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Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.

According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as ‘a danger area of a collision’, and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, ‘a danger area of a collision’ is proposed for evaluating equipment collision risk based on the moving equipment’s speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the ‘danger area of a collision’, thereby mitigating accident risks on construction sites.

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.

Endophytes are microbial symbionts living inside plants and have been extensively researched in recent decades for their functions associated with plant responses to environmental stress. We conducted a meta-analysis of endophyte effects on host plants’ growth and fitness in response to three abiotic stress factors: drought, nitrogen deficiency, and excessive salinity. Ninety-four endophyte strains and 42 host plant species from the literature were evaluated in the analysis. Endophytes increased biomass accumulation of host plants under all three stress conditions. The stress mitigation effects by endophytes were similar among different plant taxa or functional groups with few exceptions; eudicots and C₄ species gained more biomass than monocots and C₃ species with endophytes, respectively, under drought conditions. Our analysis supports the effectiveness of endophytes in mitigating drought, nitrogen deficiency, and salinity stress in a wide range of host species with little evidence of plant-endophyte specificity.

Sustainable production of biomass for bioenergy relies on low-input crop production. Inoculation of bioenergy crops with plant growth-promoting endophytes has the potential to reduce fertilizer inputs through the enhancement of biological nitrogen fixation (BNF). Endophytes isolated from native poplar growing in nutrient-poor conditions were selected for a series of glasshouse and field trials designed to test the overall hypothesis that naturally occurring diazotrophic endophytes impart growth promotion of the host plants. Endophyte inoculations contributed to increased biomass over uninoculated control plants. This growth promotion was more pronounced with multi-strain consortia than with single-strain inocula. Biological nitrogen fixation was estimated through 15N isotope dilution to be 65% nitrogen derived from air (Ndfa). Phenotypic plasticity in biomass allocation and branch production observed as a result of endophyte inoculations may be useful in bioenergy crop breeding and engineering programs.

The positive effects of high atmospheric CO₂ concentrations [CO₂] decrease over time in most C₃ plants because of down-regulation of photosynthesis. A notable exception to this trend is plants hosting N-fixing bacteria. The decrease in photosynthetic capacity associated with an extended exposure to high [CO₂] was therefore studied in non-nodulating rice that can establish endophytic interactions. Rice plants were inoculated with diazotrophic endophytes isolated from the Salicaceae and CO₂ response curves of photosynthesis were determined in the absence or presence of endophytes at the panicle initiation stage. Non-inoculated plants grown under elevated [CO₂] showed a down-regulation of photosynthesis compared to those grown under ambient [CO₂]. In contrast, the endophyteinoculated plants did not show a decrease in photosynthesis associated with high [CO₂], and they exhibited higher photosynthetic electron transport and mesophyll conductance rates than non-inoculated plants under high [CO₂]. The endophyte-dependent alleviation of decreases in photosynthesis under high [CO₂] led to an increase in water-use efficiency. These effects were most pronounced when the N supply was limited. The results suggest that inoculation with N-fixing endophytes could be an effective means of improving plant growth under high [CO₂] by alleviating N limitations.

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level encoder features are repeatedly used at multiple scales. Second, the contextual information of the low-level encoder feature is insufficient, leading to poor performance for pixel-wise recognition when concatenating with the corresponding high-level decoder feature. In this study, we propose a novel spatial-channel attention gate that addresses the limitations of plain skip connections. This can be easily integrated into an encoder-decoder network to effectively improve the performance of the image segmentation task. Comprehensive results reveal that our spatial-channel attention gate remarkably enhances the segmentation capability of the U-Net architecture with a minimal computational overhead added. The experimental results show that our proposed method outperforms the conventional deep networks in term of Dice score, which achieves 71.72%.

The tumor microenvironment (TME) is associated with tumor prognosis, immunotherapy response, and prognosis in patients. Here, we hypothesized that the entire TME in pathology image is associated with the survival time prediction. To address this hypothesis, we utilize the entire TME on pathology image of early-stage lung adenocarcinoma (esLUAD), which is the most common histological subtype of lung cancer. Notably, we investigated whether machine learning models can predict individual survival time from pathology images without region-level annotation and solely based on patient-level survival data. In particular, we proposed a pathology image-based predictive model in a cascaded learning system to predict the individual survival time of esLUAD patients in two independent cohorts (National Lung Screening Trial (NLST) and Cancer Genome Atlas Program (TCGA)). Besides that, we estimate a mean absolute error (MAE) score and a C-index score that are strongly associated with the survival time prediction. Our method achieved (361.90 MAE - 0.70 C-index) and (365.67 MAE - 0.58 C-index) in early-stage NLST and early-stage TCGA cohorts, respectively. Together, the presented results highlight the importance of computation pathology algorithms in predicting survival time using the entire TME information in pathology images and support the use of computational methods to improve the efficiency of clinical trial studies.