The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets

Comparative Study of Artificial Neural Network based Classification for Liver Patient

The extensive accessibility of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to steadily select the most appropriate strategy to cope with clinical prediction problems. Data mining offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. Therefore, in this study, Liver patient data is considered and evaluated by univariate analysis and a feature selection method for predicator attributes determination. Further comparative study of artificial neural network based predictive models such as BP, RBF, SOM, SVM are provided. Keywords: Medical Informatics, Classification, Liver Data, Artificial Neural Networ

S-Divergence-Based Internal Clustering Validation Index

A clustering validation index (CVI) is employed to evaluate an algorithm’s clustering results. Generally, CVI statistics can be split into three classes, namely internal, external, and relative cluster validations. Most of the existing internal CVIs were designed based on compactness (CM) and separation (SM). The distance between cluster centers is calculated by SM, whereas the CM measures the variance of the cluster. However, the SM between groups is not always captured accurately in highly overlapping classes. In this article, we devise a novel internal CVI that can be regarded as a complementary measure to the landscape of available internal CVIs. Initially, a database’s clusters are modeled as a non-parametric density function estimated using kernel density estimation. Then the S-divergence (SD) and S-distance are introduced for measuring the SM and the CM, respectively. The SD is defined based on the concept of Hermitian positive definite matrices applied to density functions. The proposed internal CVI (PM) is the ratio of CM to SM. The PM outperforms the legacy measures presented in the literature on both superficial and realistic databases in various scenarios, according to empirical results from four popular clustering algorithms, including fuzzy k-means, spectral clustering, density peak clustering, and density-based spatial clustering applied to noisy data

Clinical study of chemotherapy induced febrile neutropenia: talcott’s versus multinational association for supportive care in cancer risk assessment scoring systems

Background: Cancer is a leading cause of death worldwide, accounting for 8.2 million deaths in 2012. Febrile neutropenia (FN) is fever associated with abnormally low neutrophil count signifying an immunocompromised state secondary to malignancy or its treatment. The aim of this study was to evaluate clinical outcome of chemotherapy induced febrile neutropenia.Methods: This was a hospital based prospective, descriptive observational study. Patients of either sex, age (18-90 years), with cancer on chemotherapy, single oral temperature ≥101°Fahrenheit (38.3°C) or a temperature ≥100.4° Fahrenheit (38.0° C) for ≥ one hour with absolute neutrophil counts <500 cells/mm3 or <1000 cells/mm3 with a predicted decrease to less than 500 cells/mm3 in the next 24 hours, only with first febrile episode occurring during study period and prior or concurrent radiation therapy were included in this study.Results: Among 87 patients, 70 (80.5%) were less than 60 years and 17 (19.5%) were ≥60 years. The mean age of study patients was 44.46±15 years, (range 18 to 77 years), 31(35.6%) were male and 56 (64.4%) were female. Talcott’s and MASCC risk predicting tool versus outcome, p values for Talcott’s and MASCC were significant (<0.05).Conclusions: Neutropenic fever is a potentially life-threatening complication of cancer chemotherapy. MASCC and Talcott’s model can be used to identify low and high risk patients. MASCC risk index may have a better performance than the Talcott’s model in risk classification

A Perspective on Plasmonics within and beyond the Electrostatic Approximation

Plasmonic is an emerging branch of nanophotonics wherein the electromagnetic properties of nanoparticles are studied for variety of applications. The optics of nanoparticles is studied in terms of surface plasmon resonances and optical cross section. Initially the first principle approach has been used to study the plasmonic fundamentals known as electrostatic approach. Under this approach, various parameters are taken into account to observe the electromagnetic properties of plasmonic nanogeometries. This electrostatic model is only used to analyze the optical signature of smaller size plasmonic geometries. Therefore, for the estimation of optical properties of larger size nanoparticle numerical model (Discrete Dipole Approximation) has been used. The observed surface plasmon resonances could be useful in sensing field, SERS signal detection and thin film solar cell application

Evolution of VANETS to IoV: Applications and Challenges

Advancement in wireless communication technology along with the evolution of low power computational devices, have given rise to the Internet of things paradigm. This paradigm is transforming conventional VANETs into Internet-of- vehicles. This transition has led to a substantial commercial interest; as a result, there has been a significant boost in the field of the Internet of vehicles during the past few years. IoV promises a wide range of applications of commercial interest as well as public entertainment and convenience (collision warning systems, on-demand in-car entertainment, smart parking, traffic information). Applications related to vehicular and passenger safety are particularly of great commercial as well as a research interest as such IoV is going to be a core component in implementing the smart city concept. This paper gives an overview of the transition of conventional VANETs to IoV and highlights the potential applications and challenges faced by the Internet of Vehicles (IoV) paradigm

Extended galactic rotational velocity profiles in f(R)f(R) gravity background

An attempt has been made to explore the galactic dynamics via the rotational velocity beyond the Einstein's geometric theory of gravity. It is inspired from the geometric relation obtained in the power law $f(R)$ gravity model in vacuum. We analyse the action with a small positive deviation from the Einstein-Hilbert action (taking $R$ as $f(R)\propto R^{1+\delta}$) at the galactic scales for the explanation of cosmological dark matter problem and obtain the contribution of dynamical $f(R)$ background geometry in accelerating the test mass. In the weak field limits, we obtain the effective acceleration of the test mass due to a massive spherically symmetric source in $f(R)$ background and develop an equation for the rotational velocity. We test the viability of the model by tracing the motion of test mass outside the typical galactic visible boundaries without considering any dark matter halo profile. We obtain a nice agreement in the outer regions (up to few tens of kpc beyond the visible boundary) of the typical galaxy by using the known galaxy data.\\ We further explore the galactic dynamics for a galaxy NGC 1052 of which the dark matter deficient galaxies, i.e., DF2 and DF4 are a part (satellite galaxies) and discuss plots of the dynamical feature of rotation curves in $f(R)$ background for the model parameter $\delta<<1$ and interpret the results for its satellite galaxies.Comment: 7 pages, 5 figure

Light deflection angle through velocity profile of galaxies in f(R)f(R) model

We explore a new realisation of the galactic scale dynamics via gravitational lensing phenomenon in power-law $f(R)$ gravity theory of the type $f(R)\propto R^{1+\delta}$ with $\delta<<1$ for interpreting the clustered dark matter effects. We utilize the single effective point like potential (Newtonian potential + $f(R)$ background potential) obtained under the weak field limit to study the combined observations of galaxy rotation curve beyond the optical disk size and their lensing profile in $f(R)$ frame work. We calculate the magnitude of light deflection angle with the characteristic length scale (because of Noether symmetry in $f(R)$ theories) appearing in the effective $f(R)$ rotational velocity profile of a typical galaxy with the model parameter $\delta \approx O(10^{-6})$ constrained in previous work. For instance, we work with the two nearby controversial galaxies NGC 5533 and NGC 4138 and explore their galactic features by analysing the lensing angle profiles in $f(R)$ background. We also contrast the magnitudes of $f(R)$ lensing angle profiles and the relevant parameters of such galaxies with the generalised pseudo-isothermal galaxy halo model and find consistency.Comment: 7 pages, 5 figure

Probiotics in acute diarrhea: A randomized control trial

Background: Probiotics have been used for long in the treatment of acute diarrhea although their efficacy has always&nbsp;remains the subject of discussion. Objective: To determine the effect of probiotics in acute diarrhea among the children in&nbsp;rural population. Method: Double-blinded randomized control trial. We included children of age group 6 months - 5 years&nbsp;suffering from acute diarrhea of &lt;48 h and fulfilling the inclusion criteria. All children were given oral rehydration salts&nbsp;(ORS) ad-lib till the resolution of diarrhea and zinc 20 mg/day for 14 days while intervention arm (n=101) were given&nbsp;probiotic sachet twice a day for 7 days containing Streptococcus faecalis 30 million, Clostridium butyricum 2 million,&nbsp;Bacillus mesentericus 1 million, Lactobacillus sporogenes 50 million, control group were given identical placebo apart from&nbsp;ORS and zinc. Duration of diarrhea in both the groups was measured as primary outcome while secondary outcome was&nbsp;to know the days of maximum recovery from diarrhea in both groups. Results: Totally, 207 patients were randomized to&nbsp;control and study group, out of which, 195 completed the study. Out of total 195 patients, 94 (48.2%) patients were treatedwith standard treatment of diarrhea without probiotics while 101 (51.8%) patients were given probiotics apart from standard&nbsp;treatment of diarrhea. The mean duration of diarrhea was found to be reduced in the study group (4.6 days [2.84-4.776 days])&nbsp;as compared to control group (5.31 days [5.108-5.512 days]), p&lt;0.001. Conclusion: Probiotics significantly reduced the&nbsp;duration of acute diarrhea in children

Deceptive Content Analysis using Deep Learning

Fake news is the deliberate spread of false or misleading information through traditional and social media for political or financial gain. The impact of fake news can be significant, causing harm to individuals and organizations and undermining trust in legitimate news sources. Detecting fake news is crucial to promote a well-informed society and protect against the harmful effects. Tools such as machine learning and natural language processing are being developed to help identify fake news automatically. Necessity of fake news detection is very important to maintain a trustworthy and responsible media environment. We have used Word2Vec model for word vectorization and represents words in a multi- dimensional space based on their semantic and syntactic relationships. The use of the LSTM with 256 units allows our model to capture the sequential nature of the data and make predictions based on past information. The proposed model uses Word2Vec and LSTM models to provide a powerful approach to fake news detection, combining the ability to capture the complexity of language and the sequential nature of the data. The model has the potential to accurately detect fake news and promote a well-informed society. The accuracy achieved by building the model was 97%