Search results for: cognitive image dimension

Authors: Prasannakumar Palaniappan, Dong Ho Shin, Chul Gyu Song

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The photoacoustic images are obtained from a custom developed linear array photoacoustic tomography system. The biological specimens are imitated by conducting phantom tests in order to retrieve a fully functional photoacoustic image. The acquired image undergoes the active region based contour filtering to remove the noise and accurately segment the object area for further processing. The universal back projection method is used as the image reconstruction algorithm. The active contour filtering is analyzed by evaluating the signal to noise ratio and comparing it with the other filtering methods.

Keywords: contour filtering, linear array, photoacoustic tomography, universal back projection

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Authors: Amanda de Oliveira Ferreira Leite, Ana Luiza del Pino Ferreira, Bruna Garcez Correa, Janaíne de Souza Mello, Marla Manquevich, Mirna Wetters Portuguez

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Objective: We sought to investigate a neuropsychological intervention focused on improving cognition, psychological aspects, and quality of life of elderly people with mild cognitive impairment. Method: A controlled and randomized study, blind to the evaluator, was executed. We evaluated 78 elderly people, divided into the neuropsychological and control groups, through a semi-structured interview, Addenbrooke’s Cognitive Examination, Katz Index, Lawton and Brody Scale, Geriatric Depression Scale, Beck Anxiety Inventory, Personal Development Scale, WHOQOL-bref and WHOQOL--old. Results: After the intervention, the neuropsychological group showed improvement in the cognitive subtests and in the total score, reduction in the frequency of symptoms associated with anxiety and depression, better psychological well-being, and quality of life. The research highlights useful intervention strategies for improving the general condition of these patients and rehabilitating damaged areas. Conclusion: We concluded that there is a relationship between neuropsychological intervention and improvement in cognitive and psychological performance, as well as in the quality of life in elderly people with mild cognitive impairment.

Keywords: aging, mild cognitive impairment, neuropsychology, quality of life

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Authors: Wei Zhang, Yan He, Yan Wang, Yufeng Li, Chuanpeng Hao

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Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness.

Keywords: feature analysis, machine vision, PCA, surface roughness, SVM

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Authors: Giulio Nisoli, Jonas Brüngger, Karin Hostettler, Nicole Stoller, Katrin Fischer

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Technical operators in the railway domain are experts responsible for the supervisory control of the railway power grid as well as of the railway tunnels. The technical systems used to master these demanding tasks are constantly increasing in their degree of automation. It becomes therefore difficult for technical operators to maintain the control over the technical systems and the processes of their job. In particular, the operators must have the necessary experience and knowledge in dealing with a malfunction situation or unexpected event. For this reason, it is of growing importance that the skills relevant for the execution of the job are maintained and further developed beyond the basic training they receive, where they are educated in respect of technical knowledge and the work with guidelines. Training methods aimed at improving the cognitive skills needed by technical operators are still missing and must be developed. Goals of the present study were to identify which are the relevant cognitive skills of technical operators in the railway domain and to define which topics should be addressed by the training of these skills. Observational interviews were conducted in order to identify the main tasks and the organization of the work of technical operators as well as the technical systems used for the execution of their job. Based on this analysis, the most demanding tasks of technical operators could be identified and described. The cognitive skills involved in the execution of these tasks are those, which need to be trained. In order to identify and analyze these cognitive skills a cognitive task analysis (CTA) was developed. CTA specifically aims at identifying the cognitive skills that employees implement when performing their own tasks. The identified cognitive skills of technical operators were summarized and grouped in training topics. For every training topic, specific goals were defined. The goals regard the three main categories; knowledge, skills and attitude to be trained in every training topic. Based on the results of this study, it is possible to develop specific training methods to train the relevant cognitive skills of the technical operators.

Keywords: cognitive skills, cognitive task analysis, technical operators in the railway domain, training topics

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Authors: Cut Maisyarah Karyati, Aries Muslim, Sulardi

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Digital image processing techniques to obtain detailed information from an image have been used in various fields, including in civil engineering, where the use of solid beam profiles in buildings and bridges has often been encountered since the early development of beams. Along with this development, the founded castellated beam profiles began to be more diverse in shape, such as the shape of a hexagon, triangle, pentagon, circle, ellipse and oval that could be a practical solution in optimizing a construction because of its characteristics. The purpose of this research is to create a computer application to edge detect the profile of various shapes of the castella beams hole. The digital image segmentation method has been used to obtain the grayscale images and represented in 2D and 3D formats. This application has been successfully made according to the desired function, which is to provide information on the type of castella beam hole.

Keywords: digital image, image processing, edge detection, grayscale, castella beams

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Authors: Alireza Pirkhaefi

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Background: Multiple sclerosis (MS) is one of the most common diseases of the central nervous system (brain and spinal cord). Given the importance of cognitive disorders in patients with multiple sclerosis, the present study was in order to compare cognitive functions (Working memory, Attention and Centralization, and Visual-spatial perception) in patients with relapsing- remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS). Method: Present study was performed as a retrospective study. This research was conducted with Ex-Post Facto method. The samples of research consisted of 60 patients with multiple sclerosis (30 patients relapsing-retrograde and 30 patients secondary progressive), who were selected from Tehran Community of MS Patients Supported as convenience sampling. 30 normal persons were also selected as a comparison group. Montreal Cognitive Assessment (MOCA) was used to assess cognitive functions. Data were analyzed using multivariate analysis of variance. Results: The results showed that there were significant differences among cognitive functioning in patients with RRMS, SPMS, and normal individuals. There were not significant differences in working memory between two groups of patients with RRMS and SPMS; while significant differences in these variables were seen between the two groups and normal individuals. Also, results showed significant differences in attention and centralization and visual-spatial perception among three groups. Conclusions: Results showed that there are differences between cognitive functions of RRMS and SPMS patients so that the functions of RRMS patients are better than SPMS patients. These results have a critical role in improvement of cognitive functions; reduce the factors causing disability due to cognitive impairment, and especially overall health of society.

Keywords: multiple sclerosis, cognitive function, secondary-progressive, normal subjects

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Authors: Clara Eichberger, Daantje Derks, Hannes Zacher

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Technology-assisted supplemental work (TASW) involves performing job-related tasks after regular working hours with the help of technological devices. Due to emerging information and communication technologies, such behavior becomes increasingly common. Since previous research on the relationship of TASW, psychological detachment and well-being are mixed, this study aimed to examine the moderating roles of appraisal and cognitive coping. A moderated mediation model was tested with daily diary data from 100 employees. As hypothesized, TASW was positively related to negative affect at bedtime. In addition, psychological detachment mediated this relationship. Results did not confirm appraisal and cognitive coping as moderators. However, additional analyses revealed cognitive coping as a mediator of the positive relationship of TASW and positive affect at bedtime. These results suggest that, on the one hand engaging in TASW can be harmful to employee well-being (i.e., more negative affect) and on the other hand, it can also be associated with higher well-being (i.e., more positive affect) in case it is accompanied by cognitive coping.

Keywords: cognitive coping, psychological detachment, technology-assisted supplemental work, well-being

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: magnetic resonance image (MRI), c-means model, image segmentation, information entropy

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Authors: Hadis Mahmoodsalehi, Elham Shakoor, Maryam Koushkie Jahromi

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Background and aims: Given the importance of health promotion in elderly and attention to health factors including physical activity and self-image reinforcing, this study aimed to investigate the relationship between exercise attitude and performance with self-image concept in elderly men. Methods: In this descriptive–correlational study, 50 different daily exercise activities of the elderly men living in Iran (mean age: 60.94 years) were selected through simple sampling method. Participants completed a questionnaire regarding exercise attitude and performance and Beck self-image concept. Pearson correlation test was used for analysis of the data. Results: The results showed the significant correlation between optimism and exercise performance (p = 0.012) and exercise attitude (p = 0.005). Conclusion: Findings show that exercise performance and attitude are associated positively with optimism in elderly women. So, increasing exercise or improving attitude toward exercise can lead to improving optimism.

Keywords: elderly, exercise performance and attitude, self-image, descriptive–correlational study

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Shannon Steinmetz, Ellen Gethner

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In this paper, a theoretical foundation is developed for patterned segmentation of audio using the geometry of music and statistical manifold. We demonstrate image content clustering using conic space sonification. The algorithm takes a geodesic curve as a model estimator of the three-parameter Gamma distribution. The random variable is parameterized by musical centricity and centric velocity. Model parameters predict audio segmentation in the form of duration and frame count based on the likelihood of musical geometry transition. We provide an example using a database of randomly selected images, resulting in statistically significant clusters of similar image content.

Keywords: sonification, musical information geometry, image, content extraction, automated quantification, audio segmentation, pattern recognition

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Authors: Ali A. Ukasha, Majdi F. Elbireki, Mohammad F. Abdullah

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Data security needed in data transmission, storage, and communication to ensure the security. This paper is divided into two parts. This work interests with the color image which is decomposed into red, green and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using the key image that has same original size and are generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours from color images recovery can be obtained with accepted level of distortion using single step parallel contour extraction (SSPCE) method. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Color images and completely reconstructed without any distortion. Also shown that the analyzed algorithm has extremely large security against some attacks like salt and pepper and Jpeg compression. Its proof that the color images can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression and salt and peppers attacks, bitplanes decomposition, Arnold transform, color image, wavelet transform, lossless image encryption

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Authors: Alina Trifan, António J. R. Neves

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Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods.

Keywords: bayer image, CFA, lossless compression, image coding standards

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Authors: Yung Gi Wu

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With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.

Keywords: data hiding, vector quantization, watermark, color image

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Authors: In-Geun Lim, Sung-Woong Ra

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As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically.

Keywords: facility detection, satellite image, object, mathematical morphology

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Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Kateryna Nykytchenko

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Modern fiction and non-fiction writers commonly use their own lexical and stylistic devices to capture a reader’s attention and bring certain thoughts and feelings to his reader. Among such devices is the appearance of one of the neologic notions – individual author’s formations: occasionalisms or nonce words. To a significant extent, the host of examples of new words occurs in chick lit genre which has experienced exponential growth in recent years. Chick Lit is a new-millennial postfeminist fiction which focuses primarily on twenty- to thirtysomething middle-class women. It brings into focus the image of 'a new woman' of the 21st century who is always fallible, funny. This paper aims to investigate different types of occasional word-formation which reflect cognitive mechanisms of conveying women’s perception of the world. Chick lit novels of Irish author Marian Keyes present genuinely innovative mixture of forms, both literary and nonliterary which is displayed in different types of occasional word-formation processes such as blending, compounding, creative respelling, etc. Crossing existing mental and linguistic boundaries, adopting herself to new and overlapping linguistic spaces, chick lit author creates new words which demonstrate the result of development and progress of language and the relationship between language, thought and new reality, ultimately resulting in hybrid word-formation (e.g. affixation or pseudoborrowing). Moreover, this article attempts to present the main characteristics of chick-lit fiction genre with the help of the Marian Keyes’s novels and their influence on occasionalisms. There has been a lack of research concerning cognitive nature of occasionalisms. The current paper intends to account for occasional word-formation as a set of interconnected cognitive mechanisms, operations and procedures meld together to create a new word. The results of the generalized analysis solidify arguments that the kind of new knowledge an occasionalism manifests is inextricably linked with cognitive procedure underlying it, which results in corresponding type of word-formation processes. In addition, the findings of the study reveal that the necessity of creating occasionalisms in postmodern fiction novels arises from the need to write in a new way keeping up with a perpetually developing world, and thus the evolution of the speaker herself and her perception of the world.

Keywords: Chick Lit, occasionalism, occasional word-formation, cognitive linguistics

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Authors: Ramy Mohamed, Moushira E. Zaki, Hala T. El-Bassyouni, Walaa Yousef, Safinaz El Toukhy, Samira Ismail

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Obesity is a worldwide epidemic problem. This study investigates the role of obesity on body image, frequency of anxiety, depression disorders and DNA damage in a sample of Egyptian women. The study included 172 women, 84 were obese, and 88 were age-matched non-obese women. Obese women showed a significantly higher frequency of abnormal body image (77.4%) as compared with the non-obese (42%) (P < 0.0001). Moreover, they had significantly higher frequencies of severe anxiety (26.2%) and depression (41.7%) than non-obese women (2.3 and 18.2%, respectively) (P < 0.001). Leukocyte DNA damage was evaluated by comet assay and revealed high DNA damage in obese women. Obesity is a potential risk factor for abnormal body image, anxiety, depression, and DNA damage among Egyptian women. Identifying the psychological problems in obese women is essential to improve quality of life and promote management and prevention.

Keywords: anxiety, body image, depression, DNA damage, obesity

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Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Adil Al-Rammahi

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Image or document encryption is needed through e- government data base. Really in this paper we introduce two matrices images, one is the public, and the second is the secret (original). The analyses of each matrix is achieved using the transformation of singular values decomposition. So each matrix is transformed or analyzed to three matrices say row orthogonal basis, column orthogonal basis, and spectral diagonal basis. Product of the two row basis is calculated. Similarly the product of the two column basis is achieved. Finally we transform or save the files of public, row product and column product. In decryption stage, the original image is deduced by mutual method of the three public files.

Keywords: image cryptography, singular values decomposition

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Authors: Omar Nasr, Yasser Naguib, Mohamed Hafez

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This paper aims at providing an innovative solution for blind interference threshold estimation in an underlay cognitive network to be used in adaptive beamforming by secondary user Transmitter and Receiver. For the task of threshold estimation, blind detection of modulation and SNR are used. For the sake of beamforming several localization algorithms are compared to settle on best one for cognitive environment. Beamforming algorithms as LCMV (Linear Constraint Minimum Variance) and MVDR (Minimum Variance Distortion less) are also proposed and compared. The idea of just nulling the primary user after knowledge of its location is discussed against the idea of working under interference threshold.

Keywords: cognitive radio, underlay, beamforming, MUSIC, MVDR, LCMV, threshold estimation

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Authors: R. Bouhennache, T. Bouden, A. A. Taleb, A. Chaddad

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In this paper we propose a method to map the urban areas. The method uses an arithmetic calculation processed from the land features indexes and Tasseled cap transformation TC of multi spectral Thematic Mapper Landsat TM image. For this purpose the derived indexes image from the original image such SAVI the soil adjusted vegetation index, UI the urban Index, and EBBI the enhanced built up and bareness index were staked to form a new image and the bands were uncorrelated, also the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification approaches were first applied on the new image TM data using the reference spectra of the spectral library and subsequently the four urban, vegetation, water and soil land cover categories were extracted with their accuracy assessment.The urban features were represented using a logic calculation applied to the brightness, UI-SAVI, NDBI-greenness and EBBI- brightness data sets. The study applied to Blida and mentioned that the urban features can be mapped with an accuracy ranging from 92 % to 95%.

Keywords: EBBI, SAVI, Tasseled Cap Transformation, UI

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Authors: Hazem Munawer Al-Otum

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In this work, an efficient watermarking technique is proposed and can be used for detecting intentional attacks in RGB color images. The proposed technique can be implemented for image authentication and exhibits high robustness against unintentional common image processing attacks. It deploys two measures to discern between intentional and unintentional attacks based on using a quantization-based technique in a modified 2D multi-pyramidal DWT transform. Simulations have shown high accuracy in detecting intentionally attacked regions while exhibiting high robustness under moderate to severe common image processing attacks.

Keywords: image authentication, copyright protection, semi-fragile watermarking, tamper detection

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Authors: Fuad M. Alkoot

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The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy.

Keywords: fractal geometry, machine learning, classifier, fractal dimension

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Authors: Ahmed Mohamed Elnady Mohamed Elsafty

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This paper explains a developed applied objective model to measure the culture qualitatively and quantitatively, whether in individuals or in teams, in order to be able to use culture correctly or modify it efficiently. This model provides precise measurements and consistent interpretations by being comprehensive, updateable, and protected from being misled by imitations. Methodically, the provided model divides the culture into seven dimensions (total 43 cultural factors): First dimension is outcome-orientation which consists of five factors and should be highest in leaders. Second dimension is details-orientation which consists of eight factors and should be in highest intelligence members. Third dimension is team-orientation which consists of five factors and should be highest in instructors or coaches. Fourth dimension is change-orientation which consists of five factors and should be highest in soldiers. Fifth dimension is people-orientation which consists of eight factors and should be highest in media members. Sixth dimension is masculinity which consists of seven factors and should be highest in hard workers. Last dimension is stability which consists of seven factors and should be highest in soft workers. In this paper, the details of all cultural factors are explained. Practically, information collection about each cultural factor in the targeted person or team is essential in order to calculate the degrees of all cultural factors using the suggested equation of multiplying 'the score of factor presence' by 'the score of factor strength'. In this paper, the details of how to build each score are explained. Based on the highest degrees - to identify which cultural dimension is the prominent - choosing the tested individual or team in the supposedly right position at the right time will provide a chance to use minimal efforts to make everyone aligned to the organization’s objectives. In other words, making everyone self-motivated by setting him/her at the right source of motivation is the most effective and efficient method to achieve high levels of competency, commitment, and sustainability. Modifying a team culture can be achieved by excluding or including new members with relatively high or low degrees in specific cultural factors. For conclusion, culture is considered as the software of the human beings and it is one of the major compression factors on the managerial discretion. It represents the behaviors, attitudes, and motivations of the human resources which are vital to enhance quality and safety, expanding the market share, and defending against attacks from external environments. Thus, it is tremendously essential and useful to use such a comprehensive model to measure, use, and modify culture.

Keywords: culture dimensions, culture factors, culture measurement, cultural analysis, cultural modification, self-motivation, alignment to objectives, competency, sustainability

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Authors: Sevilay Çankaya

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The detection of deception in children’s interviews is essential for statement veracity. The widely used method for deception detection is building cognitive load, which is the logic of the cognitive interview (CI), and its effectiveness for adults is approved. This meta-analysis delves into the effectiveness of inducing cognitive load as a means of enhancing veracity detection during interviews with children. Additionally, the effectiveness of cognitive load on children's total number of events recalled is assessed as a second part of the analysis. The current meta-analysis includes ten effect sizes from search using databases. For the effect size calculation, Hedge’s g was used with a random effect model by using CMA version 2. Heterogeneity analysis was conducted to detect potential moderators. The overall result indicated that cognitive load had no significant effect on veracity outcomes (g =0.052, 95% CI [-.006,1.25]). However, a high level of heterogeneity was found (I² = 92%). Age, participants’ characteristics, interview setting, and characteristics of the interviewer were coded as possible moderators to explain variance. Age was significant moderator (β = .021; p = .03, R2 = 75%) but the analysis did not reveal statistically significant effects for other potential moderators: participants’ characteristics (Q = 0.106, df = 1, p = .744), interview setting (Q = 2.04, df = 1, p = .154), and characteristics of interviewer (Q = 2.96, df = 1, p = .086). For the second outcome, the total number of events recalled, the overall effect was significant (g =4.121, 95% CI [2.256,5.985]). The cognitive load was effective in total recalled events when interviewing with children. All in all, while age plays a crucial role in determining the impact of cognitive load on veracity, the surrounding context, interviewer attributes, and inherent participant traits may not significantly alter the relationship. These findings throw light on the need for more focused, age-specific methods when using cognitive load measures. It may be possible to improve the precision and dependability of deceit detection in children's interviews with the help of more studies in this field.

Keywords: deceit detection, cognitive load, memory recall, children interviews, meta-analysis

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Authors: Agastya Pratap Singh

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This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles.

Keywords: generative adversarial networks, image recognition, anomaly detection, synthetic data generation, deep learning, computer vision, unsupervised learning, pattern recognition, model evaluation, machine learning applications

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Authors: Fahad R. Choudhry., Khadeeja Munawar

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This clinical case presents a 24 years old, Muslim Pakistani girl with a history of conversion disorder. Her symptoms comprised fits, restlessness, numbness in legs, poor coordination and balance, burning during urination and retention. A cognitive-behavioral model was used for conceptualizing her problem and devising a management plan based on cognitive behavioral therapy (CBT) and culturally adapted coping statements. She took 13 therapy sessions and was presented with idiosyncratic case conceptualization. Psychoeducation, coping statements, extinction, verbal challenging, and behavioral activation techniques were practiced in a collaborative way for cognitive restructuring of the client. Focus of terminal sessions was on anger management. The client needed a couple of more sessions in order to help her manage her anger. However, the therapy was terminated on the part of the client after attainment of short term goals. The client reported to have a 75 % improvement in her overall condition and remained compliant throughout the therapy.

Keywords: cognitive behavioral therapy, conversion disorder, female, Muslim, Pakistani

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Hossam M. Moftah, Aboul Ella Hassanien

Abstract:

In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach.

Keywords: traffic counting, traffic management, image processing, object detection, computer vision

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