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  1. Article ; Online: Automatic driver distraction detection using deep convolutional neural networks

    Md. Uzzol Hossain / Md. Ataur Rahman / Md. Manowarul Islam / Arnisha Akhter / Md. Ashraf Uddin / Bikash Kumar Paul

    Intelligent Systems with Applications, Vol 14, Iss , Pp 200075- (2022)

    2022  

    Abstract: Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the ...

    Abstract Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the driver's behavior during the driving time to detect the distraction and mitigate the number of road accident. To detect various kinds of behavior like- using cell phone, talking to others, eating, sleeping or lack of concentration during driving; machine learning/deep learning can play significant role. However, this process may need high computational capacity to train the model by huge number of training dataset. In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face and hand localization. Four architectures namely CNN, VGG-16, ResNet50 and MobileNetV2 have been adopted for transfer learning. To verify the effectiveness, the proposed model is trained with thousands of images from a publicly available dataset containing ten different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained MobileNetV2 model has the best classification efficiency.
    Keywords Driver distraction ; Deep learning ; Convolutional neural network ; Transfer learning ; Resnet50 ; MobileNetV2 ; Cybernetics ; Q300-390 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 380
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: DTLCx

    Md. Khabir Uddin Ahamed / Md Manowarul Islam / Md. Ashraf Uddin / Arnisha Akhter / Uzzal Kumar Acharjee / Bikash Kumar Paul / Mohammad Ali Moni

    Diagnostics, Vol 13, Iss 551, p

    An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images

    2023  Volume 551

    Abstract: COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing ... ...

    Abstract COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.
    Keywords coronavirus (COVID-19) ; respiratory syndrome ; convolutional neural network ; pneumonia diagnosis ; deep learning ; healthcare professionals ; Medicine (General) ; R5-920
    Subject code 006
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: DeepCrop

    Md. Manowarul Islam / Md Abdul Ahad Adil / Md. Alamin Talukder / Md. Khabir Uddin Ahamed / Md Ashraf Uddin / Md. Kamran Hasan / Selina Sharmin / Md. Mahbubur Rahman / Sumon Kumar Debnath

    Journal of Agriculture and Food Research, Vol 14, Iss , Pp 100764- (2023)

    Deep learning-based crop disease prediction with web application

    2023  

    Abstract: Agriculture plays a significant role in every nation's economy by producing crops. Plant disease identification is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases ... ...

    Abstract Agriculture plays a significant role in every nation's economy by producing crops. Plant disease identification is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector and to prevent wasting money and other resources. Various diseases that could affect a plant cause crop farmers to lose a substantial sum yearly. Deep learning can play a crucial role in helping farmers prevent crop failure by early disease detection in plant leaves. In the experiment, we examined CNN, VGG-16, VGG-19 and ResNet-50 models on plant-village 10000 image dataset to detect crop infection and got the accuracy rate of 98.60%, 92.39%, 96.15%, and 98.98% for CNN, VGG-16, VGG-19 and ResNet-50 respectively. The study indicates that ResNet-50 outperforms the other models with an accuracy of 98.98%. So, the ResNet50 model was chosen to be developed into a smart web application for real-life crop disease prediction. The proposed web application aims to assist farmers in identifying diseases of plants by analyzing photos of the plant leaves. The proposed application uses the ResNet50 transfer learning model at its heart to distinguish healthy and infected leaves and classify the present disease type. The goal is to help farmers save resources and prevent economic loss by detecting plant diseases early and applying the appropriate treatment.
    Keywords Agriculture ; Plant ; Disease ; Farmers ; Crop ; Transfer Learning ; Agriculture (General) ; S1-972 ; Nutrition. Foods and food supply ; TX341-641
    Subject code 580
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Analysis of gene network model of Thyroid Disorder and associated diseases

    Md Kawsar / Tasnimul Alam Taz / Bikash Kumar Paul / Shahin Mahmud / Md Manowarul Islam / Touhid Bhuyian / Kawsar Ahmed

    Informatics in Medicine Unlocked, Vol 20, Iss , Pp 100381- (2020)

    A bioinformatics approach

    2020  

    Abstract: Chronic Kidney Disease (CKD), High Blood Pressure (HBP), and Thyroid Disorder (TD) diseases are interrelated. When human patients are affected by one of them, then the possibility of affectness by the other two diseases is increased. Background studies ... ...

    Abstract Chronic Kidney Disease (CKD), High Blood Pressure (HBP), and Thyroid Disorder (TD) diseases are interrelated. When human patients are affected by one of them, then the possibility of affectness by the other two diseases is increased. Background studies indicate that there are large numbers of similar biological and genetic features among HBP, CKD, and TD. For this reason, the common gene network models among these three diseases are explored. The gene number is reduced through preprocessing and filtering. Then the common genes among the selected diseases and the most significant genes are explored. After completing this process, ten common genes among HBP, CKD, and TD are recognized. This analysis identifies the most significant hub proteins based on biological, biochemical, and genetic relationships between common genes. Following these relationships, the Protein-Protein Interactions network, Co-Expression network, Enrichment Analysis, Topological properties analysis, Gene regulatory network, and Physical Interaction network are exhibited. This analysis helps us to identify similar biological and genetic features among HBP, CKD, and TD. Interaction of proteins with drug molecules enables an efficient drug design for this research. These drugs can be considered for further verification by chemical experiments.
    Keywords High blood pressure ; Chronic kidney disease ; Thyroid disorders ; Co-expression network ; Topological properties analysis ; Gene regulatory network ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 570
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Energy Efficient Fitness Based Routing Protocol for Underwater Sensor Network

    Md. Ashrafuddin / Md. Manowarul Islam / Md. Mamun-or-Rashid

    International Journal of Intelligent Systems and Applications, Vol 5, Iss 6, Pp 61-

    2013  Volume 69

    Abstract: Underwater sensor network is one of the potential research arenas that opens the window of pleasing a lot of researcher in studying the field. Network layer of the underwater sensor networks must be one of the most attractive fields to build up anew ... ...

    Abstract Underwater sensor network is one of the potential research arenas that opens the window of pleasing a lot of researcher in studying the field. Network layer of the underwater sensor networks must be one of the most attractive fields to build up anew protocol. In this paper, we proposed a underwater sensor network routing protocol named Energy Efficient Fitness based routing protocol for under water sensor networks (EEF) that promises the best use of total energy consumptions. The proposed routing protocol takes into account residual energy, depth and distance from the forwarding node to the sink node to guide a packet from source to the destination node. The prominent advantages of the proposed protocol are to confirm higher network life time and less end to end delay. The proposed protocol does not use control packet that causes much energy consumption and end to end delay. Simulation has been performed to certify the better result of the proposed routing protocol.
    Keywords UWSNs ; Fitness ; Holding Time ; Heuristic Function ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q ; DOAJ:Computer Science ; DOAJ:Technology and Engineering
    Subject code 003
    Language English
    Publishing date 2013-05-01T00:00:00Z
    Publisher MECS Publisher
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: MCCM

    Md. Manowarul Islam / Md. Abdur Razzaque / Md. Ashraf Uddin / A.K.M Kamrul Islam

    International Journal of Information Technology and Computer Science , Vol 6, Iss 6, Pp 9-

    Multilevel Congestion Avoidance and Control Mechanism for Mobile Ad Hoc Networks

    2014  Volume 18

    Abstract: Congestion in Mobile Ad Hoc Network causes packet loss, longer end-to-end data delivery delay which affects the overall performance of the network significantly. To ensure high throughput, the routing protocol should be congestion adaptive and should be ... ...

    Abstract Congestion in Mobile Ad Hoc Network causes packet loss, longer end-to-end data delivery delay which affects the overall performance of the network significantly. To ensure high throughput, the routing protocol should be congestion adaptive and should be capable of handling the congestion. In this research work, we propose a Multilevel Congestion avoidance and Control Mechanism (MCCM) that exploits both congestion avoidance and control mechanism to handle the congestion problem in an effective and efficient way. MCCM is capable of finding an energy efficient path during route discovery process, provide longer lifetime of any developed route. The efficient admission control and selective data packet delivery mechanism of MCCM jointly overcome the congestion problem at any node and thus, MCCM improves the network performance in term of packet delivery ratio, lower data delivery delay and high throughput. The result of performance evaluation section shows that, MCCM outperforms the existing routing protocols carried out in Network Simulator-2(NS-2).
    Keywords Mobile Ad Hoc Network ; Energy Efficient Routing ; Path Usability ; Congestion Control ; Admission Control ; Selective Packet Delivery ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Subject code 000
    Language English
    Publishing date 2014-05-01T00:00:00Z
    Publisher MECS Publisher
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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