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Over the past years, a digital multimedia uprising has been experienced in every walk of life, due to which the un-annotated or unstructured multimedia content has always been a key issue for research. The multimedia content is usually created with some intended emotions, which the creator wants to induce in viewers. The affectiveness of the multimedia content can be measured by analyzing elicited emotions of its viewers. In this paper, we present a rigorous study of human cognition using EEG signals while watching a video, to analyze the affectiveness of video content. The analysis presented in this paper is done to establish an effective relationship between video content and the human emotional state. For this, the most effective scalp location and frequency ranges are identified for two categories of videos, i.e., excited and sad. Furthermore, a common affective response (CAR) is extracted for finding the distinguishable features for aforementioned categories of videos. The CAR is calculated and tested on the publicly available dataset “AMIGOS,” and the results presented here show the utility of cognitive features on extracted scalp locations and frequency ranges for automatic tagging of video content. The current research explores the innovative applicability of neuro-signals for a mouse-free video tagging based on human excitement level to augment a range of brain–computer interface (BCI)-based devices. It can further aid to automatically retrieve the video content which is exciting and interesting to human viewers. With this analysis, we aimed to provide a thorough analysis which can be used to customize a low-cost and mobile EEG system for automatic analysis and retrieval of videos.

Artificial intelligence systems are becoming ubiquitous in everyday life as well as in high-risk environments, such as autonomous driving, medical treatment, and medicine. The opaque nature of the deep neural network raises concerns about its adoption in high-risk environments. It is important for researchers to explain how these models reach their decisions. Most of the existing methods rely on softmax to explain model decisions. However, softmax is shown to be often misleading, particularly giving unjustified high confidence even for samples far from the training data. To overcome this shortcoming, we propose Bayesian model uncertainty for producing counterfactual explanations. In this paper, we compare the counterfactual explanation of models based on Bayesian uncertainty and softmax score. This work predictively produces minimal important features, which maximally change classifier output to explain the decision-making process of the Bayesian model. We used MNIST and Caltech Bird 2011 datasets for experiments. The results show that the Bayesian model outperforms the softmax model and produces more concise and human-understandable counterfactuals.

The purpose of this paper is to compare the strategies of companies with data science practices and methodologies and the data specificities/variables that can influence the definition of a data science strategy in pharma companies. The current paper is an empirical study, and the research approach consists of verifying against a set of statistical tests the differences between companies with a data science strategy and companies without a data science strategy. We have designed a specific questionnaire and applied it to a sample of 280 pharma companies. The main findings are based on the analysis of these variables: overwhelming volume, managing unstructured data, data quality, availability of data, access rights to data, data ownership issues, cost of data, lack of pre-processing facilities, lack of technology, shortage of talent/skills, privacy concerns and regulatory risks, security, and difficulties of data portability regarding companies with a data science strategy and companies without a data science strategy. The paper offers an in-depth comparative analysis between companies with or without a data science strategy, and the key limitation is regarding the literature review as a consequence of the novelty of the theme; there is a lack of scientific studies regarding this specific aspect of data science. In terms of the practical business implications, an organization with a data science strategy will have better direction and management practices as the decision-making process is based on accurate and valuable data, but it needs data scientists skills to fulfil those goals. info:eu-repo/semantics/acceptedVersion

Cancer is a genetic disease that is categorized among the most lethal and belligerent diseases. An early staging of the disease can reduce the high mortality rate associated with cancer. The advancement in high throughput sequencing technology and the implementation of several Machine Learning algorithms have led to significant progress in Oncogenomics over the past few decades. Oncogenomics uses RNA sequencing and gene expression profiling for the identification of cancer-related genes. The high dimensionality of RNA sequencing data makes it a complex and large-scale optimization problem. CDRGI presents a Discrete Filtering technique based on a Binary Artificial Bee Colony coupling Support Vector Machine and a two-stage cascading classifier to identify relevant genes and detect cancer using RNA seq data. The proposed approach has been tested for seven different cancers, including Breast Cancer, Stomach Cancer (STAD), Colon Cancer (COAD), Liver Cancer, Lung Cancer (LUSC), Kidney Cancer (KIRC), and Skin Cancer. The results revealed that the CDRGI performs better for feature reduction while achieving better classification accuracy for STAD, COAD, LUSC and KIRC cancer types.

Agriculture industry is the foundation of Indian economy where quality fruit production plays an important role. Apple or pome fruits are always in demand because of rich nutrients in it. Hence, to analyze and recognize the nutrients deficiency in fruits, a deep neural-based model is being proposed. This model automatically classifies and recognizes the type of deficiency present in apple. In this paper, a database has been created for four major types of nutrients deficiency in apples and used for training and validation of the proposed deep convolutional network. The model is tuned with k-fold cross-validation. The hyper-parameters such as epoch are set at 100 and batch size kept at 5. Finally, the model is tested with the testing data and achieved an average accuracy of 98.24% with k-fold cross-validation set to 15. The model accuracy depends on the hyper-parameters. The process of features optimization reduces the risk of overfitting of the model. Hence, careful selection of hyper-parameters is important for the convergence of cost function to the global minima that results in minimum misclassification.

Skin cancer is one of the diseases which lead to death if not detected at an early stage. Computer-aided detection and diagnosis systems are designed for its early diagnosis which may prevent biopsy and use of dermoscopic tools. Numerous researches have considered this problem and achieved good results. In automatic diagnosis of skin cancer through computer-aided system, feature extraction and reduction plays an important role. The purpose of this research is to develop computer-aided detection and diagnosis systems for classifying a lesion into cancer or non-cancer owing to the usage of precise feature extraction technique. This paper proposed the fusion of bag-of-feature method with speeded up robust features for feature extraction and quadratic support vector machine for classification. The proposed method shows the accuracy of 85.7%, sensitivity of 100%, specificity of 60% and training time of 0.8507 s in classifying the lesion. The result and analysis of experiments are done on the PH2 dataset of skin cancer. Our method improves performance accuracy with an increase of 3% than other state-of-the-art methods.