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With the advances in computing resources, the processing of huge amount of data becomes possible. However, the human ability to identify patterns from such data has not scaled accordingly. Consequently, efficient computational approaches for condensing and simplifying data are becoming essential for uncovering actionable insights, especially the big social networks. Many large datasets analyzed today are represented with the help of graphs. In many real-world applications, summarizing large graphs is beneficial to achieve a number of advantages, including 1) significant speedup for graph algorithms, 2) graph storage space reduction, 3) faster network transmission, 4) improved data privacy, 5) more effective graph visualization, etc. All these benefits can be obtained from the summarized graph, if it is summarized accurately. The quality of the summary graph is mostly measured using the Reconstruction Error (RE). In the existing literature, RE is a very challenging task. Most of the approaches investigated so far ending up with high value of RE. Hence, the summarized graph does not express the original graph completely due to the high value of the RE. Therefore, in this work we have examined how can we summarize a big graph structure into a compact summary by reducing its RE and ensuring its usefulness. For this task, we have proposed a novel graph summarization approach, called g-Sum, to calculate the graph structure summaries while minimizing RE and compare to the existing work in the domain. The functionality of the g-Sum is decomposed into three different, but interlinked phases; (1)- graph dataset pre-processing; (2)- graph partitioning; and (3)- graph summarization. We have performed an extensive series of experiments on different real-world social graph dataset, including Ego-Facebook, Enron Email and Web-Stanford graph datasets. The computed results show the effectiveness of the g-Sum and also compared with the state-of-the-art graph summarization approaches, S2L and GraSS. [ABSTRACT FROM AUTHOR]

Sentiment analysis and opinion mining is the most explored area of research where an opinion has been analysed for better decision and recommendation. Currently this field has been supported by many decision support systems that not only depend on the preferences of a decision support system (DSS) designer but also affected by the public thoughts and opinions. Potential users along with their opinions also play a vital role in decision making process. Minion's exploration using sentiment analysis is the process of analysing facts, sentiments and opinions that are expressed on different social media forums in the form of tweets or reviews. In this research work, a decision process and sentiment analysis has been amalgamated to strengthen the functionality of traditional DSS for better decision-making process from reviews of open forums. The proposed system comprises of data extraction from social media reviews, pre-processing using natural language processing (NLP), aspects extraction using part of speech (POS) tagger and normalized Google distance (NGD). Aspect optimization using genetic algorithm (GA) and finally polarity estimation of each sentiment expressed in the review using SentiWordNet is computed. The experimental results conclude the supremacy of the proposed work by adding the sentiment and opinion information performed better decision-making process than the existing state-of-art techniques. [ABSTRACT FROM AUTHOR]

Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users' transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models. [ABSTRACT FROM AUTHOR]

Objective: To determine clinical decision limits (cut off points) for vitamin D deficiency based on secondary hyperparathyroidism. Study Design: Cross sectional study. Place and Duration of Study: This study was conducted in the Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology (AFIP), from Dec 2016 to May 2017. Methodology: One hundred and sixteen subjects, aged 18 to 60 years, of either gender, with vitamin D levels less than 50nmol/L, were consecutively included in the study. Results: Serum 25(OH) D had inverse relation with plasma iPTH level (r=-0.597, p<0.0001). Thirty five percent of the subjects with vitamin D level below 50nmol/L had secondary hyperparathyroidism. However, the frequency of secondary hyperparathyroidism was 52% in subjects with 25 (OH) D level less than 25nmol/L compared with 28% having serum vitamin D levels of 25-40nmol/L. None of the patient with Vitamin D level above 40nmol/L had secondary hyperparathyroidism. Receiver operating characteristic (ROC) curve showed that 25(OH) D level of 25.5nmol/L as an optimal cut off level for vitamin D deficiency based on Secondary Hyperparathyroidism with area under the curve (AUC) was 0.761. Conclusion: High frequency of secondary hyperparathyroidism in adults with vitamin D levels under 25nmol/L necessitates reconsideration of vitamin D cutoff limit for bone health. [ABSTRACT FROM AUTHOR]

In present research investigations, enzyme (catalase) secretion, salt tolerance and Geneticin response against few strains of Xanthomonas oryzae pv oryzae was checked under laboratory conditions. Consequences regarding enzyme efficacy (catalase) varied among the isolates. The findings regarding temperature tolerance unveiled that all the strains were able to grow at 1%, 3%, 5% NaCl concentration. Moreover, Geneticin against tested bacteria exhibited development of maximum inhibition zone i.e. 3, 4, 5 milimeter in eleven days of incubation period against 6 isolates; ASNNBXI, ASNNB-XII, ASNNB-XVIII, ASNNB-XIV, ASNNB-XV, ASNNB-X. Moreover, strains ASNNB-XIII, ASNNB-XII exhibited minimum inhibition zone i.e. 0 millimeters against12 isolates. [ABSTRACT FROM AUTHOR]

In third world countries like Pakistan, the production of electricity has been quickly reduced in past years due to rely on the fossil fuel. According to a survey conducted in 2017, the overall electrical energy capacity was 22,797MW, since the electrical grids have gone too old, therefore the efficiency of grids, goes down to nearly 17000MW. Significant addition of fossil fuel, hydro and nuclear is 64.2%, 29% and 5.8% respectively in the total electricity production in Pakistan. In 2018, the demand crossed 20,223MW, compared to peak generation of 15,400 to 15,700MW as by the Ministry of Water and Power. Country faces a deficit of almost 4000MW to 5000MW for the duration of 2019 hot summer term. Focus on one aspect considering Demand Side Management (DSM) cannot oversea the reduction of gap between power demand and customer supply, which eventually leads to the issue of load shedding. Hence, a scheduling scheme is proposed in this paper called RPSMDSM that is based on selection of those appliances that need to be only Turned-On, on priority during peak hours consuming minimum energy. The Home Energy Management (HEM) system is integrated between consumer and utility and bidirectional flow is presented in the scheme. During peak hours of electricity, the RPSMDSM is capable to persuade less power consumption and accomplish productivity in load management. Simulations show that RPSMDSM scheme helps in scheduling the electricity loads from peak price to off-peak price hours. As a result, minimization in electricity cost as well as (Peak-to-Average Ratio) PAR are accomplished with sensible waiting time. [ABSTRACT FROM AUTHOR]

The assurance of astounding requirements and system specifications requires a tremendous exertion and requires semantically incorporated prerequisites. Particularly when utilizing diverse portrayals of requirements, the semantic integration is very troublesome. The present prerequisites management tools incorporate requirements just syntactically and, along these lines, give just constrained features to help the requirements analysis amid quality assurance. Sentiment analysis is a rising field of research and it assesses individuals' perspectives, thoughts or feelings. Amid the most recent decade, the research community concentrated on distinguishing and separating aspects. Some methodologies for aspect extraction depend on term frequency and frequency. These methodologies are very great if aspects are related to predefined classes and may fall flat if the concern is aspects with low frequency. However, the execution time for these systems increments because of the expansion in aspect set of data. Additionally, redundant and irrelevant aspects take an interest in deciding the assessment record related to the product, in this way precision of the algorithm is shifted. In this exploration, a segregational approach has been shown for perspective aspect extraction and aspect refinement utilizing similarity concept for the improvement of the quality of the product. To acquire preferable precision over existing methodologies we will additionally develop an arrangement of Visuword and integrate it with the proposed procedure. The experimental investigation will uncover that projected system beats the existing counter parts. [ABSTRACT FROM AUTHOR]

A massive number of daily spam demands a method of anti-spam filtering that can efficiently detect unsolicited emails. Traditional spam filtering techniques block spam at receiver's side, unable to stop their propagation on the network, resulting in wastage of network bandwidth, processing power, and disk space. To address these issues, this paper proposes a methodology for sender oriented anti-spamming that identifies and filters spam at its origin, thus not only discourages spam but also reduces the resource wastage. To the best of our knowledge, addressing this wastage by spam has not been done before. Along with blocking spam at origin, this research proposes a diagonal hash algorithm to optimize image spam filtering, and email hash token generator to promote email authentication and discourage zombie accounts. Experiments suggest that the proposed technique has the potential to achieve better performance and accuracy compared to the current approaches. [ABSTRACT FROM AUTHOR]

The development of chronic kidney disease (CKD) in people with diabetes is commonplace, and is frequently associated with a significant and unfavourable impact on patient outcomes along with a substantial economic burden. With the development of novel classes of drug therapies in diabetes, there has been a recent focus on cardiovascular safety measures, with dedicated cardiovascular outcome trials (CVOTs) carried out for all new diabetes medications. More recently, there has been a growing regulatory view that such trials should report more specific renal outcomes to ensure simpler comparability between drugs and drug classes. This article explores some of the possible mechanisms by which these drugs may improve renal function in people with diabetes, and it reviews important CVOTs that have reported renal outcomes to date. These include CVOTS of sodium-glucose cotransporter-2 inhibitors (EMPA-REG OUTCOME study, CANVAS study, CREDENCE trial, DECLARE-TIMI trial and DAPA-HF study), dipeptidyl peptidase-4 inhibitors (EXAMINE trial, SAVOR-TIMI 53, TECOS trial and CARMELINA trial) and glucagon-like peptide-1 analogues (ELIXA trial, LEADER trial, SUSTAIN-6 trial, PIONEER-6 trial, EXSCEL trial, HARMONY Outcomes study and the REWIND study). Ongoing cardiovascular and renal outcome studies such as Dapa-CKD, EMPA-KIDNEY, EMPEROR-Preserved and EMPEROR-Reduced are also discussed. The heterogeneity of patient characteristics and reported renal outcomes, which hinders comparisons between trials and drug classes, is highlighted. Novel classes of diabetes therapies present an important opportunity for nephroprotection beyond the blockade of the renin–angiotensin–aldosterone system in this high-risk group. Clinicians should be aware of such benefits when prescribing these medications for people with, and possibly those without, type 2 diabetes. [ABSTRACT FROM AUTHOR]

Microblogs and social media sites have gained a central place and people use these platforms to express their opinions, sentiments, and thoughts about products, news, events, blogs, etc. Sentiment analysis is the process of exploring opinions and sentiments in user reviews and tweets. This area is still in its early developmental phase and requires imperative improvements on various issues. One of the main issues is multilingual tweets and reviews. Earlier sentiment analysis techniques only classified the text of a specific language, i.e., English, Turkish, etc. The accuracy of these techniques decreases in the presence of multilingual text. Existing methods are domain oriented. Using BERT and a lexicon, we propose a method for sorting out multilingual text and improving the polarity calculation of reviews. Experimental results reveal that our proposed technique achieved 90.14% accuracy and outperformed existing aspect-based sentiment analysis techniques. [ABSTRACT FROM AUTHOR]

Alzheimer's diseases (AD) and dementia are among the highly prevalent neurological disorders characterized by deposition of beta amyloid (Aβ) plaques, dense deposits of highly phosphorylated tau proteins, insufficiency of acetylcholine (ACh) and imbalance in glutamatergic system. Patients typically experience cognitive, behavioral alterations and are unable to perform their routine activities. Evidence also suggests that inflammatory processes including excessive microglia activation, high expression of inflammatory cytokines and release of free radicals. Thus, targeting inflammatory pathways beside other targets might be the key factors to control- disease symptoms and progression. This review is aimed to highlight the mechanisms and pathways involved in the neuroprotective potentials of lead phytochemicals. Further to provide updates regarding challenges associated with their use and their progress into clinical trials as potential lead compounds. Most recent scientific literature on pre-clinical and clinical data published in quality journals especially on the lead phytochemicals including curcumin, catechins, quercetin, resveratrol, genistein and apigenin was collected using SciFinder, PubMed, Google Scholar, Web of Science, JSTOR, EBSCO, Scopus and other related web sources. Literature review indicated that the drug discovery against AD is insufficient and only few drugs are clinically approved which have limited efficacy. Among the therapeutic options, natural products have got tremendous attraction owing to their molecular diversity, their safety and efficacy. Research suggest that natural products can delay the disease onset, reduce its progression and regenerate the damage via their anti-amyloid, anti-inflammatory and antioxidant potentials. These agents regulate the pathways involved in the release of neurotrophins which are implicated in neuronal survival and function. Highly potential lead phytochemicals including curcumin, catechins, quercetin, resveratrol, genistein and apigenin regulate neuroprotective signaling pathways implicated in neurotrophins-mediated activation of tropomyosin receptor kinase (Trk) and p75 neurotrophins receptor (p75NTR) family receptors. Phytochemicals especially phenolic compounds were identified as highly potential molecules which ameliorate oxidative stress induced neurodegeneration, reduce Aβ load and inhibit vital enzymes. Yet their clinical efficacy and bioavailability are the major challenges which need further interventions for more effective therapeutic outcomes. [ABSTRACT FROM AUTHOR]

Mg0.95Ni0.05Ti0.98Zr0.02O3 and CaTiO3 were prepared separately using solid state reaction method. The effect of CaTiO3 addition on the microwave dielectric properties of Mg0.95Ni0.05Ti0.98Zr0.02O3 was investigated to get low loss and temperature stable ceramics in (1 – x)Mg0.95Ni0.05Ti0.98Zr0.02O3-xCaTiO3 series. Mg0.95Ni0.05Ti0.98Zr0.02O3 formed as the major phase along with Mg0.95Ni0.05Ti2O5 phase that formed as minor secondary phase for the composition with x = 0. Microwave dielectric properties ∈r ~ 17.1, Qufo of 195,855 GHz and τf of –46.3 ppm/°C were obtained for the composition with x = 0. The positive τf value of CaTiO3, tuned the τf value of Mg0.95Ni0.05Ti0.98Zr0.02O3 through zero and ∈r ~ 28.4, Qufo ~ 108,775 GHz and τf ~ 3.1 ppm/°C were attained for x = 0.15 in this study. This composition is the best choice for high frequency applications. [ABSTRACT FROM AUTHOR]

High dimensionality is a well-known problem that has a huge number of highlights in the data, yet none is helpful for a particular data mining task undertaking, for example, classification and grouping. Therefore, selection of features is used frequently to reduce the data set dimensionality. Feature selection is a multi-target errand, which diminishes dataset dimensionality, decreases the running time, and furthermore enhances the expected precision. In the study, our goal is to diminish the quantity of features of electroencephalography data for eye state classification and achieve the same or even better classification accuracy with the least number of features. We propose a genetic algorithm-based feature selection technique with the KNN classifier. The accuracy is improved with the selected feature subset using the proposed technique as compared to the full feature set. Results prove that the classification precision of the proposed strategy is enhanced by 3 % on average when contrasted with the accuracy without feature selection. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis is an emerging field of research that evaluates people's views, ideas or sentiments. It is a subtask of sentiment analysis that is used to identify text sentiment orientation towards different aspects of a mobile phone such as camera and screen resolution. During the last decade, research community focused on identifying and extracting aspects like the most common methods used for aspect extraction to identify the main features of an entity only. These techniques are corpus or lexicon based and domain specific. Some approaches for aspect extraction are based on term frequency and inverse document frequency. Such approaches are quite good if aspects are associated with predefined categories and may fail if low-frequency aspects are concerned. The heuristic-based approaches are better than frequency and lexicon-based approaches in terms of accuracy, but due to the different combinations of features, they consumed time. The researchers have already implemented machine learning techniques to analyse sentiments present in the given document. But, execution time for these techniques increases due to the increasing aspects in a set of data. Also, irrelevant and redundant aspects participate in determining the sentiment of the given document, thereby varying the accuracy of the algorithm. In this research, we present a segregational approach for aspect identification that is based on aspect and opinion words disentangling and aspect refinement using concept similarity. To obtain better accuracy, we also built a set of part of speech tagger and integrated it with our proposed technique. The experimental analysis reveals that our proposed technique outperforms the existing counterparts. [ABSTRACT FROM AUTHOR]