The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or livin

In this study a new antiseptic was formulated and tested to match the effectiveness against microorganisms. The formulation consisted of Povidone - Iodine (PVP-I) (10%), H₂O₂ (3%) and Aloe Vera gel (pure). Different ratios of these materials were prepared within the acceptable range of pH for an antiseptic (3-6). The prepared samples were tested. The In Vitro test was performed by using four bacteria, two were Gram-Positive (Staphylococcus aureus and Bacillus cereus) and two were Gram-Negative (Escherichia coli and Pseudomonas aeruginosa). The new antiseptic showed 100% killing rate for E. coli, Ps. aeruginosa and S. aureus and 96.4667% killing rate for B. cereus. When the new antiseptic was compared with two common

   In this paper, a mathematical model for the oxidative desulfurization of kerosene had been developed. The mathematical model and simulation process is a very important process due to it provides a better understanding of a real process. The mathematical model in this study was based on experimental results which were taken from literature to calculate the optimal kinetic parameters where simulation and optimization were conducted using gPROMS software. The optimal kinetic parameters were Activation energy 18.63958 kJ/mol, Pre-exponential factor  2201.34 (wt)^-0.76636. min^-1  and the reaction order 1.76636. These optimal kinetic parameters were used to find the optimal reaction conditions which

Fine aggregates used for concrete works in Sulaymaniyah city frequently fail to meet the standard requirements for gradation and fineness modulus in cement concrete. This paper aims to critically evaluate gradation, fineness modulus, and clay contents of various natural sands produced and used for concrete work in the region.  Sixteen field sand samples were collected from various sites in Darbandikhan (5 samples), Qalat Dizah (5 samples), Koysinjaq (5 samples), and Piramagroon (1 sample) confirming to ASTM D75. The field samples were parted into test specimens based on ASTM C702. Then, sieve analysis was carried out on the oven-dry test specimens in compliance with ASTM C136. The test results of fine aggregates wer

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi