Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.Comment: This paper is currently under review at ICCIT 202

Identifying Lung Cancer Using CT Scan Images Based On Artificial Intelligence

Lung cancer appears to be the common reason behind the death of human beings at some stage on the planet. Early detection of lung cancers can growth the possibility of survival amongst human beings. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected in time. Although computerized tomography (CT) is frequently more efficient than X-ray. However, the problem regarded to merge way to time constraints in detecting this lung cancer concerning the numerous diagnosing strategies used. Hence, a lung cancer detection system that usage of image processing is hired to categorize lung cancer in CT images. In image processing procedures, procedures like image pre-processing, segmentation, and have extraction are mentioned intimately. This paper is pointing to set off the extra precise comes approximately through making use of distinctive improve and department procedures. In this proposal paper, the proposed method is built in some filter and segmentation that pre-process the data and classify the trained data. After the classification and trained WONN-MLB method is used to reduce the time complexity of finding result. Therefore, our research goal is to get the maximum result of lung cancer detection

A Secured Model of IoT-based Smart Gas Detecting and Automatic Alarm System

A gas leakage detector is a device for detecting gases in an area that is often used in a security system. This type of equipment is used to detect gas leakage or another emission. A gas warning device can alert operators in the vicinity of a possible gas leak and enable them to escape. The device is important because many gases can be harmful to organic life, such as humans or animals. This can be used to detect flammable, flammable, and toxic gases, as well as a lack of oxygen. Identifying potentially dangerous gas leaks through sensors. These sensors often use an audible alarm to alert people when dangerous gas has been detected. The purpose of this paper is to propose and discuss the design of an IoT-based gas leakage detection system that can automatically detect and warn gas leaks. The proposed system also includes a warning system for users. The system is based on sensors that can easily detect gas leaks

Effect of Polyamine on Pigmentation, Reactive Oxidative Species and Antioxidant under Drought in Maize (Zea mays L.)

To examine polyamines (PAs) effect in modulating the drought induced by polyethylene glycol (PEG) in maize (Zea mays L.) seedlings (variety Khoibhutta, 8 day seedlings grown in petri dish in incubator) were subjected to 20% PEG (polyethylene glycol) followed by 20 µmol PAs, Putrescine (Put), Spermidine (Spd) and Spermine (Spm) with PEG solution for 48 hours. Sharp decrease in Relative Water Content (RWC), Chl a, Chl b, carotenoid (Car) and total pigment content was observed under drought compared to control condition, while PAs application reversed their decreasing trends. PEG significantly increased Reactive Oxidative Species (ROS) [superoxide (O2•−) and H2O2], Methyl Glyoxal (MG), Melondialdehyde (MDA) and Lipoxigenase (LOX) activity, while Pas decreased the contents considerably (except MG) as compared to those under drought. Drought increased proline content, which was further augmented in PA treatments. PAs failed to incline glyoxalase’s (Gly-I and Gly-II) activities, reduced under PEG. The activity and western blot confirmed the accumulation of Glutathione S-Transferase (GST) under drought, but PAs failed to augment the activity. Ascorbic Acid (AsA) and Glutathione (GSH) got oxidized into Dehydroascorbate (DHA) and oxidized Glutathione (GSSG) under drought but PAs effectively maintained homeostasis. Superoxide Dismutase (SOD), Peroxidase (POD), Ascorbate Peroxidase (APX), Glutathione Peroxidase (GPX), Monodehydroascorbatereductase (MDHAR), Dehydroascorbatereductase (DHAR), and Glutathione Reductase (GR) inclined in drought stressed seedlings, while Catalase (CAT) activity decreased under drought. PAs addition increased SOD, POD, GPX, CAT, MDHAR, and GR activities, but declined DHAR activity. These findings suggested important role of PAs in increasing tolerance under short term drought by modulating antioxidant effect

Identifying Lung Cancer Using CT Scan Images Based on Artificial Intelligence

Lung cancer appears to be the common reason behind the death of human beings at some stage on the planet. Early detection of lung cancers can growth the possibility of survival amongst human beings. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected in time. Although computerized tomography (CT) is frequently more efficient than X-ray. However, the problem regarded to merge way to time constraints in detecting this lung cancer concerning the numerous diagnosing strategies used. Hence, a lung cancer detection system that usage of image processing is hired to categorize lung cancer in CT images. In image processing procedures, procedures like image pre-processing, segmentation, and have extraction are mentioned intimately. This paper is pointing to set off the extra precise comes approximately through making use of distinctive improve and department procedures. In this proposal paper, the proposed method is built in some filter and segmentation that pre-process the data and classify the trained data. After the classification and trained WONN-MLB method is used to reduce the time complexity of finding result. Therefore, our research goal is to get the maximum result of lung cancer detection

A Secured Model of IoT-based Smart Gas Detecting and Automatic Alarm System

A gas leakage detector is a device for detecting gases in an area that is often used in a security system. This type of equipment is used to detect gas leakage or another emission. A gas warning device can alert operators in the vicinity of a possible gas leak and enable them to escape. The device is important because many gases can be harmful to organic life, such as humans or animals. This can be used to detect flammable, flammable, and toxic gases, as well as a lack of oxygen. Identifying potentially dangerous gas leaks through sensors. These sensors often use an audible alarm to alert people when dangerous gas has been detected. The purpose of this paper is to propose and discuss the design of an IoT-based gas leakage detection system that can automatically detect and warn gas leaks. The proposed system also includes a warning system for users. The system is based on sensors that can easily detect gas leaks

Prevalence and Antifungal Susceptibility of Clinically Relevant Candida Species, Identification of Candida auris and Kodamaea ohmeri in Bangladesh

Candida species are major fungal pathogens in humans. The aim of this study was to determine the prevalence of individual Candida species and their susceptibility to antifungal drugs among clinical isolates in a tertiary care hospital in Bangladesh. During a 10-month period in 2021, high vaginal swabs (HVSs), blood, and aural swabs were collected from 360 patients. From these specimens, Candida spp. was isolated from cultures on Sabouraud dextrose agar media, and phenotypic and genetic analyses were performed. A total of 109 isolates were recovered, and C. albicans accounted for 37%, being derived mostly from HVSs. Among non-albicans Candida (NAC), C. parapsilosis was the most frequent, followed by C. ciferrii, C. tropicalis, and C. glabrata. Three isolates from blood and two isolates from aural discharge were genetically identified as C. auris and Kodamaea ohmeri, respectively. NAC isolates were more resistant to fluconazole (overall rate, 29%) than C. albicans (10%). Candida isolates from blood showed 95% susceptibility to voriconazole and less susceptibility to fluconazole (67%). Two or three amino acid substitutions were detected in the ERG11 of two fluconazole-resistant C. albicans isolates. The present study is the first to reveal the prevalence of Candida species and their antifungal susceptibility in Bangladesh

Nationwide Distribution of Dengue Virus Type 3 (DENV-3) Genotype I and Emergence of DENV-3 Genotype III during the 2019 Outbreak in Bangladesh

Bangladesh is an endemic region of dengue fever and experienced an unprecedented large outbreak with more than 100,000 confirmed cases in 2019. To understand the prevalence of dengue antibody in patients and molecular epidemiological characteristics of dengue virus (DENV) in this outbreak, a total of 179 blood samples were collected from patients in 10 districts (seven divisions) covering nearly the whole country from August to December 2019. DENV NS-1 was detected in 162 samples, among which DENV-specific IgM was positive in 119 samples (73.5%), including 60.5% samples also positive for DENV-specific IgG. Sequencing of the partial C-prM gene and its phylogenetic analysis revealed predominance of DENV type 3 genotype I, accounting for 93% of samples examined. DENV-3 genotype III was identified in two samples from separate districts, and only one DENV-2 cosmopolitan genotype was found in the capital city, Dhaka. These findings suggest the predominance of DENV-3 genotype I and occurrence of DENV-3 genotype III, associated with increased incidence of recent secondary infection in Bangladesh in 2019