Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

The  lossy-FDNR  based  aclive  fil ter has an  important   property among  many  design  realizations. 'This includes  a significant reduction in component count particularly in the number  of OP-AMP which consumes   power.  However  the·  problem  of  this   type  is the  large component spreads  which affect the fdter performance.

In  this  paper   Genetic   Algorithm   is  applied   to  minimize   the component  spread   (capacitance  and  resistance  p,read). The minimization of these spreads allow the fil

Advances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library. This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship an

Fuzzy Based Clustering for Grayscale Image Steganalysis

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Vol. 6, Issue 1 (2025)

      Mukdadiya Formation represents one of the formations that cover a huge area of Iraq. It contains several clastic deposits, such as sandstone, siltstone, and a noticeable amount of gravels. The gravels are considered as the hallmark to differentiate between Injana and Mukdadiya formations. Therefore, the current study focused on these facies to determine the petrography, paleontology , and origin of Mukdadiya deposits. The results of SEM-EDX and XRD analyses showed two types of gravels, namely the siliceous and lime gravels. The highest percentage of gravels belonged to the sedimentary origin (limestone). The elements of Si, Ca, and Fe represented the common elements that formed the studied gravels. The pale

The sequence in the upper part of the Balambo Formation is composed mainly of limestone alternating with marly limestones and dark grey shale in the Bosheen section (eastern Sulaymaniyah, northeastern Iraq) and has been studied in terms of its rare earth element (REE) content. The REEs are very low compared to modern marine sediments. They are depletion in LREEs, and enrichment in HREEs and (La/Yb)_N in the studied rocks, indicating that these sediments retained the REEs pattern of marine waters. The negative Ce anomaly reflects direct sedimentation from marine waters under anoxic conditions with the contribution of terrigenous clays. The positive correlation of ∑REEs with Al, Ti, and Y, and the negative correlation of ∑REE