Exploring Climate Change Adaptation Practices and Agricultural Livelihoods among Rice Farmers of the Brahmaputra Valley in Northeast India.

Global climate change has seriously threatened agriculture and connected sectors, especially in developing countries like India. The Brahmaputra Valley in Assam, Northeast India, is vulnerable to climate change due to its agrarian economy, fragile geo-ecological setting, recurrent floods and droughts, and poor socioeconomic conditions of the farmers. The climate-induced hindrances faced by the rice farming community of this region and the local adaptation practices they employ have not been adequately studied. Therefore, we carried out a survey among 635 rice farmers across four agro-climatic zones of Assam, namely the Upper Brahmaputra Valley Zone, North Bank Plain Zone, Central Brahmaputra Valley Zone, and Lower Brahmaputra Valley Zone, to understand how they perceive and respond to climatic changes. The survey revealed that all the respondents have perceived an increase in ambient temperature, and 65% of the respondents have perceived a slight change in rainfall characteristics over the years. Most farmers reported adjusting the existing farming practices and livelihood choices to adapt to the changing climate. Farming adjustments were made mainly in terms of field preparation and management of water, rice variety, nutrients, and pests. Environmental variables like rainfall, flood, drought, and pest level, and socioeconomic variables like family size, education, farming experience, training, digital media exposure, and land area were found to influence farmers' adaptation choices. The findings imply that policies to strengthen flood, drought, pest management, education, land-use planning, agricultural training, and digital media applications in agriculture are needed for effective climate change adaptation in this region.

Enhancing the efficacy of depression detection system using optimal feature selection from EHR.

Diagnosing depression at an early stage is crucial and majorly depends on the clinician's skill. The present work aims to develop an automated tool for assisting the diagnostic procedure of depression using multiple machine-learning techniques. The dataset of sample size 4184 used in this study contains biometric and demographic information of individuals with or without depression, accessed from the University of Nice Sophia-Antipolis. The Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) are used for classifying the depressed from the control group. To enhance the computational efficiency, various feature selection algorithms like Recursive Feature Elimination (RFE), Mutual Information (MI) and three bio-inspired techniques, viz. Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Firefly Algorithms (FA) have been incorporated. To enhance the feature selection process further, majority voting is carried out in all possible combinations of three, four and five feature selection techniques. These feature selection techniques bring down the feature set size significantly to a mean of 33 from the actual size of 61 which is a reduction of 45.90%. The classification accuracy of the enhanced model varies between 84.18% and 88.46%, which is a significant improvement in performance as compared to the pre-existing models (83.76-85.89%). The proposed predictive models outperform the pre-existing classification models without feature selection and thereby enhancing both the performance and efficiency of the diagnostic process.

Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique.

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop's growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.

The diagnosis of ASD using multiple machine learning techniques.

Autism Spectrum Disorder (ASD) is a highly heterogeneous set of neurodevelopmental disorders with the global prevalence estimates of 2.20%, according to DSM5 criteria. With the advancements of technology and availability of huge amount of data, assistive tools for diagnosis of ASD are being developed using machine learning techniques. The present study examines the possibility of automating the Autism diagnostic tool using various machine learning techniques on a dataset of 701 samples that contains 10 fields from AQ-10-Adult and 10 from individual characteristics. It takes two scenarios into consideration. First one is ideal case, where there are no missing values in the test cases. In this case Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) classifiers are trained and tested on the pre-processed dataset. To reduce computational complexity Recursive Feature Elimination (RFE) based feature selection algorithm is applied. To deal with the real-world data, in the second case missing values are introduced in the test dataset for the fields' 'age', 'gender', 'jaundice', 'autism', 'used_app_before' and their three combinations. Support Vector Machine, Random Forest, Decision Tree and Logistic Regression based RFE algorithm is introduced to handle this scenario. ANN, SVM and RF classifier based learning models are trained with all the cases. Twelve classification models were generated with RFE, out of which best performing models specific to missing value were evaluated using test cases and suggested for ASD Diagnosis.

An insight into diagnosis of depression using machine learning techniques: a systematic review.

BACKGROUND: In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS: This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS: A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION: The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.