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The main objective of this study is to consider how to benefit efficiently from the significant potential of humanitarian operations by individuals. For this purpose, this study aims to assess failure factors in humanitarian supply chain operations after the Kermanshah earthquake considering the role of all parties, focusing on individuals who did not wish to work with formal organisations on the whole. In the aftermath of the Kermanshah earthquake, which occurred on 12 November 2017, improvised groups of Iranian civilians from all over the country played an important role in humanitarian supply chain operations as individuals. Although most of these groups sincerely intended to help the affected society, victims could not benefit properly from these significant potential humanitarian actions. On the contrary, these potential actions caused some issues during humanitarian operations, such as blocking roads, inappropriate last-mile distribution, wasting resources and so on.

This research study considers mixed methods, including an on-site survey, semi-structured interviewing and a questionnaire designed for statistical analyses. The analysis included 140 responses to the questionnaire, semi-structured interviews with 32 affected families, interviews with 5 emergency managers from the Housing Foundation of the Islamic Republic of Iran and on-site survey reports.

This study presents a framework for humanitarian supply chain management to deal with future disasters in the same area or areas with similar characteristics to the case study. In general, the results of this study demonstrate that the nature of humanitarian supply chain operations makes it impossible to consider that these operations are free of challenges. However, several influential factors, such as training humanitarian actors and integrated management, might considerably increase the efficiency of humanitarian operations by individuals.

This study highlights the influential factors of inappropriate humanitarian operations by individuals, derived from an analysis of the Kermanshah case and literature review. The authors suggest a framework to benefit from the significant potential of individuals with wide-ranging experiences and proficiency, for future cases similar to the case study.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.