Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Fusion by Meta Heuristic Genetic Algorithm Approach

:- With the ready availability of multiple sensors, the area of information fusion has been receiving increasing attention. For multi-sensor image data, algorithms such as simple average method, Principal Component Analysis (PCA) method, Gradient Pyramid (GP) method, Laplacian Pyramid (LP) , Ratio Pyramid (RP) method and Discrete Wavelet Transform (DWT) methods have been successfully applied for image fusion. Another important issue that arises in image fusion: the performance of image fusion is that the performance of the associated algorithms is difficult to evaluate, especially when a clearly defined ground-truth image is not available. Some common measures to assess the performance for image fusion are Mutual information (MI), Tsallis and Renyi divergence based information. However they are difficult to estimate precisely. In this paper, a new approach is proposed for evaluating the performance of image fusion algorithms based on copula functions. To achieve this, copulas are proposed for the estimation of the MI, Tsallis and Renyi divergence based information and these are used to evaluate the quality of image fusion.
Effect of Viscous Dissipation on MHD Williamson Nanofluid Flow in a Porous Medium

The main objective of this paper is to focus on a numerical study of viscous dissipation effect on the steady state flow of MHD Williamson nanofluid. A mathematical modeled which resembles the physical flow problem has been developed. By using an appropriate transformation, we converted the system of dimensional PDEs (nonlinear) into coupled dimensionless ODEs. The numerical solution of these modeled ordinary differential equations (ODEs) is achieved by utilizing shooting technique together with Adams-Bashforth Moulton method of order four. Finally, the results of discussed for different parameters through graphs and tables.
CAPACITY CRUNCH: CAN MoSoNets OFFLOAD DATA SOLVE THIS

: Due to the increasing popularity of various applications for smart phones, 3G cellular networks are currently overloaded by mobile data traffic. Offloading mobile cellular network traffic through opportunistic communications is a promising solution to partially solve mobile data traffic, because there is no monetary cost for it. To solve this problem we propose target-set selection problem for information delivery in the emerging Mobile Social Networks (MoSoNets). We propose to exploit opportunistic communications to facilitate the information dissemination and thus reduce the amount of cellular traffic. Here, with only k mobile users we study how to select the target set, such that we can minimize the cellular mobile data traffic. In this scenario, initially the content service providers deliver information over cellular networks to only users in the target set. Then with the help of opportunistic communications, target-users will further disseminate the information among all the registered users. At last, service providers will send the information to users who fail to receive it before the delivery deadline (i.e., delay-tolerance threshold). Here we intend three algorithms, called Greedy, Heuristic, and Random, to solve data traffic over cellular mobile data traffic and then evaluate their performance through an extensive trace-driven simulation study. The simulation results check the efficiency of these three algorithms for both synthetic and real-world mobility traces. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we execute a proof-of-concept prototype, called Opp-Off, which utilizes their Bluetooth interface for device/service discovery and content transfer. Through this work we can improve performance, security, and cost of existing data traffic over network.
SENTIMENT ANALYSIS OF TWITTER DATA USING INTELLIGENT RELATED WORD IDENTIFICATION

Sentiment Analyzer is used to understand and analyze the human thoughts about particular object. With the help of social networking sites, this paper examines the sentiment analyzer on twitter data by analyzing the tweets as Tweet Illustration, Segmentation, Normalization and Classification of twitter data. This paper introduces a new approach, “Intelligent Related Word Identification (IRWI)” which helps us to reduce the neutral words. It has been validated using “Bayesian Classification”, is based on bayes theorem with independence conventions between predictors. The experimental validation was done using python 2.7, Pyenchant and tweets collected using twitter API. The performance of the proposed approach was found to be better than the traditional approach by using Intelligent Related Word Identification (IRWI)
Effect of Multicollinearity in Predicting Diabetes Mellitus Using Statistical Neural Network

Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. Multicollinearity is one of the problems usually encountered by Economists and Statisticians when predicting a dependent variable from the set of independent variables that are significantly or highly correlated especially when the traditional method of regression analysis is used. This research is aimed to determine the effect of multicollinearity in predicting diabetes mellitus using statistical neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Variance inflation factor (VIF) detected multicollinearity among some risk factors and Principal component technique (PCA) was employed to remove it. Levenberg-Marquardt (LM) algorithm was used to train the statistical neural network for the original and principal components data. The results show that when five (5) hidden neurons architecture is used, the model achieved 99.0% and 93.9% accuracy for training the original and reduced data respectively. Similarly, when the number of hidden neurons is increased to ten (10), the model achieved 98.7% and 94.4% accuracy for training the original and reduced data respectively. The research therefore concludes that unlike traditional econometrics and statistical models, statistical neural networks estimation process is not negatively affected by the presence of multicollinearity in the data but get better when more information are utilised because it gives better estimates when the whole data is considered as inputs than when the reduced data is used.
To Avoid Unwanted Messages from User Wall: Content Based Filtering Approach

— This paper proposes a system that implements a content-based message filtering service for Online Social Networks (OSNs). Our system allows OSN users to have a direct control on the messages that are posted on their walls. This is done through a rule-based system, that allows a user to customize the filtering criteria, which is to be applied to their walls
An Efficient Algorithm for Reversible Data Hiding in Encrypted Images by RRBE

: Recently reversible data hiding in encrypted images is gaining importance as this technique of watermarking can reconstruct the original image after extracting the desired data hidden in the image. In all the previous works, room was reserved for data in the image and the image would be encrypted using a standard stream cipher. This work proposes a technique for reversible data hiding in encrypted images where the data to be hidden is encrypted using Advanced Encryption Standard (AES) that can improve the PSNR. Also the encrypted image holding data would be permuted and transmitted, that can increase the level of security. Experimental results show that this method can achieve a PSNR of more than 60db thereby increasing the embedding rate.
SPECTROPHOTOMETRIC ANALYSIS OF FRUSEMIDE IN DIFFERENT FORMULATIONS

The present study describes the use of an aqueous solution containing a blend of hydrotropic solubilizing agents (mixed hydrotropic substance’s solution) as a successful solvent system utilizing the concept of mixed hydrotropy for spectrophotometric analytical estimation of various conventional formulations as well as novel drug delivery systems. Frusemide, a poorly water-soluble drug, was estimated by application of mixed hydrotropic solubilization method. There was more than 15-fold enhancement in aqueous solubility of frusemide in a solution of blend of hydrotropic agents which consisted of 30% urea, 13.6% sodium acetate and 11.8% sodium citrate. This solvent mixture was employed to solubilize the drug from the fine powder of tablet formulations as well as the niosomes of frusemide. The selected λmax for spectrophotometric estimation was 333 nm. The hydrotropic agents used in the analysis and additives used in the manufacture of tablets and preparation of niosomes did not interfere in the analysis. Statistical data proved the accuracy, reproducibility and precision of the proposed method. The results suggested that proposed method is new, rapid, simple, accurate, and reproducible as well as employed aqueous solvent instead of organic solvents in estimation of drug from the dosage forms.
Subcarrier and Power Allocation of Cognitive Network by Genetic TLBO Algorithm

— Cognitive radio (CR) systems allow opportunistic, secondary users (SUs) to get to segments of the range that are unused by the system's authorized primary clients (PUs), gave that the incited obstruction does not trade off the PU' execution ensures. To represent impedance requirements of this sort, work consider an adaptable channel sharing plan that matched SUs in light of the obstruction that they cause to the framework's Pus. Here for this matching hereditary calculation was utilized which is named as educator inclining based advancement where double learning influence viable blending for expanding channel to pick up and control appropriation. It was demonstrated that the subsequent work has increment different assessment parameters when contrasted with past existing techniques.
The Role of Cognitive predictors in academic achievement of high school students in Salmas

The study aimed to investigate the role of Cognitive predictors in academic achievement of high school students in Salmas. The multi-stage cluster sampling method is used for the study, 285 (male and female) participants were selected as sample. They responded to LaGuardia, Ryan, Couchman, and Deci's basic psychological needs Inventory (2000), Ruchi, Valerand, Deci, and Ryan's academic motivation scale (AMS) (2013), and Peltier, Pintriech, and colleagues' learning strategies questionnaire (1991). The data were analyzed using SPSS version 18 software. The structural equation modeling was used to study the relationship between variables. The results showed that there is no relationship between cognitive strategies and academic achievement and result showed it was confirmed meta-cognitive (planning, monitoring, regulation) strategies and academic achievement.
Estimation of Pathloss in Femtocells for Indoor Environments

Femtocells Access Points (FAPs) are low power base stations which improve the cellular service within indoor environments. RF propagation modeling is required to analyze the femtocell performance, power issue and the interference impact on the macro network. An indoor propagation channel is considerably more antagonistic than an outdoor channel. This paper deals with the path loss and coverage of the femtocells in indoor environments. The ability to accurately predict radio-propagation behavior for wireless systems is becoming crucial to system design. To reduce the cost of on-site measurements, propagation models have been developed. These models provide a suitable, low-cost and convenient alternative to costly on-site measurements. Lack of a LOS, heavy attenuation, diffraction by objects in the propagation path, and multipath all contribute to losses in an RF channel. RF modeling is required to predict this path loss. The path loss is associated with the design of base stations, as this tells us how much power a transmitter needs to radiate to provide coverage in the intended region. A femtocell increases overall system capacity by reducing the coverage holes and by capacity gain from higher SINR to indoor subscribers. Finally, RF modeling is carried out for indoor environments experienced in a typical femtocell.
Study the Voltammetric Behavior of Symmetrical Tetradentate Schiff Bases

Accepted on 25 th August 2014 _____________________________________________________________________________ ABSTRACT Synthesis of symmetrical Schiff bases (1-4) employed by using condensation methods. Purity was monitored by TLC and column chromatographic techniques. In this present study cyclic voltammetric behavior of symmetrical Schiff bases (1-4) were studied. The cyclic voltammetric studies reveals that the – CH=Ngroup in Schiff bases is reduced and the process is quasireversible with kinetically controlled behavior. Based on the cyclic voltammetric data propose the possible mechanism for this redox behavior of schiff bases.
Management of spontaneous large intra cerebral hematoma with minimal invasive procedure (twist drill burr hole) with fibrinolytic therapy

Background: Spontaneous intracerebral haemorrhage (ICH) is associated with the high mortality among cerebrovascular events, and most of the survivors end with significant morbidity. Spontaneous intracerebral haemorrhage (ICH) is the second most common cause of stroke comprising 7.5-30% of all strokes. Surgery mainly helps in decrease in secondary injury and early rehabilitation. Aim: To analyse the outcome and advantages of minimal invasive surgery i.e., burr hole or twist drill craniostomy with intraclot streptokinase injection to dissolve clot and aspiration to reduce mass effect in primary intracerebral hematoma, this help to prevent secondary injury and recovery of salvageable brain. Materials and methods: Prospective study was done over from November 2014 to January 2017 in our department for the patients of spontaneous large intra cerebral hematoma > 80ml who underwent clot evacuation with fibrinolytic therapy with minimally invasive procedure with twist drill burr hole. Total 62 patients were included in the study. All the patients were followed with CT sequential scans to see for the clot size and followed with clinical status. Karla Ravi, Nandigama Pratap Kumar, Ginjupally Dhanunjaya Rao, Savarapu Sai Kalyan, Gollapudi Prakash Rao. Management of spontaneous large intra cerebral hematoma with minimal invasive procedure (twist drill burr hole) with fibrinolytic therapy. IAIM, 2017; 4(11): 229-240. Page 230 Results: Our analysis of 62 patients was consistent with the hypothesis that hematoma evacuation leads to improved neurological outcome, the outcome has been correlated with the rate of clot reduction. Conclusion: Minimally invasive surgery is a safe and effective option in the management of spontaneous ICH especially in the patients whom major surgical procedures pose a significant risk. MIS is associated with lower mortality and better outcomes compared to surgical evacuation or conservative management. Our study have clearly shown an improved outcome after minimally invasive surgery and still there is some emptiness to determine the exact protocol to insist these type of studies to deal with bed occupancy and a burden to the society.
Short term load forecasting using fuzzy logic

– Load forecasting is essential for planning and operation in energy management. It enhances the Energy efficient and reliable operation of a power system. The energy supplied by utilities meets the load plus the energy lost in the system is ensured by this tool. Since in power system the next day’s power generation must be scheduled every day. The day-ahead short term load forecasting (STLF) is a necessary daily task for power dispatch. Short term load forecasting is essential for unit commitment, economic allocation of generation, maintenance schedules. This paper presents a solution methodology using fuzzy logic for short term load forecasting. Fuzzy logic approach is implemented on weather sensitive data and historical load data for forecasting the load. The proposed methodology uses fuzzy reasoning decision rules that capture the nonlinear relationships between inputs and outputs. The input data include historical load and hourly data like temperature, humidity and windspeed. Jaipur Vidyut Nigam hourly load data is used for training and testing which is collected from State Load Dispatch and Communication Centre, Rajasthan Vidyut Parasaran Nigam. The forecasted load results are obtained from fuzzy logic model using triangular membership function.
Classification of Prostate Cancer using Wavelet Neural Network

Prostate cancer is the century disease that endanger the life of men. The earlier to diagnose the disease, the probability of curing this disease is higher. Therefore, new approaches of diagnosis is required to effectively detect the prostate cancer in early stage compared to the traditional methods. Therefore, WNN is a new adopted approach in prostate cancer diagnosis. Morlet function is used as an activation function of wavelet neural network (WNN) and back propagation (BP) is applied to train the Wavelet network. WNN classifies prostate cancer according to three factors: patient age, PSA level, and prostate volume. WNN performance is evaluated based on the percentage of classification and the computational complexity of several cases. The results of the simulation show that WNN has lower mean squared error (MSE) than the Neural Network (NN).
Nutritional Status And Socio-Economic Conditions Influencing Prevalence Of Anaemia In Pregnant Women

Globally, about two hundred million people suffer from anaemia per annum with the highest prevalence rates found among women of reproductive age, infants and young children, and especially those of poor socioeconomic status. This study is a survey-based descriptive study conducted among 400 pregnant women to determine the prevalence of anaemia and the influence of some factors associated with anaemia during pregnancy such as age, socio-economic status, nutrition, awareness, child spacing and spouse’s level of education. The concentration of haemoglobin is collected from analytical data and the personal, nutrition and socio-economical information was recorded using questionnaire interview. The investigation indicated that the prevalence of anaemia was 91.25% among the interviewed women with 27.5% of mildly anaemic (11.0-9.0g/dl), 28.5% moderately anaemic (9.0-7.0g/dl), 32% severely anaemic (7.0-4.0g/dl) and 3.25% very severely anaemic (<4.0g/dl). More studies are needed to explore the cause of the failure to prevent anaemia among pregnant women. The main recommendations of the study included implementing more efforts to improve the quality of health services, improve the awareness about anaemia, improve family planning and birth spacing program to diminish maternal iron losses, intensify measures to improve public awareness and implement an advocacy program to increase utilization of health services.
Review on Facial Micro - Expression Detection

 Abstract: An important issue of the emotional recognition, the Micro-expression recognition has received very long attention and fast development in past one decade. In the paper, our own selves give a view of the growth of the in recognition of Micro-expression. This elaborate various feature extraction methods, various classification techniques and various databases were discussed which are used to estimate the Micro-expression. In the conclusion, we discuss existing research challenges in the Micro-expression to enhance the recognition rate.
Cancer Metabolic and Immune Reprogramming: The Intimate Interaction Between Cancer Cells and Microenvironment

ECM: Extracellular Matrix; CAFs: Cancer Associated Fibroblasts; TLRs: Toll-like Receptors; Ig: Immunoglobulin; RAG: Recombination Activating Genes; TCA: Tri Carboxylic Acid; MCT: Monocarboxylate Transporter; ROS: Reactive Oxygen Species; HK2: Hexokinase 2; PET: Positron Emission Tomography; PFK-1: 6-Phosphofructo-1-Kinase; PK: Pyruvate Kinase; SPs: Specificity Proteins; GFs: Growth Factors; PDGF: Platelet-Derived Growth Factor; TGF-β: Transforming Growth Factor-β; bFGF: basic Fibroblast Growth Factor; EMT: Epithelial-to-Mesenchymal Transition; TGFβ1: Transforming Growth Factor-β1; MDSCs: Myeloid-Derived Suppressor Cells
Antioxidant activity and phytochemical screening of five species of capsicum fruits

Antioxidants are compounds that help to inhibit many oxidation reactions caused by free radicals. The oxidants activity of five extracts from five varieties of pepper fruits (Solonaceae) were evaluated using the ferric thiocyanates method. The total phenolic content of the extracts was determined spectrophotometrically as tannic acid equivalent. The reducing power was also investigated. The antioxidant activity (% oxidation inhibition) ranged from as high as 78.46% in red bird pepper (Capsicum frutescens) extracts to as low as 10.11% in green pepper fruits (Capsicum annum) extract. Antioxidant activity correlated significantly and positively with total phenolic content (R 2 = 0.78), while there was no correlation between total antioxidant activity and reducing power (R 2 = -0.46), neither between reducing power and total phenolic content (R 2 = -0.56). The phytochemical screening revealed the presence of tannins, flavonoids, saponins, phenols, alkaloids and volatile oils. Pepper fruits studied possess potential health benefits by inhibiting lipid peroxidation hence their use as a value added ingredient. The results indicate that reducing power does not fully characterize the antioxidant activity of the capsicum species studied.
Systemic Lupus Erythematosus

Systemic lupus erythematosus or SLE is a persistent heterogeneous autoimmune disease that affects multisystem of the body. It is distinguished by acute and chronic inflammation of various tissues and even organs of the body principally the skin and joints. Systemic lupus erythematosus is a multisystem disorder and hence, it can affect any tissues, organs and even systems of the body. There are few categories of lupus for instance, lupus dermatitis or cutaneous lupus erythematosus (CLE) that affects the skin and causes malar rash, discoid lupus erythematosus (DLE) as well as systemic lupus erythematosus that causes damage to single or multiple internal organs. The damage is due to the inflammation that is caused by direct antibody reaction to the body tissues as well the deposition of immune complexes. Glucocorticoids, immunosuppressant, and antimalarial are the combination therapy used to treat SLE besides providing counseling and awareness. Lupus erythematosus in any form particularly systemic lupus erythematosus (SLE) are prevalent in women compared to men with ratio of 6:1. It has the tendency to affect all ages but most frequently attacks women of aged 20 to 45 years old compared to men. On the other hand, if lupus erythematosus causes damage to internal organs either single or multiple, it is known as systemic lupus erythematosus. The damage is due to the inflammation that is caused by direct antibody reaction to the body tissues as well the deposition of immune complexes.
SOLAR PV-WIND HYBRID POWER GENERATION SYSTEM

Renewable energy sources i.e.,energy generated from solar, wind, biomass, hydro power, geothermal and ocean resources are considered as a technological option for generating clean energy. But the energy generated from solar and wind is much less than the production by fossil fuels, however, electricity generation by utilizing PV cells and wind turbine increased rapidly in recent years. This paper presents the Solar-Wind hybrid Power system that harnesses the renewable energies in Sun and Wind to generate electricity. System control relies mainly on micro controller. It ensures the optimum utilization of resources and hence improve the efficiency as compared with their individual mode of generation. Also it increases the reliability and reduces the dependence on one single source. This hybrid solar-wind power generating system is suitable for industries and also domestic areas.