Miracle-dz/UniFAR-FTUs huggingface dataset:
ROUTING ATTACKS IN MOBILE AD HOC NETWORKS

— Mobile ad hoc networks (MANETs) are a set of mobile nodes which are self-configuring and connected by wireless links automatically as per the defined routing protocol. These nodes communicate with each other by exchange of packets, which for those nodes not in wireless range goes hop by hop. Unique characteristics, such as dynamic network topology, limited bandwidth, and limited power, nodes running on battery routing in a MANET is a particularly challenging task compared to a conventional network. MANET research has focused on developing an efficient hybrid routing mechanism in such a highly dynamic mobile nodes in network. At present, several efficient routing protocols have been proposed for MANET. However, in the presence of malicious nodes, the networks are vulnerable is possible to various kinds of attacks. In particular, we examine routing attacks, such as link spoofing and colluding miss relay attacks, as well as countermeasures against such attacks in existing MANET protocols.
An Investigation on Conventional Preoperative Planning in TKA

Introduction: Alignment view before the total knee arthroplasty is a conventional study aiming to regulate tibia and femur vertical cuts perpendicular to the tibia and femur mechanical axes. This research examines how successful this method is in fulfilling this objective. Materials and methods: This is a prospective descriptive research study carried out over 50 samples. The difference of the mechanical angles of tibia and femur was measured before and after the operation. The difference of mechanical and anatomical axes of femur was also measured and recorded before the operation. The difference of the posterior condylarline and trans-epicondyle was recorded during the operation. Findings: The difference of the mechanical angles of tibia and femur before and after the operation was (Mean ± SD) 12 ± 7.1 and (Mean ± SD) – 0.08 ± 2.8 respectively. The difference of the mechanical and anatomical axes of femur before the operation was recorded at (Mean ± SD) 5.8 ± 2.1. The difference of the posterior condylar (line) and transepicondyle (axis) in distal femur was (Mean ± SD) 3.7 ± 1.7. Conclusion: About 82% percent of studied knees were in the standard area after the operation. The difference of mechanical and anatomical axes of femur in each knee was different. The same is true about the posterior condylar line and trans-epicondyle in each knee.
Characteristic Analysis of Transverse Flux Type Linear Oscillating Actuator According to Slot Number

393 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: H10730688S219/19©BEIESP  A transverse flux linear oscillatory actuator (TFLOA) for a compressor is proposed, which features a unique configuration consist of dual stator cores, single-phase winding coil and permanent magnet (PM) mover. It not only offers low manufacturing cost and good magnetic force density, but also provides high efficiency for its transverse flux flow. This paper deals with the design and electro-magnetic analysis of TFLOA according to a number of slots. For the same comparison, the use amount of the moving magnet, the outer diameter of the stator core, the moving stroke, and the frequency are selected equally. Finally, we compared the electro-magnetic characteristics such as no-load analysis, electromagnetic loss analysis, electromagnetic output and efficiency of each TFLOA and proposed the optimal TFLOA.
MEMORY ENHANCING ACTIVITY OF POLYHERBAL FORMULATION MEDHYA RASAYANA IN SCOPOLAMINE INDUCED AMNESIA IN RAT

Objective: Memory enhancing activity of polyherbal formulation Medhya Rasayana in scopolamine induced amnesia in rats using morris water maze and pole climbing models. Material and Methods: About 48 male rats (200-300gm) were randomly divided into six groups and each group contains four rats. the six groups are: Control group, Diseased group (scopolamine 0.7 mg/kg i.p. treated group), Positive group (piracetam 200mg/kg p.o. treated group), Scopolamine plus piracetam group (Scopolamine 0.7 mg/kg i.p. plus piracetam 200 mg/kg p.o. group), Test dose 1 group (scopolamine plus Medhya Rasayana 100 mg/kg treated group), Test dose 2 group (scopolamine plus piracetam 200 mg/kg treated group 200 mg/kg). Results: The result of the effect of administration of Medhya Rasayana on scopolamine induced memory deficits in the morris water maze and pole climbing model were evaluated in rats. The effect of control treated, scopolamine treated (0.7 mg/kg i.p.), piracetam (200 mg/kg p.o.) and Medhya Rasayana at doses 100 mg/kg p.o. and 200 mg/kg p.o. body weight, were evaluated at regular 5 days of treatment. The transfer latency on the acquisition as well as on the retention days was found increased at 123±2.8099 sec on 5th day. The latency time of control group is 39.25±2.8099 sec on 1st day and 32±2.1602 sec on 5th day. The effect of Medhya Rasayana after continuously 5 days administration was observed and compared with scopolamine treated group and piracetam treated groups. The decrease in latency period in compared to scopolamine treated group was observed at dose of 200 mg/kg as compare to 100 mg/kg dose of Medhya Rasayana. Conclusion: Our data suggested that Medhya Rasayana of 200 mg/kg shows better result in comparison to Medhya Rasayana 100 mg/kg.
Evaluation of drought tolerance in mutant Kenyan bread wheat (Triticum aestivum L.) using in vitro techniques

Wheat (Triticum aestivum L) is widely cultivated as a small-grain cereal. In Kenya, it is ranked second after maize in its contribution towards food security. Biotic stress conditions such as drought cause extensive losses to agricultural production worldwide. In Kenya, arid and semiarid lands represent 83% of total land area, which experience frequent crop failure due to drought stress. Developing drought-tolerant wheat genotypes has been the focus of many wheat improvement programs. Few drought tolerant varieties are available for commercial production in Kenya. Hence, there is need to develop more drought tolerant wheat varieties. The objective of this study was to screen for drought resistance in two mutant wheat lines in vitro using Polyethylene Glycol (PEG). Four wheat germplasm were tested for drought tolerance using -3.0, -9.0 and -15.0 PEG-6000 concentrations and the data was recorded on various seedling parameters including root length, shoot length and root length /shoot length ratio. The experiment was carried out in three replicates using completely randomized design. Data was subjected to analysis of variance (ANOVA) using GENSTAT 12th edition. Correlation was done by Pearson Correlation Coefficients to determine significant associations among the different variables. Results indicated that there was a significant difference (p=0.05) between Mutant 1 and Mutant 2 having longer roots, shoots and a root to shoot ratio compared to Chozi and Duma in the different PEG concentrations used. Hence, the two mutant lines are possible candidates for varieties that can be grown in ASALs regions in Kenya. *Corresponding Author: Gerald Gikonyo Githinji  gggikonyo@gmail.com International Journal of Agronomy and Agricultural Research (IJAAR) ISSN: 2223-7054 (Print) 2225-3610 (Online) http://www.innspub. net Vol. 9, No. 2, p. 180-189, 2016 International Journal of Agronomy and Agricultural Research (IJAAR) ISSN: 2223-7054 (Print) 2225-3610 (Online) http://www.innspub. net Vol. 5, No. 1, p. 14-22, 2014
Critical Review of Extended Agile Process Model

—Concrete efforts to integrate Software Engineering and Human Computer Interaction exist in the form of models by many researchers. Many efforts in this regard lead to various proposals of smooth integration of SE (software engineering) processes with HCI (human computer integration) for product development. One such effort is extended Agile process model. This paper presents a critical review of extended Agile Process model. It also suggests means to bring nearer the two diverse communities of SE and HCI. The objective of closing the gap between SE and HCI is to increase product usability.
APPLICATION OF NEURAL NETWORK IN DROUGHT FORECASTING; AN INTENSE LITERATURE REVIEW

India is the agrarian country. The overall economy of our country is based on agriculture. Although the methods of cultivation are traditional and not hi-tech thus more over 75% of our farmers are dependent on monsoon. Prediction of actual monsoon is a challenge for meteorological scientists. Since the climatic data time series shows highly non-linear and chaotic behavior thus its forecast is still an enigma. Thus, forecasting of climate phenomenon is a challenging issue for the researchers round the globe. However, it is a prime necessity to forecast climatic changes such as Rainfall (daily rainfall, monthly rainfall, heavy rainfall etc.), Flood, Drought, minimum and maximum Temperature, River flow etc. To recognize applications of Artificial Neural Network (ANNs) in weather forecasting, especially in drought forecasting a comprehensive literature review from 2000 to 2017 is done and presented in this paper. In the study, more over 90 contributions have been surveyed and it has been observed that the architecture of ANN such as BPN, RBFN, MLP, ANFIS, ARIMA etc. are found best to forecast chaotic behavior and have efficient enough to forecast drought as well as other weather phenomenon over broader or smaller homogeneous region.
Cyclic HMM-based Method for Pathological Gait Recognition from Side View Gait Video

— The analysis of human gait has become a popular and dynamic research on computer vision. An important application of gait analysis is to early detect the gait abnormalities, which may be caused by some diseases. In this paper, we present a video-based method to recognize common pathological gaits of the elderly such as ataxic, hemiplegic, limping, neuropathic, and Parkinson. There are three main processing steps in the system. In the first step, from each frame of the side view gait video sequence, we first separate the walking person from the background using adaptive background Gaussian Mixture Model. In the second step, we convert the extracted object into a seven-dimensional feature vector based on Hu’s moments. In the final step, we analyze those extracted features to recognize different abnormal gaits using Cyclic Hidden Markov Model (CHMM). The Cyclic HMMs are trained with different values of parameters in order to achieve the best reliable recognition model for each disease. Experimental results on simulated abnormal gait database indicate the good performance of the proposed method in terms of low complexity and high recognition rate (about 83% for recognition and 93% for detection task).
Diagnosis of Liver Images Lesions in Mr Images Using Improved Segmentation and Classification Task

---Detection of liver abnormalities is a deep study that reflects the condition of liver during the time of liver disease. The diagnosis reveals the condition of liver in human body, however earlier detection of images during medical diagnosis is still a tough task with texture analysis and classification process. Hence, in this study we reveal the condition of liver as normal or abnormal based on the analysis done using the proposed method. The proposed system identifies the condition of liver through two stages: structural and statistical analysis and classification process. The former one is carried out with Gabor Gray Level - Local Binary Patterns (GGL-LBP) that provides the structural texture representation of a liver image. The latter one uses various machine learning classifiers to test the proposed method that includes Artificial Neural Network Fuzzy Inference System (ANFIS) is used for classification process. The set of images are used for training and testing the classifier using the structural features. The proposed classification method is evaluated using 225 test records and it is tested against conventional methods in terms of accuracy, sensitivity and specificity. The experimental validation shows that proposed method with ANFIS classifier acquires improved accuracy than other classifiers, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT) and Linear Discriminant Analysis (LDA).
Purification and Characterization of Nattokinase from Bacillus Subtilis from Coconut Field Soils

Nattokinase is an enzyme that finds a wide range of applications in pharmaceutical industry, health care and medicine etc. The main aim of research work is to screen and identification of Bacillus subtilis from coconut field soil source and purification of nattokinase. Biochemical test like Methyl red (MR) and the voges-proskauer (V-P) broth opposite result were obtained, in urease test phenol red changes from yellow color to a red or deep pink (cerise) color and catalase test trypticase soy agar slants presence of gas bubbles. Using different nitrogen sources like cow fibrin, sheep fibrin, peptone and tryptone enzyme activity was found to be 6, 26, 28.3 and 38 U/ml respectively. The caseinolytic activity of Nattokinase was found to be 636.73 U/ml. The enzymes were purified to homogeneity by ammonium sulfate precipitation, dialysis and ion exchange chromatography. Characterization studies showed an optimum temperature 40C, 38 U/ml and 1.0 U/ml of substrate and zinc chloride activator were found to be more when compared to the
Identification of reduced features for Intrusion Detection System using Naïve Bayes Classifier

— In the era of new technologies there is a threat for security. Now-a-days there are various attacks or events occurring in the computer system. So, in order to detect this events occurring in the computer system we use Intrusion Detection. Intrusion detection mainly focuses on feature selection and the feature reduction. For identification of reduced features the system uses two techniques viz; Feature Selection Techniques and the Feature Vitality Based Reduction Method. This system uses Naive bayes Classifier on reduced datasets for identification of intrusions.
Steganographic Data Embedding with Revocable Texture Synthesis

— we propose a unique approach for steganography using a reversible texture synthesis. A texture synthesis method re-samples a smaller texture image that synthesizes a brand new texture image with the same native look and impulsive size. We weave the feel synthesis method into steganography to conceal secret messages. In distinction to exploitation associate existing cowl image to cover messages, our rule conceals the supply texture image and embeds secret messages through the method of texture synthesis. This permits us to extract secret messages and also the supply texture from a stego artificial texture. Our approach offers 3 distinct benefits. First, our theme offers the embedding capacity that's proportional to the scale of the stego texture image. Second, a steganalytic rule isn't doubtless to defeat our steganographic approach. Third, the reversible capability inherited from our theme provides practicality that permits recovery of the supply texture. Experimental results have verified that our planned rule will give varied numbers of embedding capacities, manufacture a visually plausible texture pictures, and recover the supply texture.
The Cloud Computing and Internet of Things (IoT)

The cloud is a huge, interconnected network of powerful servers that performs services for businesses and people. The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. IoT has evolved with the greater generation of data. Internet of Things Cloud Service creates excessive communication between inexpensive sensors in the IoT which means even greater connectivity. Billions of connected devices and machines will soon join human-users. IoT generates lots of data while on the other hand, cloud computing paves way for this data to travel. In this paper we try to focus on cloud providers who take advantage of this to provide a pay-as-you-use model where customers pay for the specific resources used. Also, cloud hosting as a service adds value to IoT startups by providing economies of scale to reduce their overall cost structure.
Human Action Recognition through the First-Person Point of view, Case Study Two Basic Task

In this study, a human motion dataset is built and developed based on indoors and outdoors actions through a bounded-on-head camera and Xsens for tracking the motions. The key point here to structuring the dataset is utilized to set the sequence of a Deep Neural Network and order an arrangement of frames in the performed task (washing, eating, etc.). As a final point, a 3D modeling of the person suggested at every frame centered with the comparable structure of the first network. More than 120,000 frames constructed the dataset, taken from 7 different people, each one acting out different tasks in diverse indoor and outdoor scenarios. The sequences of every video frame were 3D synchronized and segmented 23 parts.
Optimization of Deep Learning using various Optimizers, Loss functions and Dropout

: Deep Learning is gaining lot of prominence due to its break through results in various fields like Computer Vision, Natural Language Processing, Time Series Analysis, Health Care etc. Earlier, the Deep Learning was implemented using the batch and stochastic gradient descent algorithms and some optimizers which lead to very less performance of the models. But today, lot of work is going on for the enhancement of the performance of Deep Learning using various optimization techniques. So, in this context, It is proposed to build a Deep Learning model using various Optimizers (Adagrad, RmsProp, Adam), Loss functions (mean squared error, binary cross entropy) and Dropout concept for the Convolutional neural networks and Recurrent neural networks and verify the performance such as Accuracy and Loss of the model. The proposed model has achieved maximum Accuracy when Adam optimizer and mean squared error loss function are applied on convolutional neural networks and the model is run with minimum Loss when the same Adam optimizer and mean squared error loss function are applied on Recurrent neural networks. While performing the Regularization of the model, the maximum Accuracy is achieved when the Dropout with a minimum fraction ‘p’ of nodes is applied on convolutional neural networks and the model has run with minimum Loss when the same dropout value is applied on Recurrent neural networks.
ES-MAC: A Sink-aware Beacon Scheduling Transmission for Receiver-Initiated MAC Protocol for Wireless Sensor Network

: In Wireless Sensor Network energy is a vital parameter as the sensor nodes are low power devices. Many energy efficient protocols have been proposed to efficiently utilize the battery-life of the sensor nodes. Hence it is vital to forward the data packet from the sensor nodes to the sink node without any delay. In contention-based Receiver-Initiated MAC protocols this delay is caused by the idle listening which makes the data packet to stay in the nodes for a longer duration. Also due to collision and retransmission it further introduces delay in the network. As it is not aware of the sink node the data packet may end up in looping. In this paper an Enhanced Scheduling-MAC (ES-MAC) protocol is proposed which a sink-aware beacon is scheduling algorithm applied over receiver-initiated MAC protocol, guaranteeing a delay-efficient communication enhances network lifetime. The ns-2 performance evaluation shows the proposed ES-MAC has high throughput and less network latency. Moreover the comparative simulation result indicates that the proposed protocol outperforms the contention-based RI-MAC protocols.
AUTOMATIC SPEECH RECOGNITION SYSTEM CONCERNING THE MOROCCAN DIALECTE (Darija and Tamazight)

In this work we present an automatic speech recognition system for Moroccan dialect mainly: Darija (Arab dialect) and Tamazight. Many approaches have been used to model the Arabic and Tamazight phonetic units. In this paper, we propose to use the hidden Markov model (HMM) for modeling these phonetic units. Experimental results show that the proposed approach further improves the recognition.
Efficient Clustering Algorithm to Discover User Pattern Applying on Weblog Data

: WWW has becomes today not only an accessible and searchable information source but also one of the most important communication channels. One of the key steps in Knowledge Discovery in Databases is to create a suitable target data set for the data mining. Clustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data.K-means clustering algorithm suffers from two major shortcomings, right value of clusters ( k ) are initially unknown and effective selections of initial seed are also difficult. In this dissertation, the efficient algorithm is proposed which overcomes initial seed problem and also the validation of cluster problem. The comparison is performed between proposed fuzzy clustering algorithm, fuzzy C-means and K-mean clustering algorithm on web log dataset to test its accuracy and efficiency.
INHIBITION OF α-SMA, Bax AND INCREASE OF Bcl-2 EXPRESSION IN MYOCARDIOCYTES AS RESPONSE TO CHITOSAN ADMINISTRATION TO HYPERCHOLESTEROLEMIC RATS

Hypercholesterolemia (HPC) cause biochemical alteration in lipid profile, disturbances in cardiac tissue, immunohistochemical expression of alpha-smooth muscle actin (α-SMA) and Bax were induced; while Bcl-2 expression was decreased. The present investigation aimed to evaluate the protective effect of chitosan (COS) in HPC rats by feeding on high fat diet for one month. Biochemical studies of total cholesterol, triglycerides, LDL and VLDL perceived high values and diminution in HDL levels in serum. Induced histopathological lesions in the cardiomyocytes was represented by detached muscle fibres, deposition of fats and presence of fibrotic areas; occupied by lymphocytes, macrophage and increase in the collagen fibres distribution throughout the cardiac tissue. COS supplementation to HPC rats, produced a counteractive effect on HPC rats, represented in the compensation of biochemical alteration and amended histological picture, and decrease in α-SMA and Bax expression and increase in Bcl-2 expression in the cardiac tissue. These findings were attributed to the significant inhibitory effect of COS remedy adjoining deleterious effects of HPC.
EIGHTEEN LEVEL ASYMMETRICAL SINGLE PHASE MULTI LEVEL INVERTER TOPOLOGY WITH LOW SWITCHING FREQUENCY AND LESS DEVICE COUNTS

An eighteen level asymmetrical single phase multi-level inverter topology with low switching frequency and less device counts have been proposed in this paper. In this new topology the desired output voltage of the inverter has been obtained by simple combination of addition and subtraction of all DC sources through different switches. The desired output voltage obtained by this method shows that the less number of sources, switch counts, and considerable reduction in losses, cost and size of the inverter. While comparing the proposed topology with the existing one the results shows that the proposed topology needs very less device count, switch counts and components. Matlab based simulation software have been used to analysis the performance of the proposed topology along with mathematic modelling the output of proposed topology simulation shows that the proposed topology need very less count of switch and also the performance of the circuit is fare better than the other conventional topology available in the market.