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Profit prediction is a pivotal task in financial markets, empowering investors and traders to make informed decisions. In recent years, the advent of deep learning techniques has revolutionized the field of financial forecasting, offering the potential to extract intricate patterns and relationships from vast and complex datasets. This paper presents an innovative approach to profit prediction using Long Short-Term Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN). LSTMs excel at capturing long term dependencies in sequential data, making them well-suited for modeling the dynamics of the financial markets. The core of the paper lies in the practical application of LSTM model architecture specially tailored for profit prediction. This includes defining the input layer, LSTM layers, fully connected layers and the output layer. The training and validation process is elucidated, covering data splitting, model training, validation techniques and hyper parameter tuning to enter ensure the model performance. The paper also explores the practical application of the LSTM-based profit prediction algorithm through a case study involving real-world financial data. Evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are employed to assess the algorithm's predictive accuracy and effectiveness. Additionally, the paper addresses risk assessment, a critical aspect of profit prediction in financial markets. It sheds light on the promising potential of LSTM-based profit prediction algorithms as a powerful tool for financial forecasting. It summarizes key findings, acknowledges limitations and challenges, and outlines future directions for improving the algorithm, including incorporating additional data sources and fine-tuning hyper parameters. The presented approach offers a significant advancement in the realm of profit prediction, enabling investors and traders to make more informed and data-driven decisions in an ever-evolving financial landscape. [ABSTRACT FROM AUTHOR]

Nowadays, according to the Food and Agriculture Organization (FAO), agricultural pests cause severe loss of global crop production every year. In order to eliminate these harmful insect pests, smart agriculture is the best way for farmers to use artificial intelligence techniques integrated with modern information and communication technology. Thus, they can increase the productivity of their crops. The main limiting factor for cultivation, however, is pest and disease infections. It is not possible to eradicate these diseases, but they can be controlled and monitored to minimize their impact. Crop diseases can be rapidly diagnosed by using automated image identification systems. An accurate identification system requires the extraction of features from images. To identify the samples, we used three different classes of machine learning: a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN). In addition, our deep learning method outperforms other previous recognition methods, namely Single Shot Multi-Box Detector (SSD), which increases the accuracy and multi-directional way of pest detection. Our results contribute to the use of machine learning in agriculture literature and have implications for research in deep learning and practical implications. [ABSTRACT FROM AUTHOR]

The age of technology has introduced integrated wearables into the area of driving to prevent accidents. A smart device is fitted easily into any moving vehicle, which renders a convenient option regarding the activities of the drivers such as abnormality, health condition, and sleep information. This research study discusses two types of outcomes that are enabled according to the situations of the input provided to the BPNN (Back Propagation Neural Network). At first, when an anomaly is predicted within the vehicle, such as the unusual behaviour of the driver, then it is reported through the help of IoT sensors installed inside the vehicle. The BPNN communicates the relevant signal to the driver assistance system to gain control over the vehicle and safely stop it at an appropriate place on the road. The second type of outcome discussed is when an anomaly is explored on the road when a vehicle moves in a heedless way. In such cases, the nearby vehicles are alerted and in the case of an accident are reported, the signals are sent to the local police officials. These suggested instructions perhaps minimize the damage and decrease the rate of mortality every year because of road accidents. Appropriate actions are taken by the model in averting the occurrence of accidents using cloud AI. The performance of the proposed system is assessed based on its accuracy rate. [ABSTRACT FROM AUTHOR]

The uncontrolled dumping of agricultural and industrial wastes and waste water has serious environmental and public health consequences. In order to utilise these wastes for agricultural purposes, the response of soybean seed germination to the desiccated coconut water and dairy whey was examined. In this investigation the nutritional content analysis and enumeration of microorganisms found in desiccated coconut water and dairy whey obtained, respectively from coconut mills and dairy were carried out. Different microorganisms were isolated and characterized morphologically and biochemically characterized. The results showed that the desiccated coconut water included a diverse microbial population such as bacteria (286.4 × 104 cfu mL-1), yeast (8.89 × 104 cfu mL-1), actinomycetes (1.00 × 104 cfu mL-1), free living nitrogen fixers (26.7 × 104 cfu mL-1), phosphate solubilizing bacteria (33.3 × 104 cfu mL-1), Pseudomonas sp. (73.3 × 104 cfu mL-1) and coliforms (76.7 × 104 cfu mL-1). Whereas, in dairy whey, microorganisms such as bacteria (153.3 × 104 cfu mL-1), yeasts (3.30 × 104 cfu mL-1), actinomycetes (0.12 × 104 cfu mL-1) and coliforms (56.7 × 104 cfu mL-1) were found. The effect of desiccated coconut water and dairy whey on soybean seed germination was studied by pre-treating the seeds and examining different seed quality indicators. Compared to all other treatments and the control, seeds pre-treated with 10 per cent desiccated coconut water and 10 per cent whey had significantly higher germination percentage (98.00 and 97.33), shoot length (13.58 cm and 14.30 cm), root length (21.94 cm and 20.12 cm), seedling dry weight (404.28 mg and 100.19 mg), seedling vigour index-I (3481 and 3351) and seedling vigour index -II (39619 and 39019), respectively. [ABSTRACT FROM AUTHOR]

Endometrial cancer (EC) is the most prevalent gynecological malignancy. Abnormal accumulation of sterol-O-acyl transferase 1 (SOAT1) and SOAT1-mediated cholesterol ester (CE) contributes to cancer progression in various malignancies, including ovarian cancer. Therefore, it was hypothesized that similar molecular changes may occur in EC. The present study aimed to evaluate the diagnostic and/or prognostic potential of SOAT1 and CE in EC by: i) Determining SOAT1 and CE levels in plasma, peritoneal fluid and endometrial tissue from patients with EC and control subjects; ii) performing receiver operating characteristic curve analysis to determine diagnostic performance; iii) comparing SOAT1 and CE expression to that of the tumor proliferation marker Ki67; and iv) assessing the association between SOAT1 expression and survival. Enzyme-linked immunosorbent assay was used to determine the levels of SOAT1 protein in tissue, plasma and peritoneal fluid. The mRNA and protein expression levels of SOAT1 and Ki67 in tissues were detected by reverse transcription-quantitative polymerase chain reaction and immunohistochemistry, respectively. CE levels were determined colorimetrically in plasma and peritoneal fluid. SOAT1-associated survival data from the cBioPortal cancer genomics database were used to assess prognostic relevance. The results revealed that SOAT1 and CE levels were significantly elevated in tumor tissue and peritoneal fluid samples collected from the EC group. By contrast, the plasma levels of SOAT1 and CE in the EC and control groups were similar. Significant positive associations between CE and SOAT1, SOAT1/CE and Ki67, and SOAT1/CE and poor overall survival in patients with EC suggested that SOAT1/CE may be associated with malignancy, aggressiveness and poor prognosis. In conclusion, SOAT1 and CE may serve as potential biomarkers for prognosis and target-specific treatment of EC. [ABSTRACT FROM AUTHOR]