Dangerous Type Research Articles

인명, 환경, 재산피해를 고려한 고위험 화학사고의 원인과 동향 분석

This study analyzed the causes and trends of chemical accidents that occurred in Korea from 2014 to 2022. In particular, by comprehensively considering human casualties, environmental damage, and property damage, the relative risks and accident trends of each type of accident were identified. Utilizing the relative risk, the most dangerous accident type for each type of accident were derived. And, LDA topic modeling and network analysis were used to analyze accident trends for fire accidents identified as the most dangerous accident types. Results showed that the most dangerous accident type in terms of human casualties was explosion accidents, and the type of accident with a large impact on the environment and property was analyzed as fire accidents. As a result of LDA topic modeling, chemical fire accidents were broadly classified into four major topics. It is possible to analyze the complex causes and patterns of chemical accidents through methods using LDA topic modeling and network analysis, and it is possible to effectively establish prevention and response strategies for chemical accidents through the methods used.

Advancing Melanoma Diagnosis: A Computer-Aided Diagnostic Approach for Enhanced Precision

Abstract: Melanoma, a dangerous type of skin cancer, is now the ninth most common cancer. Detecting it early is crucial for easier treatment. Dermoscopy, a common method that magnifies skin lesions for examination, relies on the skill of trained professionals. This limits its use to those with extensive training. To overcome this, our research suggests using a ComputerAided Diagnostic (CAD) system. This system has a user-friendly interface, making it accessible for dermatologists with limited training. Unlike traditional methods, our study shows that CAD not only supports skilled professionals but also provides a reliable second opinion in identifying melanoma. This research aims to improve the accuracy of melanoma diagnosis, making it more accessible and precise for better patient outcomes

Algorithm for Recognition of Small Air Targets by Trajectory Features in Passive Bistatic Radar

Introduction. In the past few years, the rapid development and widespread use of unmanned aerial vehicles (UAVs) for solving a variety of tasks has created new threats. The problem of ensuring the safety of protected objects, especially in the area of critically important objects or in places with difficult ornithological conditions (airports, wind power facilities), is of particular importance. In this regard, the issue of detecting small air targets and recognizing their type and degree of danger is acquiring greater importance. This paper presents an algorithm for recognizing air targets based onartificial intelligence technology. The results of a comparative analysis of decision-making methods for recognizing small UAVs and birds based on their trajectory features are presented. The results obtained can be used in the development of systems for recognizing classes of small airborne targets in existing and future radar stations. Aim. Development of an algorithm for recognizing small air targets by trajectory features based on machine learning. Implementation and evaluation of the quality of decision-making methods in a given recognition problem. Materials and methods. Experimental data on the trajectories of UAVs and birds obtained in a passive bistatic radar system are used. The trajectory parameters of the targets and their statistical characteristics are calculated; a set of features for recognition is formed. Using the MATLAB software package, a program for implementing the recognition algorithm and analyzing the quality of decision-making methods was developed. Results. An algorithm for recognizing air targets based on artificial intelligence technology is presented. A comparative analysis of the six most common recognition methods based on machine learning (Naïve Bayes, decision trees, k-nearest neighbors, neural network recognition algorithm, support vector machine, random forests) was carried out, which showed that, under the conditions of this problem, the most effective are k-nearest neighbor method and support vector machine. Conclusion. The presented methods can be used to directly determine the class of targets from a set of marks of their trajectories. Further research will be aimed at developing and implementing a real-time recognition algorithm.

К вопросу использования контрольных электродов для технического диагностирования протекторов морских судов

The world statistics of fleet accidents indicate that the most dangerous type of damage to engineering structures are fragile destructions that occur suddenly and spread at high speed. The causes of brittle fractures are defects such as corrosion-fatigue cracks, which often occur due to active corrosion processes. Seawater contains a large number of microorganisms that contribute to the acceleration of corrosion and fouling of metal structures in contact with water. The main method of corrosion protection of systems and mechanisms of marine fishing vessels is tread protection. To increase the effectiveness of tread protection, it is necessary to improve the methods of technical diagnostics of treads in order to use new methods at ship repair plants and marine vessels. It is proposed to use control electrodes made of stainless steel for technical diagnostics of marine vessel protectors. Laboratory tests of a stainless steel electrode and a standard silver chloride reference electrode were performed. The experiment took place at the installation for technical diagnostics of marine protectors, which was specially developed at the Department of “Power Plants and Electrical Equipment of Ships” of the Kamchatka State Technical University. Control measurements of the potential of the working protector were performed for five days, while 50 single control measurements of the working potential of the protector were performed daily using each electrode. The accuracy of the measurement results was evaluated using a mathematical and statistical method. The results of diagnosing the potential of the tread, obtained using an electrode made of stainless steel, comply with regulatory requirements. The proposed type of electrode can be used by ship crews for technical diagnostics of marine vessel protectors.

Performance analysis of deep transfer learning approaches in detecting and classifying brain tumor from magnetic resonance images

The Central Nervous System (CNS) is one of the most crucial parts of the human body. Brain tumor is one of the deadliest diseases that affect CNS and they should be detected earlier to avoid serious health implications. As it is one of the most dangerous types of cancer, its diagnosis is a crucial part of the healthcare sector. A brain tumor can be malignant or benign and its grade recognition is a tedious task for the radiologist. In the recent past, researchers have proposed various automatic detection and classification techniques that use different imaging modalities focusing on increased accuracy. In this paper, we have done an in-depth study of 19 different trained deep learning models like Alexnet, VGGnet, DarkNet, DenseNet, ResNet, InceptionNet, ShuffleNet, NasNet and their variants for the detection of brain tumors using deep transfer learning. The performance parameters show that NASNet-Large is outperforming others with an accuracy of 98.03% for detection and 97.87% for classification. The thresholding algorithm is used for segmenting out the tumor region if the detected output is other than normal.

Brain tumor classification and detection via hybrid alexnet-gru based on deep learning

Brain tumors are among the most dangerous types of brain cancer due to their aggressiveness. The development of aberrant brain tissue leads to brain tumors, which pose a major threat to people's health and welfare. Early detection of these malignancies is still fairly challenging, particularly when attempting to distinguish between various kinds including gliomas, meningioma, and pituitary tumors. The complexity of the brain's structure further increases the need for a speedy and accurate identification of irregularities. An innovative approach using a hybrid model that combines AlexNet and GRU (Gated Recurrent Unit) neural networks is presented to identify and characterize brain cancers using MRI data. The MRI images should first be sharpened and denoised using a non-local means filter to ensure the best input data. To avoid overfitting and achieve optimal model performance, the AlexNet architecture employs layers to extract specificfeatures from images. Model complexity and potential for generalization are regulated through hyperparameter modification. The GRU component resolves gradient vanishing in deep networks and uses the softmax activation function to categorize braintumorsinto four distinct classes. According to the findings, the model can categorize and diagnose brain cancers with 97% accuracy, 97.63% precision, a robust 96.78% recall rate, and an impressive 97.25% F1-Score. These findings show that theresearch has the potential to improve patient outcomes and create a more favorable healthcare environment in medical imaging and brain tumor detection.

Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO)

Skin Cancer (SC) is one of the most dangerous types of cancer and if not treated in time, it can threaten the patient’s life. With early diagnosis of this disease, treatment methods can be used more effectively and the progression of the disease can be prevented. Machine Learning (ML) techniques can be utilized as a useful and efficient tool for SCD. So far, various methods for automatic SCD based on ML techniques have been presented; However, this research field still requires the application of optimal and efficient models to increase the accuracy of SCD. Therefore, in this article, a new method for SCD using a combination of optimization techniques and Artificial Neural Networks (ANNs) is presented. The proposed method includes four steps: pre-processing, segmentation, feature extraction, and classification. Image segmentation for identifying the lesion region is performed using a Kohonen neural network, where the identified region of interest (ROI) is enhanced using the Greedy Search Algorithm (GSA). The proposed method, uses a Convolutional Neural Network (CNN) for extracting features from ROIs. Also, to classify features, an ANN is used, and by the Improved Gray Wolf Optimization (IGWO) algorithm, the number of neurons and weight vector are adjusted. In this method, a probabilistic model is used to improve the convergence speed of the GWO algorithm. Based on the evaluation results, using the IGWO model to optimize the structure and weight vector of the ANN can be effective in increasing the diagnosis accuracy by at least 5%. The results of implementing the proposed method and comparing its performance with previous methods also show that this method can diagnose SC in the ISIC-2016 and ISIC-2017 databases with an average accuracy of 97.09 and 95.17%, respectively; which improves accuracy by at least 0.5% compared to other methods.

Review on Automated Skin Cancer Detection Using Image Processing Techniques

Skin is the most important part of the human body, protecting the muscles, bones and the entire body. One of the most common diseases affecting many people today is cancer, and skin cancer is one of the most common cancers known to man. As one of the deadliest cancers, skin cancer requires special attention. Skin cancer is caused by genetic defects or mutations caused by unreduced deoxyribonucleic acid in skin cells. One of the most dangerous types of skin cancer is melanoma, which has a high survival rate if detected early. Accordingly, early detection of melanoma is very important. To this end, this paper presents a computer-aided method that uses image processing tools to detect skin cancer, especially melanoma. This system takes an image of a skin lesion and uses new image processing techniques to determine the presence of skin cancer. The lesion image analysis tool evaluates various features of melanoma, including asymmetry, contour, color, diameter (ABCD), and more. Then these extracted parameters are used to classify the images as benign or malignant lesions. This article describes methods and approaches for automatic skin cancer detection. This article provides researchers with useful information about the methods, databases, and procedures required for early detection of skin cancer.

Linear programming based computational technique for leukemia classification using gene expression profile.

Cancer is a serious public health concern worldwide and is the leading cause of death. Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer that affects the blood cell and bone marrow. Acute leukemia is a chronic condition that is fatal if left untreated. A timely, reliable, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. There are four types of leukemia, namely acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic in extracting, and chronic myelogenous leukemia. Recognizing these cancerous development cells is often done via manual analysis of microscopic images. This requires an extraordinarily skilled pathologist. Leukemia symptoms might include lethargy, a lack of energy, a pale complexion, recurrent infections, and easy bleeding or bruising. One of the challenges in this area is identifying subtypes of leukemia for specialized treatment. This Study is carried out to increase the precision of diagnosis to assist in the development of personalized plans for treatment, and improve general leukemia-related healthcare practises. In this research, we used leukemia gene expression data from Curated Microarray Database (CuMiDa). Microarrays are ideal for studying cancer, however, categorizing the expression pattern of microarray information can be challenging. This proposed study uses feature selection methods and machine learning techniques to predict and classify subtypes of leukemia in gene expression data CuMiDa (GSE9476). This research work utilized linear programming (LP) as a machine-learning technique for classification. Linear programming model classifies and predicts the subtypes of leukemia Bone_Marrow_CD34, Bone Marrow, AML, PB, and PBSC CD34. Before using the LP model, we selected 25 features from the given dataset of 22283 features. These 25 significant features were the most distinguishing for classification. The classification accuracy of this work is 98.44%.

Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification

Melanoma is one of the most dangerous types of skin cancer. Since 2018, the number of skin cancer cases in the US has increased and exceeded 100,000. Melanoma is the third most common cancer in Indonesia, following womb cancer and breast cancer. Standard detection of melanoma skin cancer biopsy is costly and time-consuming. The purpose of this research is to build and compare melanoma skin cancer detection using various Convolutional Neural Network method. This research used four CNN model architectures methods, VGG-16, LeNet, Xception, and MobileNet. The dataset for this research is image data that consists of 9605 data divided into benign and malignant classes. The data will be augmented to increase its quantity. After that, the data will be trained using four CNN architecture models and evaluated using the confusion matrix. The result of this study is that Xception model has the best accuracy and the lowest loss, with 93% accuracy and 19% loss, with precision 93%, recall 93,5%, and f1-score 93%. Whereas the other model, VGG-16 gives 90 % accuracy, 27% loss, LeNet 89,7% accuracy, 28% loss, and mobileNet 90,8% accuracy and 22,5% loss.

Tympanomastoidectomy with modified Bondy procedure on young patient with cholesteatoma and dangerous type of chronic suppurative otitis media (CSOM)

Tympanomastoidectomy is tympanoplasty with mastoidectomy. Modified with Bondy procedure of mastoidectomy is a type of canal wall down mastoidectomy which is rarely used about 5.8% patients of all mastoidectomy and pediatric cholesteatoma patients underwent modified with Bondy procedure. The purpose of the modified with Bondy procedure is for young patients, to maintain or improve hearing during surgery or next operations and to facilitate better wound care after surgery. A cholesteatoma is a lesion of the ear, formed of a mass of stratified keratinizing squamous epithelium. Cholesteatoma in the pediatric generally to be more aggressive than cholesteatoma occurring in the adult population. Cholesteatoma is usually followed by Chronic suppurative otitis media (CSOM) and results in hearing loss. It has been reported that a 15-year-old female patient with right dangerous type of CSOM and cholesteatoma underwent tympanomastoidectomy with Modified Bondy. Right dangerous type of CSOM and cholesteatoma can be diagnosed based on anamnesis, physical examination, and supporting examinations consisting of a CT scan and histopathology. The result of Modified tympanomastoidectomy with Bondy procedure on this patient was good.

Assessment of noise levels by using noise prediction modeling

The third-most dangerous type of pollution, after air and water pollution, according to the World Health Organization, is noise pollution. Brief and prolonged exposure to noise pollution can have negative consequences on people, including psychological disorders, including anxiety and depression, hypertension, hormonal imbalances, and a rise in blood pressure that can result in cardiovascular disease. The WHO estimates that up to 40% of individuals in Europe are currently exposed to loud noises. This study makes an effort to predict noise levels in and around the School of Architecture and Planning (SAP) campus using data on traffic volume and flow, vehicle speed, and geometric mean of the road. Additionally, it does a comparison between the expected and actual noise levels and offers workable noise reduction techniques. A mathematical model that takes into consideration has been used to forecast the equivalent noise level. By comparing the expected and actual noise levels, it was found that all values are beyond the permitted limits. Five different locations within SAP were used to assess the amount of noise present. The Lobby recorded the highest and lowest noise levels, respectively, at 75.63 and 74.15 dB (A). There were 73.05, 71.01, 71.81, and 70.5 dB (A) accordingly as the strongest noises in the classroom and auditorium. The maximum noise levels in the library was 63.76 and 64.54 dB (A), respectively. A maximum noise level of 75.29 and 68.14 dB (A) was recorded for the studio.

Engineering Low-Cost Banana Peel Derived Biochar for the Highly Adsorption Capacity of Metformin Hydrochloride Drug from Aqueous Solution on Chemical Activation

Adsorption is one of the easiest and best ways to purify water. In recent years, the adsorption process has been adopted to purify drinking water. Using adsorption technology to remove the most dangerous types of pharmaceuticals in drinking water, such as (metformin hydrochloride MF-HCl drug), and a primary goal of this empirical study utilizing banana peels as derived biochar activated carbon (ACBP) to remove MF-HCl drug. The FTIR, FE-SEM, XRD and TEM technique was utilized to estimate the surface characteristics before and after the adsorption process. The adsorption capacity Qe mg/g and percentage removed E% increase with increasing equilibrium time and, solution temperature and pH. The adsorption capacity Qe mg/g Qe mg/g decreases with increasing of adsorbent dosage. Thermodynamics including (ΔH), (ΔS) and (ΔG) are found to be endothermic and spontaneous. Results show that the MF-HCl drug adsorbed amount on ACBP was 147.59 mg/g. The adsorbent was treated via several acids like as (H3PO4, HNO3, HCl and H2SO4). The obvious from the results the best removal percentage E% when the adsorbent was treated via HCl. This is may be due to the increase in acid acidity caused by reactivating the active sites for the adsorbent surface. From the results obtained, ACBP are eco-friendly, extensive, and effective as an adsorbent, giving a promising prospect for removing wastewater.

To the Issue of Solid Municipal Waste in the Irkutsk Region

The article presents data on solid municipal waste (SMW) in the Irkutsk region as the main source of pollution of urban and suburban ecosystems. SMW is currently the most massive and dangerous type of waste, which can lead to irreversible consequences. In Russia, very active attention is being paid to the issues of waste collection and recycling in recent years. The objectives, which the authors set themselves, are a detailed analysis of the situation of SMW in the Irkutsk region. Special attention is focused on the location of already existing facilities as well as planned until 2027 ones. Unfortunately, the population of the Irkutsk region is not yet ready for mass separation of garbage according to its origin and recyclability, but a number of measures are proposed to change the situation. Statistical, comparative-geographical, cartographical and the method of comparative analysis and synthesis were used. The paper presents an analysis of the work of regional operators in accordance with the federal project Integrated system of SMW management carried out for the first time. The amount of waste taken to landfills in the Irkutsk region should be reduced by half by 2030 while sorted waste should gain 100 per cent of all garbage thrown away. The study results in the following conclusions: the region is not ready for processing and recycling of certain categories of waste such as glass or galvanic elements. There are simply no recycling facilities for a number of wastes, which leads to uncontrolled emissions and disturbance of the environment (soil, water, and atmospheric air) but the Government of Russia and the Irkutsk Region authorities are actively reforming the system of SMW management.

Factors Related to Hearing Threshold of Workers at PT. X

Physical hazards are one of the several types of dangers in Occupational Health and Safety. Companies or industries that use machines in their work processes have the potential to cause a physical hazard. One of the physical hazards is noise. Noise exposure can have an impact on employee health, namely hearing loss. A decrease in hearing threshold values can cause communication disorders and concentration problems. Variables of sound intensity, period of work, age, and disease history can influence the reduction in hearing threshold values. The results of a preliminary study on workers at PT. X indicates the sound intensity is above the threshold value, namely 86dB and 16.6% of workers experience hearing loss. The aim of this research is to determine the factors related to the hearing threshold values of workers at PT. X. The methodology in this research uses a cross-sectional approach. This study had 30 workers as samples using total sampling techniques. The results showed that the sound intensity factors (ρ = 0.030) and disease history (ρ = 0.013) were related to the hearing threshold value. Meanwhile, the factors of work experience (ρ = 0.704) and age (ρ = 1.000) are not related to the hearing threshold value. The conclusion of this research is that there is a relationship between sound intensity and history of illness with the hearing threshold value of workers at PT. X.

A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.

Green tea leaf extract reduces viability and migration of cholesteatoma fibroblast of chronic suppurative otitis media cultured in vitro

Background: Chronic Suppurative Otitis Media (CSOM) is still a health problem, especially in developing countries. CSOM with cholesteatoma is a dangerous type. Cholesteatoma in CSOM sufferers can cause various complications. Therefore, alternative therapies are needed, such as green tea leaf extract. Green tea leaf extract can be an antioxidant and anti-inflammatory, but its effectiveness in treating CSOM has not been studied before, so research is needed.. Purpose: This research aimed to determine the effect of green tea leaf extract that could reduce viability and migration in cholesteatoma fibroblast of CSOM.Methods: This research was an in vitro experiment with a post-test-only control group design. The sample for this research was cholesteatoma fibroblast cells obtained from the isolation of patients with CSOM. The method used is Hoechst staining for viability and scratch techniques for cell migration with eight groups of cholesteatoma fibroblasts consisting of a negative control group (DMEM+FBS), DMEM group, two positive control groups dexamethasone (10 µM and 100 µM), and four green tea leaf extract group (10 µg/ml, 40 µg/ml, 80 µg/ml, and 160 µg/ml). Data analysis used One-Way ANOVA and Kruskal Wallis tests.Results: The results showed that the highest average cell viability was in the negative control group (165.33), and the lowest was in green tea leaf extract at a dose of 160 µg/ml (70.88). Cell migration at 72 hours showed that in the negative control group, there was faster closure of the scratch area (97,78%) and the slowest closure on green tea leaf extract at a dose of 160 µg/ml (13,81%).Conclusion: Green tea leaf extract can reduce the viability and migration in cholesteatoma fibroblast of CSOM. It shows the potential of green tea extract as an alternative to prevent cholesteatoma.

Detection of Melanoma Using Distinct Features

Abstract: Melanoma is one of the most dangerous types of skin cancer because it grows fast and causes the majority of skin cancer fatalities. Hence, early detection is a very important task to treat melanoma. In this work, we suggest a method for dermoscopic images that segments skin lesions based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The suggested approach requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset–a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation qualities of the proposed method are better than ones of the compared methods.

Quality by design engineered, enhanced anticancer activity of temozolomide and resveratrol coloaded NLC and brain targeting via lactoferrin conjugation in treatment of glioblastoma

The most dangerous type of high-grade astrocytoma is glioblastoma multiforme. The objective of the work was to engineer lactoferrin conjugated temozolomide and resveratrol co-loaded NLC for the treatment of glioblastoma using intranasal delivery for brain targeting. Synergistic activity of temozolomide and resveratrol was determined using combination index method and 1:1 ratio was selected. QbD approach was used to formulate and optimize NLC, with minimum particle size, maximum transmittance and entrapment efficiency using Central Composite Rotable Design (CCRD) method. The optimized LTR-NLC had desired average particle size (209.3 nm), narrow PDI along, high percentage transmittance (>95%) and better entrapment efficiency (95.26% of TEM and 87.59% of RES). From ex-vivo permeation studies it was found that the permeation at 24 h was 77.43 %, and 88.55 % from LTR-NLC and 25.76 % and 31.10% from suspension for resveratrol and temozolomide respectively. In comparison to drug suspension, NLC had nearly 3-fold increase in drug penetration. IC50 value was also significantly better in the groups treated with LTR-NLC. Hence it can be concluded that LTR-NLC may be an effective formulation for the treatment of glioblastoma, according to the findings of this investigation.

Impact of Pesticide Use on Farmers Health Condition: Case Control Design

Introduction: Reducing agricultural emissions of substances that are toxic to humans is one of the targets of the United Nations UN’s Sustainable Development Goal (SDG) 12 Responsible Consumption and Production (SDG). Though the use of agrochemical has lead to an increase agricultural productivity, but their use has also been associated with many negative direct and indirect impacts on human health resulting in loss of working efficiency. Organophosphorus compounds had the highest amount of usage among all groups of pesticides in India in the last decade. Study Purpose: The present study aimed to identify relationship between the general physical condition among farmers and type of the pesticides and methods of dealing with them. Methods and Materials: A case control design was conducted to examine the differences between intact groups on some dependent variable of interest. A non-probability sampling technique (convenience sample) used for 201 farmers (102 farmers for the study group and 99 farmers for the control group). The study group for farmers exposure for pesticides, and control groups for farmers do not spray pesticides. The data were collected by using questionnaire (PHQ-15) -based interviews by researcher. The current study was conducted in five distract (Al-Mashkhab district, Al-Abbasiya district, al- hrea district, al-hera district and al qadisya district). Results: The study results indicated that there are an overall farmers' responses toward general physical condition among study group participant are moderately impacted, while the general physical condition was not impacted among control group participant. And the presents study shows the most common type of pesticides use is insecticides and this type is the most dangerous type on farmers health condition. In additional a negative direct relationship between number of spray pesticides and general physical condition of farmers. Conclusion: The study has been concluded a most dangerous type of pesticides on farmer health is insecticides. In additional the study conducted a direct and negative relationship between number of spray and impact farmer health condition.