaka 1216Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and TechnologyDhaka 1216Bangladesh Faculty of Computer Science and Engineering Frankfurt University of Applied SciencesFrankfurt 60318Germany Department of Computer Science and Engineering University of Asia PacificDhaka 1205Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka 1208Bangladesh Department of Computer Science and Engineering Northern University BangladeshDhaka 1215Bangladesh

AutoML(Automated Machine Learning)is an emerging field that aims to automate the process of building machine learning *** emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is *** particular,research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and *** this semantic review research,we summarize the data processing requirements for AutoML approaches and provide a detailed *** place greater emphasis on neural architecture search(NAS)as it currently represents a highly popular sub-topic within the field of *** methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given *** provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10,CIFAR-100,ImageNet and wellknown benchmark ***,we delve into several noteworthy research directions in NAS methods including one/two-stage NAS,one-shot NAS and joint hyperparameter with architecture *** discussed how the search space size and complexity in NAS can vary depending on the specific problem being *** conclude,we examine several open problems(SOTA problems)within current AutoML methods that assure further investigation in future research.

关键词： AutoML Neural architecture search Advance machine learning Search space Hyperparameter optimization

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Adequate Closed Form Wave Solutions to the Generalized KdV Equation in Mathematical Physics

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Journal of Applied Mathematics and Physics 2024年第6期12卷 2069-2082页

作者： md. Munnu Miah md. al Amin Meia md. Matiur Rahman Sarker Ahammodullah hasan Department of Mathematics Islamic University Kushtia Bangladesh Department of Mathematics Government Titumir College Dhaka Bangladesh Department of Pharmacy University of Asia Pacific Dhaka Bangladesh

In this paper, we consider the generalized Korteweg-de-Vries (KdV) equations which are remarkable models of the water waves mechanics, the shallow water waves, the quantum mechanics, the ion acoustic waves in plasma, the electro-hydro-dynamical model for local electric field, signal processing waves through optical fibers, etc. We determine the useful and further general exact traveling wave solutions of the above mentioned NLDEs by applying the exp(−τ(ξ))-expansion method by aid of traveling wave transformations. Furthermore, we explain the physical significance of the obtained solutions of its definite values of the involved parameters with graphic representations in order to know the physical phenomena. Finally, we show that the exp(−τ(ξ))-expansion method is convenient, powerful, straightforward and provide more general solutions and can be helping to examine vast amount of travelling wave solutions to the other different kinds of NLDEs.

关键词： The Generalized KdV Equation The exp(-τ(ξ)) -Expansion Method Travelling Wave Solitary Wave

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