Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson's disease behavioral analysis.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model.

Pregnancy-associated anemia is a significant health issue that poses negative consequences for both the mother and the developing fetus. This study explores the triggering factors of anemia among pregnant females in India, utilizing data from the Demographic and Health Survey 2019-21. Chi-squared and gamma tests were conducted to find out the relationship between anemia and various socioeconomic and sociodemographic elements. Furthermore, ordinal logistic regression and multinomial logistic regression were used to gain deeper insight into the factors that affect anemia among pregnant women in India. According to these findings, anemia affects about 50% of pregnant women in India. Anemia is significantly associated with various factors such as geographical location, level of education, and wealth index. The results of our study indicate that enhancing education and socioeconomic status may serve as viable approaches for mitigating the prevalence of anemia disease developed in pregnant females in India. Employing both Ordinal and Multinominal logistic regression provides a more comprehensive understanding of the risk factors associated with anemia, enabling the development of targeted interventions to prevent and manage this health condition. This paper aims to enhance the efficacy of anemia prevention and management strategies for pregnant women in India by offering an in-depth understanding of the causative factors of anemia.

Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques.

Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task.

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach.

The application of deep learning for taxonomic categorization of DNA sequences is investigated in this study. Two deep learning architectures, namely the Stacked Convolutional Autoencoder (SCAE) with Multilabel Extreme Learning Machine (MLELM) and the Variational Convolutional Autoencoder (VCAE) with MLELM, have been proposed. These designs provide precise feature maps for individual and inter-label interactions within DNA sequences, capturing their spatial and temporal properties. The collected features are subsequently fed into MLELM networks, which yield soft classification scores and hard labels. The proposed algorithms underwent thorough training and testing on unsupervised data, whereby one or more labels were concurrently taken into account. The introduction of the clade label resulted in improved accuracy for both models compared to the class or genus labels, probably owing to the occurrence of large clusters of similar nucleotides inside a DNA strand. In all circumstances, the VCAE-MLELM model consistently outperformed the SCAE-MLELM model. The best accuracy attained by the VCAE-MLELM model when the clade and family labels were combined was 94%. However, accuracy ratings for single-label categorization using either approach were less than 65%. The approach's effectiveness is based on MLELM networks, which record connected patterns across classes for accurate label categorization. This study advances deep learning in biological taxonomy by emphasizing the significance of combining numerous labels for increased classification accuracy.

Species identification, reproductive biology, and nutritional value of marine shellfish (Meretrix lyrata) in the Bay of Bengal.

Meretrix lyrata which is under the family of Veneridae and under the order of Venerida, is a nutritionally and economically important edible mussel in Bangladesh. However, studies on species identification and nutritional value in M. lyrata are scarce. Therefore, a detailed investigation was conducted on (i) species identification of the common edible mussel through DNA-barcoding and morphometrics, (ii) reproductive features, such as size at sexual maturity, spawning, and peak-spawning seasons under different environmental factors, and (iii) nutritional status through proximate analysis of M. lyrata mussel collected from the Bay of Bengal, Bangladesh. The results indicated that the size at sexual maturity for M. lyrata was 4.2 cm and the spawning seasons were significantly affected by the dissolve oxygen and salinity. The study also demonstrated that the spawning of M. lyrata occurred from January to June and December while peak spawning season was May in the Bay of Bengal. The higher protein and moisture contents with lower fat in M. lyrata indicated that are value-added seafood with higher nutritional values for consumers.

Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification.

The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals.

Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography.

Nowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data instantly or subsequently. In this paper, we focus on unmanned aerial vehicle (UAV)-aided data collection in wireless sensor networks (WSNs), where multiple UAVs collect data from a group of sensors. The UAVs may face some static or moving obstacles (e.g., buildings, trees, static or moving vehicles) in their traveling path while collecting the data. In the proposed system, the UAV starts and ends the data collection tour at the base station, and, while collecting data, it captures images and videos using the UAV aerial camera. After processing the captured aerial images and videos, UAVs are trained using a YOLOv8-based model to detect obstacles in their traveling path. The detection results show that the proposed YOLOv8 model performs better than other baseline algorithms in different scenarios-the F1 score of YOLOv8 is 96% in 200 epochs.

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient's lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets-multi-ROI and single-ROI-are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022.

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

Path loss measurement and modeling of 5G network in emergency indoor stairwell at 3.7 and 28 GHz.

Research on path loss in indoor stairwells for 5G networks is currently insufficient. However, the study of path loss in indoor staircases is essential for managing network traffic quality under typical and emergency conditions and for localization purpose. This study investigated radio propagation on a staircase where a wall separated the stairs from free space. A horn and an omnidirectional antenna were used to determine path loss. The measured path loss evaluated the close-in-free-space reference distance, alpha-beta model, close-in-free-space reference distance with frequency weighting, and alpha-beta-gamma model. These four models exhibited good compatibility with the measured average path loss. However, comparing the path loss distributions of the projected models revealed that the alpha-beta model exhibited 1.29 dB and 6.48 dB for respectively, at 3.7 GHz and 28 GHz bands. Furthermore, the path loss standard deviations obtained in this study were smaller than those reported in previous studies.

Implications of COVID-19 on oxbow lake (Baors) Fisher's community, Bangladesh: resilience to food security against probable natural calamities.

The purpose of studying the consequence of COVID-19 on oxbow lake (Baor) fisher's community is to counteract the negative impacts on livelihoods with food security and figure out diversified resilience options for sustaining basic needs of life. Individual questionnaire interviews, oral history, focus group discussion, and telephonic interviews were among the methodological techniques used to gather primary data. The Baor fisher's community was impaired with income, food and feeding habit, health and marketing. The Baor fishers had to stop harvesting or reducing the amount of fish harvest because of gradual decreasing of consumer demand and prices of fish during the course of COVID-19 pandemic period. The transportation costs were raised up to 50%-80%, while the prices of fish decreased by 15%-30% prior to the onset of COVID-19 pandemic. The frequency of fish consumption was significantly come down to 37.5%. Many households substituted fish to farm reared hens, eggs, domestic hens and ducks, lentils, and vegetables during the period of lockdown across the country. Supply chains of fish and fish culture inputswere disrupted due to inadequacy of transportation facilities. Many school- and college-going students were dropped outduring the ongoing pandemic situation due to their financial problems (10%) and early marriage (7.5%). The secondary sources of income (labor of netting in other aquaculture farms) of Baor fisher's community were also impaired. The resilience options of this study will be helpful to minimize the sudden economic crises, ensure dynamic fish value chains and food security, protect individuals from ongoing health hazards, and promote sustainable food production systems followed by social cohesion and stabilityagainst the prevailing challenges owing to the pandemic and other natural calamities.

Path Loss Investigation in Hall Environment at Centimeter and Millimeter-Wave Bands.

The millimeter-wave (mmWave) frequency is considered a viable radio wave band for fifth-generation (5G) mobile networks, owing to its ability to access a vast spectrum of resources. However, mmWave suffers from undesirable characteristics such as increased attenuation during transmission. Therefore, a well-fitted path loss model to a specific environment can help manage optimal power delivery in the receiver and optimal transmitter power in the transmitter in the mmWave band. This study investigates large-scale path loss models in a university hall environment with a real-measured path loss dataset using directional horn antennas in co-polarization (H-H) and tracking antenna systems (TAS) in line-of-sight (LOS) circumstances between the transmitter and receptor at mmWave and centimeter-level bands. Although the centimeter-level band is used in certain industrialized nations, path loss characteristics in a university hall environment have not been well-examined. Consequently, this study aims to bridge this research gap. The results of this study indicate that, in general, the large-scale floating-intercept (FI) model gives a satisfactory performance in fitting the path loss both in the center and wall side links.

Reproductive performance of Asian stinging catfish Heteropneustes fossilis (Bloch 1794) in the Ganges River (NW Bangladesh) in relation to environmental factors.

Reproduction plays an important role in fish population efficiency and its resiliency to fishing and environment changes. The present study described the comprehensive information on reproductive feature of stinging catfish, Heteropneustes fossilis (Bloch 1794), including size at sexual maturity, spawning season, and fecundity using 622 female individuals sampling by the use of gill net, cast net, and square lift net from January to December 2019 in the Ganges River. We calculated the influences of various environmental parameters which include temperature, dissolved oxygen, pH, and rainfall on the reproductive feature of H. fossilis in the Ganges River. For every specimen, total length (TL), standard length (SL), and body weight (BW) were estimated by measuring board and electronic weighing scale. With ventral dissection of fishes, female gonads were cautiously removed and measured to 0.01 g precision. The gonadosomatic index (GSI), modified gonadosomatic index (MGSI), and Dobriyal index (DI) were used to assess the size at sexual maturity (Lm) and spawning season. According to the results of these indices, Lm was obtained 15.5 cm in TL. Also, TL50 was determined through logistic function as 15.5 cm in TL. Moreover, the highest GSI, MGSI, and DI values indicated the spawning season as of March-August, with peak in May-June. Total fecundity (FT) varied from 2059 to 59,984 with a mean of 25,028 ± 15,048. Temperature and rainfall was statistically correlated with GSI. In addition, long climatic data series analysis denoted that yearly mean atmospheric temperature is rising in 0.028 °C/year and yearly mean rainfall is declining in 2.98 mm/year which may suggest a potential shift of the spawning period of the species in the future if this trend persists. The results of our study might be more useful in imposing particular management and conservation for H. fossilis in the Ganges River and the surroundings.

Life-history traits of ten commercially important small indigenous fish species (SIFS) in the Oxbow lake (Southwestern Bangladesh): key for sound management.

For the first time, we revealed the life-history traits including growth pattern (length-weight relationships, LWRs), condition factors, form factor (a3.0), first sexual maturity (Lm), age at first sexual maturity (tm), life span (tmax), natural mortality (Mw), asymptotic length (L∞), and optimum catchable length (Lopt) of ten commercially important small indigenous fish species (SIFS) in the Oxbow lake (Baor), southwestern regions of Bangladesh. A total of 1651 specimens were sampled during January to December 2020 with traditional fishing gears including seine nets, gill nets, and lift nets. Individual total length (TL) and body weight (BW) were measured by digital slide calipers and digital balance, respectively. To calculate the Lm, empirical maximum length-based model was considered, and Lopt was calculated based on L∞. The TL vs. BW relationship indicated positive allometric growth for Chanda nama (Hamilton 1822), Channa punctata (Bloch 1793), Channa striata (Bloch 1793), Lepidocephalichthys guntea (Hamilton 1822), Macrognathus pancalus (Hamilton 1822), and Puntius sophore (Hamilton 1822), but negative allometric growth for Badis badis (Hamilton 1822), Gudusia chapra (Hamilton 1822), Glossogobius giuris (Hamilton 1822), and Hyporhamphus limbatus (Valenciennes, 1847). All r2 values exceed 0.910 that indicated all LWRs were highly significant (P < 0.001). According to Spearman correlation test, Fulton's condition factor (KF) vs. BW was highly correlated (P < 0.001), indicating better well-being for these species. Moreover, a3.0 indicates B. badis, C. punctata, C. striata, G. giuris, H. limbatus, L. guntea were elongated; C. nama, P. sophore, were short and deep; G. chapra was fusiform, and M. pancalus was eel-like body shape respectively. The minimum tm and tmax were obtained as 0.74 year and 2.66 year for C. striata and maximum were 0.93 year and 3.31 year for B. badis, respectively. This study provided information on tm and tmax for ten SIFS that is globally absent. From empirical models, the smallest mean value of Lm was found for B. badis (3.98 cm), and the greatest was found for C. striata (16.96 cm). The minimum Lopt was obtained as 3.78 cm TL for B. badis and maximum was 14.09 cm TL for C. punctata. The minimum Mw was documented as 1.39 for B. badis and maximum was 1.73 for C. striata. The output of this research will be helpful for developing sustainable management policies and protection of SIFS through the application of mesh size based on Lm and Lopt in the Oxbow lakes, Bangladesh and neighboring countries.

Results of Large-Scale Propagation Models in Campus Corridor at 3.7 and 28 GHz.

The indoor application of wave propagation in the 5G network is essential to fulfill the increasing demands of network access in an indoor environment. This study investigated the wave propagation properties of line-of-sight (LOS) links at two long corridors of Chosun University (CU). We chose wave propagation measurements at 3.7 and 28 GHz, since 3.7 GHz is the closest to the roll-out frequency band of 3.5 GHz in South Korea and 28 GHz is next allocated frequency band for Korean telcos. In addition, 28 GHz is the promising millimeter band adopted by the Federal Communications Commission (FCC) for the 5G network. Thus, the 5G network can use 3.7 and 28 GHz frequencies to achieve the spectrum required for its roll-out frequency band. The results observed were applied to simulate the path loss of the LOS links at extended indoor corridor environments. The minimum mean square error (MMSE) approach was used to evaluate the distance and frequency-dependent optimized coefficients of the close-in (CI) model with a frequency-weighted path loss exponent (CIF), floating-intercept (FI), and alpha-beta-gamma (ABG) models. The outcome shows that the large-scale FI and CI models fitted the measured results at 3.7 and 28 GHz.

A Survey of Rain Attenuation Prediction Models for Terrestrial Links-Current Research Challenges and State-of-the-Art.

Millimeter-wave (30-300 GHz) frequency is a promising candidate for 5G and beyond wireless networks, but atmospheric elements limit radio links at this frequency band. Rainfall is the significant atmospheric element that causes attenuation in the propagated wave, which needs to estimate for the proper operation of fade mitigation technique (FMT). Many models have been proposed in the literature to estimate rain attenuation. Various models have a distinct set of input parameters along with separate estimation mechanisms. This survey has garnered multiple techniques that can generate input dataset for the rain attenuation models. This study extensively investigates the existing terrestrial rain attenuation models. There is no survey of terrestrial rain mitigation models to the best of our knowledge. In this article, the requirements of this survey are first discussed, with various dataset developing techniques. The terrestrial links models are classified, and subsequently, qualitative and quantitative analyses among these terrestrial rain attenuation models are tabulated. Also, a set of error performance evaluation techniques is introduced. Moreover, there is a discussion of open research problems and challenges, especially the exigency for developing a rain attenuation model for the short-ranged link in the E-band for 5G and beyond networks.

Dataset on the evaluation of hydrochemical properties and groundwater suitability for irrigation purposes: South-western part of Jashore, Bangladesh.

The data herein presented concerns the article entitled "Evaluation of hydrochemical properties and groundwater suitability for irrigation uses in southwestern zones of Jashore, Bangladesh". Data was collected during 2018-2019 in the southwestern zones of Jashore, Bangladesh. One hundred groundwater samples (boreholes and tube wells) were collected to evaluate groundwater quality, using the irrigation water quality index (IWQI) as an indicator. Fourteen hydrochemical parameters (pH, EC, TDS, NO3N, pH, EC, Ca2+, Mg2+, Na+, K+, Cl-, HCO3 -, SO4 2- and Fe2+) were used to calculate irrigation water quality indices (KI, Na%, PI, SAR, SSP, MH, and TH). Statistical methods such as Viper diagrams, USSL, and Wilcox diagrams were used to visualize datasets. The attained data can be used to assess the hydrogeochemistry of the sampled sites and groundwater quality for irrigation purposes. The findings of this work can be used in the optimization of management and treatment procedures and in the implementation of sustainable water development.