Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing appli

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as

This work is concerned with building a three-dimensional (3D) ab-initio models that is capable of predicting the thermal distribution of laser direct joining processes between Polymethylmethacrylate (PMMA) and stainless steel 304(st.st.304). ANSYS® simulation based on finite element analysis (FEA) was implemented for materials joining in two modes; laser transmission joining (LTJ) and conduction joining (CJ). ANSYS® simulator was used to explore the thermal environment of the joints during joining (heating time) and after joining (cooling time). For both modes, the investigation is carried out when the laser spot is at the middle of the joint width, at 15 mm from the commencement point (joint edge) at traveling time of 3.75 s. Process par

This paper tackles with principal component analysis method (PCA ) to dimensionality reduction in the case of linear combinations to digital image processing and analysis. The PCA is statistical technique that shrinkages a multivariate data set consisting of inter-correlated variables into a data set consisting of variables that are uncorrelated linear combination, while ensuring the least possible loss of useful information. This method was applied to a group of satellite images of a certain area in the province of Basra, which represents the mouth of the Tigris and Euphrates rivers in the Shatt al-Arab in the province of Basra.

Groundwater modelling is particularly challenging in arid regions where limited water recharge is available. A fault zone will add a significant challenge to the modelling process. The Western Desert in Iraq has been chosen to implement the modelling concept and calculate the model sensitivity to the changes in aquifer hydraulic properties and calibration by researching 102 observations and irrigation wells. MODFLOW-NWT, which is a Newtonian formulation for MODFLOW-2005 approaches, have been used in this study. Further, the simulation run has been implemented using the Upstream-Weighting package (UPW) to treat the dry cells. The results show sensitivity to the change of the Kx value for the major groundwater discharge flow. Only abo

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

Pleomorphic adenoma (PA) is the most frequent benign epithelial neoplasm of salivary glands, displaying a remarkable degree of morphological variety. The aim of the study is to illustrate the clinicopathological characteristics of PA with particular emphases on epithelial cell morphology and mesenchymal elements. Materials and Methods: A total of 132 PA collected between 2002 and 2017 were recruited from Al Shahid Ghazi Al Hariri Hospital and the Department of Oral Pathology, College of Dentistry, Baghdad University, Baghdad, Iraq, and analyzed for their demographic and histopathological features. All data were analyzed using descriptive statistics. A slightly higher incidence of PA was detected in females (52.3%) compared to male subject

PVC membrane sensor for the selective determination of Mefenamic acid (MFA) was constructed. The sensor is based on ion association of MFA with Dodecaphospho molybdic acid (PMA) and Dodeca–Tungstophosphoric acid(PTA) as ion pairs. Nitro benzene (NB) and di-butyl phthalate (DBPH) were used as plasticizing agents in PVC matrix membranes. The specification of sensor based on PMA showed a linear response of a concentration range 1.0 × 10–2 –1.0 × 10–5 M, Nernstian slopes of 17.1-18.86 mV/ decade, detection limit of 7 × 10-5 -9.5 × 10 -7M, pH range 3 – 8 , with correlation coefficients lying between 0.9992 and 0.9976, respectively. By using the ionphore based on PTA gives a concentration range of 1.0 × 10–4 –1.0 × 10–5 M,