Users' Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures.

The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing online information, new opportunities emerged, and diverse issues have been raised, which have attracted researchers to address these research problems. In this current age, where online business and e-commerce are part of our daily lives, recommender systems (RSs) are very effective for information filtering. RSs play a significant role in our lives by assisting users in recommending items and services what they may be interesting in to purchase or avail. In this research work, our goal is to predict the users' ratings for various items, which are an active research area in collaborative filtering (CF). In this work, we have explored various similarity measures based on user-user and item-item rating predictions on different datasets by applying collaborative filtering approaches. The comparison of item-item and user-user CF algorithms such as user K-Nearest Neighbour using cosine; similarity, Pearson correlation as well as item-based K-NN using these measures with baseline approaches and matrix-based methods such as Matrix factorization (MF), biased MF, and factor wise MF has been carried out. For empirical-based comparison analysis, diverse approaches have been selected such as slope one, random, and global average, and it revealed that item-item K-NN using Pearson correlation has outperformed all other applied approaches. For the experiments, three real world and widely used datasets of MovieLens 1M, CiaoDVD, and MovieLens 100k have been used. The empirical-based results have been evaluated by using standard performance evaluation measures of RMSE and MAE.

Viral impacts on honey bee populations: A review.

Honey bee is vital for pollination and ecological services, boosting crops productivity in terms of quality and quantity and production of colony products: wax, royal jelly, bee venom, honey, pollen and propolis. Honey bees are most important plant pollinators and almost one third of diet depends on bee's pollination, worth billions of dollars. Hence the role that honey bees have in environment and their economic importance in food production, their health is of dominant significance. Honey bees can be infected by various pathogens like: viruses, bacteria, fungi, or infested by parasitic mites. At least more than 20 viruses have been identified to infect honey bees worldwide, generally from Dicistroviridae as well as Iflaviridae families, like ABPV (Acute Bee Paralysis Virus), BQCV (Black Queen Cell Virus), KBV (Kashmir Bee Virus), SBV (Sacbrood Virus), CBPV (Chronic bee paralysis virus), SBPV (Slow Bee Paralysis Virus) along with IAPV (Israeli acute paralysis virus), and DWV (Deformed Wing Virus) are prominent and cause infections harmful for honey bee colonies health. This issue about honey bee viruses demonstrates remarkably how diverse this field is, and considerable work has to be done to get a comprehensive interpretation of the bee virology.

Skin Cancer Detection: A Review Using Deep Learning Techniques.

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

Fighting adult illiteracy with the help of the environmental print material.

Adult illiteracy is a major problem worldwide especially in developing countries. Adult Basic Education (ABE) programs working in this context are not very effective due to lack of motivation for the people who are not literate. The reason is inadequate learning content and content delivery methods. This situation calls for developing novel learning content and a learner-directed content delivery approach. This paper presents an exploratory study investigating the use of the Environmental Print Material (EPM) as learning content for the non-literate population of Pakistan. The EPM content is presented to the adult non-literate population in two ethnographic studies. The most frequently recognized content is selected and utilized as learning content in a Computer Assisted Learning (CAL) application. An empirical study is conducted upon two groups with 107 participants to compare the EPM-based learning content with Traditional Learning Content (TLC). As many as 54 participants participated in the experimental group (presented with EPM-based learning content), whereas 53 participants took part in the control group (presented with TLC content). The results reveal that the experimental group performed significantly better compared to the control group in recognition, pronunciation, and recall of the presented content. The meta-analysis of the results shows a large effect size of (1.05) with confidence interval in the range (0.798-1.315). The results claim that the EPM has potential to be considered as learning content in the ABE programs.

Honey: Single food stuff comprises many drugs.

Honey is a natural food item produced by honey bees. Ancient civilizations considered honey as a God gifted prestigious product. Therefore, a huge literature is available regarding honey importance in almost all religions. Physically, honey is a viscous and jelly material having no specific color. Chemically, honey is a complex blend of many organic and inorganic compounds such as sugars, proteins, organic acids, pigments, minerals, and many other elements. Honey use as a therapeutic agent is as old as human civilization itself. Prior to the appearance of present day drugs, honey was conventionally used for treating many diseases. At this instant, the modern research has proven the medicinal importance of honey. It has broad spectrum anti-biotic, anti-viral and anti-fungal activities. Honey prevents and kills microbes through different mechanism such as elevated pH and enzyme activities. Till now, no synthetic compound that works as anti-bacterial, anti-viral and anti-fungal drugs has been reported in honey yet it works against bacteria, viruses and fungi while no anti-protozoal activity has been reported. Potent anti-oxidant, anti-inflammatory and anti-cancerous activities of honey have been reported. Honey is not only significant as anti-inflammatory drug that relieve inflammation but also protect liver by degenerative effects of synthetic anti-inflammatory drugs. This article reviews physico-chemical properties, traditional use of honey as medicine and mechanism of action of honey in the light of modern scientific medicinal knowledge.

MIIB: A Metric to Identify Top Influential Bloggers in a Community.

Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.