Image based Seen Detection in Real Time Video Interpretation for Surveillance Systems using Support Vectors Machine

People that are perpetually hunting for knowledge will benefit from data acquisition. The early phase in video data acquisition is splitting the video into images. Many images are tiny and don't reveal a lot about the picture's information. Scene boundary identification, or video segmentation into action sequences, enables a more complete comprehension of the image sequence by classifying images based on comparable visual content. The purpose of this article is to discuss video scene recognition, particularly video structure extraction for pattern comparison with significant properties. The article designed and developed a methodology that would include stages for image collection, detecting commonalities among frames, selecting important frames, and detecting the time at where the relevant frame is identified. The pictures are generated by Python's OpenCV and scene classification metrics are used to assess the method. As assessed by numerous parameters, the findings shows that scene identification and accuracy are considerable. Furthermore, we investigated and researched contemporary identification and assessment techniques. Moreover, we have tested extensively our research framework on a variety of publicly available event video databases, and these outperformed several futuristic techniques. The outcomes of this research can be utilized to generate real-time definitional video assessments

Integration of Social Media News Mining and Text Mining Techniques to Determine a Corporate’s Competitive Edge

Market globalization have triggered much more severe challenges for corporates than ever before. Thus, how to survive in this highly fluctuating economic atmosphere is an attractive topic for corporate managers, especially when an economy goes into a severe recession. One of the most consensus conclusions is to highly integrate a corporate’s supply chain network, as it can facilitate knowledge circulation, reduce transportation cost, increase market share, and sustain customer loyalty. However, a corporate’s supply chain relations are unapparent and opaque. To solve such an obstacle, this study integrates text mining (TM) and social network analysis (SNA) techniques to exploit the latent relation among corporates from social media news. Sequentially, this study examines its impact on corporate operating performance forecasting. The empirical result shows that the proposed mechanism is a promising alternative for performance forecasting. Public authorities and decision makers can thus consider the potential implications when forming a future policy

Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visualization

The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique

Arquitetura híbrida de máquinas de vetores de suporte e redes neurais artificiais aplicada a classificação dos solos

Trabalho de Conclusão de Curso, apresentado para obtenção do grau de bacharel no curso de Ciência da Computação da Universidade do Extremo Sul Catarinense, UNESC.O aprendizado de máquina é uma área da inteligência artificial que tem como objetivo desenvolver aplicações que possuem a característica de aprender com suas experiências. Para isso, podem ser utilizadas diversas técnicas entre elas destacam-se as redes neurais artificiais e as máquinas de vetores de suporte. Redes neurais artificiais são modelos computacionais inspirados no cérebro, que se originaram dos estudos sobre a teoria psicológica do aprendizado em animais. As máquinas de vetor de suporte utilizam medidas estatísticas para traçar retas que são empregadas para separar conjuntos de dados com a maior distância entre si. Esta técnica vem sendo aplicada com sucesso em várias áreas, como por exemplo, em Engenharia Ambiental e Geologia. A classificação dos solos é um processo com alto custo e trabalhoso, na qual algumas vezes é necessário a predição de alguns dados. A predição das propriedades do solo quando realizada por meio da inteligência artificial possui resultados melhores do que quando empregada a forma tradicional. As máquinas de vetores de suporte apesar de serem eficientes nas resoluções de problemas de classificação, possui uma desvantagem que é o alto tempo despendido nas fases de treinamento e execução. Esta pesquisa teve como objetivo o desenvolvimento de uma arquitetura híbrida de máquinas de vetores de suporte e redes neurais artificiais aplicada a classificação dos solos. Foi utilizada uma base de dados sobre a classificação dos solos do estado de Santa Catarina, aplicando primeiramente as máquinas de vetores de suporte de forma isolada e posteriormente foi desenvolvida a arquitetura híbrida com redes neurais artificiais do tipo Kohonen. O hibridismo foi realizado empregando-se o modelo de rede neural artificial Kohonen para o agrupamento dos dados e aplicando-se uma máquina de vetores de suporte para cada um dos grupos gerados a fim de se classificar os solos. Posteriormente, por meio de métodos estatísticos avaliou-se o desempenho da aplicação das máquinas de vetores de suporte e da arquitetura híbrida, considerando-se os parâmetros de taxa de erro, tempo de treinamento, tempo de execução, acurácia, entre outras medidas de avaliação de um classificador. Foram realizados testes com diferentes quantidades de grupos gerados pela rede neural artificial, sendo elas dois, quatro, cinco, oito e quinze grupos. Ocorreram pequenas melhoras na arquitetura com dois grupos em relação a qualidade do classificador. Aumentando-se a quantidade de grupos para maior que dois, o tempo de execução apresentou melhoras significativas quando comparado com a execução das máquinas de vetores de suporte isoladamente, no entanto observou-se uma diminuição na precisão -da classificação. Nesta pesquisa, a arquitetura híbrida teve seu tempo de execução otimizado, no entanto, não foi significativo estatisticamente. Em relação a acurácia dos modelos gerados, esta se manteve a mesma nos modelos híbridos e de máquinas de vetores de suporte, que foi de aproximadamente 77,5%

An automated system for the classification and segmentation of brain tumours in MRI images based on the modified grey level co-occurrence matrix

The development of an automated system for the classification and segmentation of brain tumours in MRI scans remains challenging due to high variability and complexity of the brain tumours. Visual examination of MRI scans to diagnose brain tumours is the accepted standard. However due to the large number of MRI slices that are produced for each patient this is becoming a time consuming and slow process that is also prone to errors. This study explores an automated system for the classification and segmentation of brain tumours in MRI scans based on texture feature extraction. The research investigates an appropriate technique for feature extraction and development of a three-dimensional segmentation method. This was achieved by the investigation and integration of several image processing methods that are related to texture features and segmentation of MRI brain scans. First, the MRI brain scans were pre-processed by image enhancement, intensity normalization, background segmentation and correcting the mid-sagittal plane (MSP) of the brain for any possible skewness in the patient’s head. Second, the texture features were extracted using modified grey level co-occurrence matrix (MGLCM) from T2-weighted (T2-w) MRI slices and classified into normal and abnormal using multi-layer perceptron neural network (MLP). The texture feature extraction method starts from the standpoint that the human brain structure is approximately symmetric around the MSP of the brain. The extracted features measure the degree of symmetry between the left and right hemispheres of the brain, which are used to detect the abnormalities in the brain. This will enable clinicians to reject the MRI brain scans of the patients who have normal brain quickly and focusing on those who have pathological brain features. Finally, the bounding 3D-boxes based genetic algorithm (BBBGA) was used to identify the location of the brain tumour and segments it automatically by using three-dimensional active contour without edge (3DACWE) method. The research was validated using two datasets; a real dataset that was collected from the MRI Unit in Al-Kadhimiya Teaching Hospital in Iraq in 2014 and the standard benchmark multimodal brain tumour segmentation (BRATS 2013) dataset. The experimental results on both datasets proved that the efficacy of the proposed system in the successful classification and segmentation of the brain tumours in MRI scans. The achieved classification accuracies were 97.8% for the collected dataset and 98.6% for the standard dataset. While the segmentation’s Dice scores were 89% for the collected dataset and 89.3% for the standard dataset