Consumers undergo an intellectual burden when working with technological programs. Mostly in situations of several activities. For instance, while communicating when driving with the navigation device. It is not necessary to divert users from their primary duties in such circumstances. In memory cycles and related workload, the pre-frontal cortex (PFC) has a significant role to play. In this study, we have used data from 10 participants to evaluate the task behaviors in PFC with usable near-infrared spectroscopy (fNIRS), which is a non-invasive imaging modality. In classification, CNN research has been state of the art. This has undermined the need to extract features manually. In order to assess the mental workload, we implemented a time-frequency approach with CNN approach. Rather than traditional CNN network we used ResNet50 pretrained network here. Application of Wigner-Ville Distribution in Functional Imaging is introduced here. The proposed CNN approach achieves a considerable average improvement relative to conventional methods. The results across differences in time window length are benchmarked. Satisfactory result obtained with twenty five second window for which the CNN yields 98% correct classification where traditional CNN achieved 89% accuracy.

Processing images efficiently may be influenced by some important factors which are the techniques chosen, the field of study and the quality of images. In this work, we study the field of agriculture with the focus on the early detection of plant diseases through image processing. To detect plant diseases such bacterial diseases, fungal diseases and virus, two main techniques exist: The traditional techniques provided by agricultural experts during visit on the field and the artificial techniques based on images processing algorithms. Since plantations are usually distant from the cities where experts are not easy to find, the artificial techniques incorporated in computer programs become suitable. The modern techniques used to analyse images rely on existing algorithms such as k-nearest neighbor, k-means clustering, fuzzy logic, genetic algorithm, neural networks, etc. Five main phases characterise the process of images analysis: image acquisition, pre-treatment, segmentation, feature extraction and classification. Amongst these phases, we particularly focus on the segmentation which allows to locate portions of leaf that are affected by a disease. Doing so, in this paper we propose a method to evaluate segmentation algorithms (k-means clustering, canny edge and k-nearest neighbor) on the diagnostic of diseases of three of the most cultivated plants (corn, potato, tomato) in the region of study. We study and compare performance values using the ROC-AUC of disease classification using the Support Vector Machine (SVM) algorithm. The obtained results show that the canny edge algorithm produces very poor performances on the family of solanaceae plants including potato. The k-nearest neighbour algorithm produces very poor performance due to the difficulty of choosing the k-value. Finally, the k-means algorithm makes it possible to obtain good prediction rates on all the chosen plants.

Accurate medical image processing plays a crucial role in several clinical diagnoses by assisting physicians in timely treatment of wounds and mishaps. Medical doctors in the hospitals generally rely on examining bone x-ray images based on their expertise, knowledge and  past experiences in determining whether a fracture exist in bone or not. Nevertheless, majority of fractures identification methods using X-rays in the hospitals is beyond human understanding due to variation in different attributes of fracture and complication of bone organization thereby making it difficult for doctors to correctly diagnose and proffer adequate treatment to patient ailments. The need for robust diagnostic image processing techniques for image segmentation for different bone structures cannot be overemphasized. This research implemented different image segmentation techniques on a bone x-ray image in order to identify the most efficient for timely medical diagnosis. Also, the strength and weaknesses of the diverse segmentation techniques were also identified. This will empowered researchers with appropriate knowledge needed to improve and build better image segmentation models which doctors can use in handling complex medical image processing problems. Also, miss rate in bone X-rays that contains multiple abnormalities can be lowered by using appropriate image segmentation techniques thereby improving some of the labor intensive work of medical personnel during bone diagnosis.  MATLAB 9.7.0 programing tool was used for the implementation of the work. The results of X-ray bone segmentation revealed that active contour model using snake model showed the best performance in detecting boundaries and contours of regions of interest when used in segmenting Femur bone image than the other medical image segmentation approaches implemented in the work.

Nowadays in the digital world, there are lots of videos being uploaded to video, and social media sharing platforms are growing exponentially. About the Internet and multimedia technologies, illicitly copied content is a serious social problem. Since the internet is accessible to everyone, it is easy to download content and re-upload it. Copying videos from the internet can be considered plagiarism. In this paper, a method is proposed for feature extraction of video plagiarism detection. This framework is based on the local features to identify the videos frame by frame with the videos stored in the database. It becomes important to review the existing video plagiarism detection methods, compare them through appropriate performance metrics, list out their pros and cons and state the open challenges. First of all, it will pre-process the data with the help of SIFT and OCR Feature extraction. After that, the system applies the video retrieval and detection function using the two classifier algorithm the SVM, and the KNN. In the first stage, when the query is compared to all training data, KNN calculates the distances between the query and its neighbors and selects the K nearest neighbors. It is applied in the second stage to recognize the object using the SVM algorithm. Here we use the VSD dataset to predict the plagiarized videos. And the accuracy of these plagiarized videos after comparing them is 98%. 

Fires spread quickly and are extremely difficult to contain, and cause a great deal of damage to people and property. Current domestic systems for detecting outbreaks of fire, such as smoke detectors, are prone to reliability issues and will benefit greatly from having a secondary system in place to confirm the presence of a fire in the premises. In this paper, we have proposed a novel image pre-processing algorithm known as the Selective Amplification. This technique enhances images that are to be used in Convolutional Neural Networks, which are then trained on pre-processed images to detect fires with high accuracy. The efficacy of the proposed technique is verified by training two identical Convolutional Neural Network models on the same dataset of images. We train the proposed model on a version of the dataset that uses Selective Amplification for data pre-processing. The proposed model then demonstrates an improvement in the accuracy of the detection of fire in real-time over by 12.85%, compared to an identical model trained on the dataset without any pre-processing performed beforehand.