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The effects of teak monoculture on forest soils: a case study in Bangladesh

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Journal of Forestry Research 2018年第4期29卷 1100-1109页

作者： md.abdullah al mahmud Mohammad Mahfuzur Rahman Mohammed Kamal Hossain Institute of Forestry and Environmental Sciences University of Chittagong

Teak plantations date back to 1871 in Bangladesh. This study was designed to assess how teak monoculture has impacted the soil properties in Bangladesh. Multiple linear regression and correlation matrices were estimated to evaluate these impacts. The means of soil physicochemical properties were compared across various attributes of the plantations. Older plantations accumulated more organic carbon in the soils than the younger plantations. Excessive removal of litter from the forest floor resulted in reduced levels of N, P, K, and other nutrients in the soils. Since bulk density was continuously decreasing deeper into the soil, the penetration of mineral nutrients into the soil was low. Furthermore, moisture content in the topsoil was significantly lower than that in the bottom layers due to the exposed and dry condition of the forest floor under teak plantations. For improving depleted teak plantation soils, teak could be planted with other tree species rather than just in monocultures. In addition, the forest floor could be enriched with leguminous herbs and shrubs to improve soil health in these plantations.

关键词： CHTs Monoculture. Organic carbon Soilnutrients Teak plantation

A Security System for Kindergarten School Using RFID Technology

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Journal of Computer and Communications 2019年第4期7卷 21-30页

作者： Salma Nazia Rahman Shikder Shafiul Bashar md. abdullah al mahmud md. Sazal Miah A. H. M. Zadidul Karim Maliha Marium Department of Electrical and Electronic Engineering University of Asia Pacific Dhaka Bangladesh Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

An RFID (Radio Frequency Identification) based security and access control system is proposed in this paper. Recently, a crime against children is increasing in higher rate and it is high time to offer a safety system for the school going children. In this work, our aim is to build a security system to keep the student secure. This system provides a unique identification number that is RFID card. Generally, the RFID card will be scanned in an RFID reader. RFID reader will send data to Arduino where the database program is previously set. Arduino programs are written in C language. If anyone remains absent after school starts, the GSM module will send an automated SMS to his or her parents. Students may need to leave school for an emergency case. Then he/she can take special permission from the administration. There is a button set for the administration to open the gate immediately for this reason. Parents will be provided RFID card from the school authority. Because student card won’t work for the second time in a day. So parents will have to scan their card after school to receive their children. And their card will only work after school. Hence, the risks of any occurrence are reduced and ensured the safety of the student.

关键词： Radio Frequency Identification Ardunio Safety System GSM

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

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Computers, Materials & Continua 2024年第9期80卷 3485-3506页

作者： Tajim md.Niamat Ullah Akhund Waleed M.al-Nuwaiser Department of Computer Science and Engineering(CSE) Daffodil International UniversityDhaka1216Bangladesh Graduate School of Science and Engineering Saga UniversitySaga8408502Japan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

关键词： Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test

Stock appraisal for Atlantic tripletail(Lobotes surinamensis;Bloch, 1790) in the Bay of Bengal, Bangladesh

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Journal of Oceanology and Limnology 2024年第05期 1683-1694页

作者： Suman BARUA Qun LIU Ahmed Fazley RABBY md.abdullah al-MAMUN Xu CHEN Rokeya SULTANA Aidah BalOCH College of Fisheries Ocean University of China Department of Fisheries Ministry of Fisheries and Livestock Bangladesh Fisheries Research Institute Directorate of Secondary and Higher Education Ministry of Education Gwadar Development Authority

The Atlantic tripletail (Lobotes surinamensis) is a high revenue-generating fish species predominantly caught by mechanized artisanal fishers community and the most available member of its family in *** is a ground work of fish stock assessment study in the Bay of Bengal region to explore the life history parameters and associated biomass of this species,using three length-based approaches of TropFishR,the length-based Bayesian biomass estimation (LBB),and Froese’s lengthbased indicators (LBIs).An almost homogenous body growth pattern (b=3.07;R2=0.98) was observed in the length-weight relationship of *** life history parameters for tripletail,as determined by the von Bertalanffy Growth Function (VBGF) model,were L∞=113.36 cm and k=0.51/*** length converted catch curve (LCCC) yielded an estimation of the total mortality (Z=1.77/a),with the natural mortality estimated at (M=0.53/a) and the fishing mortality estimated at (F=1.24/a).But,the ratio of mortality(F/M=0.15) by LBB captured the non-fully exploited status of biomass (B/BMSY=2.1).LBI analysis indicated that the tripletail fishery’s spawning stock biomass is greater than the target and limit reference points,indicating a healthy state of biomass.

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Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah md.Siraj Bander ali Saleh al-rimy Sultan Noman Qasem Tawfik al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious *** threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware *** security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying *** can lead to missed attacks or an abundance of false alarms,impacting overall security *** address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-*** novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and ***-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud *** evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark *** results demonstrate that ACIDS-PELT outperf

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

Neural Network-Powered License Plate Recognition System Design

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Engineering（科研） 2024年第9期16卷 284-300页

作者： Sakib Hasan md Nagib Mahfuz Sunny abdullah al Nahian Mohammad Yasin School of Information and Electronics Beijing Institute of Technology Beijing China Department of Engineering and Technology Trine University Fort Wayne USA Department of Information Technology Management Westcliff University Irvine USA

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

关键词： Intelligent Traffic Control Systems Automatic License Plate Recognition (alPR) Neural Networks Vehicle Surveillance Traffic Management License Plate Recognition algorithms Image Extraction Character Segmentation Character Recognition Low-Light Environments Inclement Weather Empirical Findings algorithm Accuracy Simulation Outcomes Digitalization

Tuberculosis-diabetes comorbidities: Mechanistic insights for clinical considerations and treatment challenges

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World Journal of Diabetes 2024年第5期15卷 853-866页

作者： md Abdul alim al-Bari Nicholas Peake Nabil Eid Department of Pharmacy University of RajshahiRajshahi 6205Bangladesh Biosciences and Chemistry and Biomolecular Research Centre Sheffield Hallam UniversitySheffield S11WBUnited Kingdom Department of Anatomy Division of Human BiologySchool of MedicineInternational Medical UniversityKuala Lumpur 57000Malaysia

Tuberculosis(TB)remains a leading cause of death among infectious diseases,particularly in poor *** infections,multidrug-resistant and ex-tensively drug-resistant TB strains,as well as the coexistence of chronic illnesses such as diabetes mellitus(DM)greatly aggravate TB morbidity and ***[particularly type 2 DM(T2DM)]and TB have converged making their control even more *** contemporary global epidemics,TB-DM behaves like a syndemic,a synergistic confluence of two highly prevalent diseases.T2DM is a risk factor for developing more severe forms of multi-drug resistant-TB and TB recurrence after preventive *** a bidirectional relationship exists between TB and DM,it is necessary to concurrently treat both,and promote recommendations for the joint management of both *** are also some drug-drug interactions resulting in adverse treatment outcomes in TB-DM patients including treatment failure,and *** addition,autophagy may play a role in these ***,the TB-DM comorbidities present several health challenges,requiring a focus on multidisciplinary collaboration and integrated strategies,to effectively deal with this double *** effectively manage the comorbidity,further screening in affected countries,more suitable drugs,and better treatment strategies are required.

关键词： Diabetes mellitus Tuberculosis Coinfections Comorbidity Drug-drug interactions Autophagy Treatment challenges

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1689-1710页

作者： md.Abdur Rahim Fahmid al Farid Abu Saleh Musa Miah Arpa Kar Puza md.Nur alam md.Najmul Hossain Sarina Mansor Hezerul Abdul Karim Department of Computer Science and Engineering Pabna University of Science and TechnologyPabna6600Bangladesh Faculty of Engineering Multimedia UniversityCyberjaya63100Malaysia Department of Computer Science and Engineering Bangladesh Army University of Science and TechnologySaidpur5311Bangladesh Department of Mathematics Pabna University of Science and TechnologyPabna6600Bangladesh Department of Electrical Electronic and Communication EngineeringPabnaUniversity of Science and TechnologyPabna6600Bangladesh Department of Electrical and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Handwriting is a unique and significant human feature that distinguishes them from one *** are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through ***,such systems are susceptible to forgery,posing security *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and *** key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive ***-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite *** meticulous amalgamation resulted in a robust set of 91 *** enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent *** the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting ***,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual ***,our experimental results unde

关键词： Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM

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