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Recommender system is an emerging field of research with the advent of World Wide Web and E-commerce. Recently, an increasing usage of social networking websites plausibly has a great impact on diverse facets of our lives in different ways. Initially, researchers used to consider recommender system and social networks as independent topics. With the passage of time, they realized the importance of merging the two to produce enhanced recommendations. The integration of recommender system with social networks produces a new system termed as social recommender system. In this study, we initially describe the concept of recommender system and social recommender system and then investigates different features of social networks that play a major role in generating effective recommendations. Each feature plays an essential role in giving good recommendations and resolving the issues of traditional recommender systems. Lastly, this paper also discusses future work in this area that can aid in enriching the quality of social recommender systems.

With the evolution of social media, an enormous amount of information is shared every day. Recommender systems contribute significantly in handling big data and presenting relevant information, services and items to people. A substantial number of recommender system algorithms based on social media data have been proposed and applied to numerous domains in the literature. This paper presents a state-of-the-art survey of existing techniques of social recommender systems. We present different domains where the existing systems have been experimented. We also present a tabular representation of different metrics used by these papers. We discuss some frequently used datasets of these systems. Lastly, we discuss some of the future works in this area. The main aim of this paper is to provide a concise review of published papers to assist potential researchers in this field to devise new techniques.

The face is the most visible biometric feature considered in manual and automated systems to identify individuals. In recent years, the inventions of low-cost high-resolution mobile cameras and surveillance systems have increased the usage and popularity of facial recognition systems. Today, various mobile apps, ICT systems, electronic devices, and cloud-based systems are using facial verification to gain access and authorization based security. Now, the facial recognition system has become a generalized authentication system instead of a specialized capability of specialized applications. This increasing scope and applications are reflected as the challenges and criticalities that exist in the real environment. The technological transformation was investigated by the researchers in image types, facial capturing, facial rectification, feature generation, and classification methods and techniques of face recognition systems. This paper has provided an extensive study on each stage of standard FRS (Face Recognition System). The methods and measures provided by researchers for improving and optimizing face normalization, segmentation, feature generation, and face recognition are provided in this paper. Each of the methods is also defined along with the performance, accuracy, and robustness against various addressed challenges. The paper has provided a map between the traditional and recent methods to explore the advancement in facial verification systems.

Face expression is the appearance-based descriptive information, which is used for recognizing the emotion, behavior, and intention of an individual. In this paper, the content and textural information are utilized for the identification of facial-expression. For exploring the content features, the Gabor filter is applied to normalized face-image. The face and Gabor-face images are divided into smaller blocks. For each block, content, structure, and texture-sensitive quantitative features are extracted. This stage transformed the image into the wider content-adaptive quantitative featureset. The fuzzy-based composite filter is applied to this larger featureset for the identification of the most relevant featureset. SVM classifier with different kernels is applied to this reduced-featureset for accurate recognition of expression. The experimental validation is conducted on JAFFE and CK+ datasets. Analytical observations are collected using accuracy, sensitivity, specificity, FNR, and FPR parameters. The experimentation results show that the proposed model outperformed the state-or-art methods and achieved a significant recognition rate.

A facial extracted image does not have the equalized distribution of features over the complete image. Instead, most striking features are located within the core of the facial part. As the distance increases from the core part, the strength of these features faded and its impact on the recognition model reduces. In this paper, a coil spring structured model is presented to generate the selective features based on structured weights. These weights are assigned under the pressure, position, direction and coverage parameters of magnetic coils. The magnetic coil effect is applied to extract the facial features. These features are collected and mapped with dataset images with region consideration. This mapping is done for the individual region with physical features and coil-spring based evaluation. As the method is center settled, so that the effective recognition rate is achieved missing facial information or the wrong captured images. The experimentation is applied to the complete facial image sets as well as improper, occluded and irregular captured facial images. The comparative analysis is provided on Aberdeen, Stirling, Iranian, ORL, FERET and LFW databases. The proportionate observations are taken against six different algorithms, including LDA, PCA, ICA, LDA–PCA, SVM and PNN classifiers. Multiple sample sets are considered over each dataset under distinctive variation aspects. These variations include expression, pose, illumination, occlusion, etc. The analytical evaluation is also taken for CNN and landmark based methods. The extensive experimentation shows that model has improved the accuracy and robustness up to an extent. The recognition rate for each variation aspect is improved.

The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

With the rapid advancement of Internet, e-commerce websites and social networks, people prefer to receive recommendations from their social friends rather than strangers. Also, the exponential evolution and use of online social networks has resulted in generation of enormous amount of information over web. The relationships between users in social networks are complex, vague and uncertain for computation. Adhering to the intuition that a user’s social network plays a prominent role in influencing the personal behavior of users on web, this paper proposes a trust and semantic-based social recommendation approach to remove cold-start issues. Social relationships are used to compute trust between users in the social networks. Trusted relations are used in addition to rating matrix to extract the implicit data. For each user, we also attempt to discover the top-k semantic friends because a user is connected to multiple friends on social networks who have different tastes. This proposed approach is superior to those traditional approaches that give equal weights to all users in social networks. One important advantage of this approach is consideration of social friends at different levels. Experimental results on real-world dataset demonstrate that our proposed approach outperforms some of the state-of-the-art recommendation approaches.

Iris Recognition is gaining popularity in various online and offline authentication and multi-model biometric systems. The non-altering and non-obscuring nature of Iris have increased its reliability in authentication systems. The iris images captured in an uncontrolled environment and situation is the challenging issue of the iris recognition. In this paper, a compression robust and KPCA-Gabor fused model is presented to recognize the iris image accurately under these complexities. The illumination and noise robustness is included in this pre-processing stage for gaining the robustness and reliability against complex capturing. The effective compression features are generated as a phase pre-treatment vector using the Logarithmic quantization method. (Kernel Principal Component Analysis) KPCA and Gabor filters are applied to the rectified image for generating the textural features. The compression is also applied to Gabor and KPCA filtered images. The fuzzy adaptive content level fusion is applied to the compression image, KPCA-Compression, and Gabor-Compression iris-image. (K-Nearest Neighbors) KNN based mapping is used to this composite-fused and reduced feature set to recognize the individual. The proposed compression and fusion-feature based model is applied to CASIA-Iris, UBIRIS, and IITD datasets. The comparative evaluations against earlier approaches identify that the proposed model has improved the recognition accuracy and the reduction in error-rate is also achieved.

BCI deals to map the brain signal or activity to evaluate the human behaviour, activities or disease. The aim of this research is to utilize the different features of EEG signal to recognize the brain activity. The composite feature model with ELM classification method is presented in this research to recognize the human activity. In this paper, multiple aspects including time domain, frequency domain and least square evaluation based features are processed under ELM classifier to recognize the human-activities. Multiple quantified features are generated under each time, frequency and the least square categories. These features are processed individually and mutually with probabilistic evaluation to expand the processing-featureset. This expanded-composite featureset is trained under ELM (Extreme Learning Machine) classifier to perform intra-class and inter-class classification. The experimentation is applied on five distinctive experiments of Dataset IIIa of BCI completion III. Each experiment is conducted with variant training and testing instances. The evaluation results identified that the proposed hybrid model has achieved the average accuracy over 80%. Comparative results are generated against ANN, SVM, KNN and Multiscale Wavelet Kernel ELM by utilizing each kind of individual and mutual feature. The results taken from various experimentations have validated that the proposed model has improved the accuracy against each of the existing feature processed classification methods.

The extensive usage of internet is fundamentally changing the way we live and communicate. Consequently, the requirements of users while browsing internet are changing drastically. Recommender Systems (RSs) provide a technology that helps users in finding relevant contents on internet. Revolutionary innovations in the field of internet and their consequent effects on users have activated the research in the area of recommender systems. This paper presents issues related to the changing needs of user requirements as well as changes in the systems’ contents. The RSs involving said issues are termed as Dynamic Recommender Systems (DRSs). The paper first defines the concept of DRS and explores the various parameters that contribute in developing a DRS. The paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.