Miracle-dz/UniFAR-FTUs huggingface dataset:
A Genetically Optimized Network Intrusion Detection System using K-means Clustering via Principal Component Analysis

— Intrusion detection is to detect attacks against a computer system. It is an important technology in business sector as well as an active area of research. In Information Security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. It plays a very important role in attack detection, security check and network inspect. This paper presents the performance of k-mean algorithm for various values of number of clusters, based on experiments. The optimization of output is done using genetic algorithm by selecting initial through GA. Preliminary experiments with KDD cup’99 Data set show that the k-mean clustering can effectively detect intrusive attacks and achieves a low false positive rate. Here PCA is used to reduce the dimensionality of feature vectors extracted from data for analysis and visualization.
Dynamic Skyline Computation in a Mobile Environment Dynamic Skyline Computation in a Mobile Environment Dynamic Skyline Computation in a Mobile Environment Dynamic Skyline Computation in a Mobile Environment

Skyline computation is an operation that extracts from a multidimensional data set the maximal subset whose elements optimally satisfy the user's requirements. This operation is considered to be useful to a mobile user, if such subset can be promptly computed from the data set that surrounds the user within a certain distance limit at any given time. In this work, we propose an efficient algorithm for continuously providing a mobile user with such skylines of the data sets located near him/her.The algorithm computes the skylines in sequence according to the movements of the mobile user.The i-th skyline in the sequence is computed by updating the ( i-1)-th skyline. The performance of the proposed algorithm is shown by experiments.
Attribute Based Face Classification Using Support Vector Machine

- Verifying human face from captured images of his/her face is challenging task due to various poses and camera focus. Generally, existing approaches to this problem use the features of pose and expression for human face. In this work, different appearance features are taken into concern for face verification. The face verification uses ten different personalities dataset with 61 different attributes of 20 different images for each person using dissimilar poses and expressions. There are two different classification approaches are used for face verification. First approach is attribute based classifiers, it uses binary classifiers trained to identify the presence or absence of describable features of visual appearance like gender, age, skin and, etc. second approach is “face” classifiers, which uses multiclass classifiers trained to verify different persons based on various features. It requires manual labeling because of fewer reasons like similarity of faces, regions of faces to specific reference people. The dataset used in this research work is real-world images of public figures such as celebrities and politicians obtained from the internet. Support Vector Machine (SVM) is used as both binary classification and multiclass classification for attribute classification and face verification respectively. There are eight different evaluation measures are used in face verification system. And the subset evaluation method is used to select attributes from the dataset. Selected attributes are used for classification based on their presence and absence of the visual appearance features. The experiment result shows that proposed work gives 98% of accuracy in face verification and various accuracy results for attribute classification.
Sustainable control of industrial production systems: A hybrid model “lean /project management”

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In a competitive environment, the most successful companies are those that well manage their projects defining the right map to achieve their goals and optimizing the triptych “cost-quality-time limit” without ignoring the actual sustainability requirements. A lot of studies and production system’s experiences are introducing lean production as the direct implementation of lean tools or some of them. However considering a new product manufacturing a new project will summarize many processes and make its management easier especially when the project management becomes lean. In this paper we try to come up with a sustainable hybrid model optimizing and controlling all processes, defining essential values and steps to well manage a manufacturing project generally and draw lean project chart for both conception and production methods to optimize a new product manufacturing based on a case study for steel parts production system . This major project cannot be lean without the integration of lean culture in project management including maintenance projects. Finally a detailed chart of lean maintenance project is proposed which can be generalized for any other industrial production system.
A Static Object Detection in Image Sequences by Self Organizing Background Subtraction

- In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles, the detection of stationary foreground objects is a critical task. In this paper the model based framework is suggested for detecting static objects. Firstly, a background subtraction based method that relies on modeling not only the background, but also the stopped foreground is implemented. Secondly, self-organizing model for image sequences which automatically adapts to scene changes is performed. Finally, we evaluate the proposed algorithm and compare results with the background segmentation algorithm using video surveillance sequences from visor datasets. Experimental results show that the proposed approach has better detection accuracy of stationary foreground regions as compared to the segmentation approach.
Clinical Outcome Study of Transfacet Fusion for Treatment of Herniated Lumbar Discs for Dynamic Stabilization

Objective of the study: The purpose of the present study is to present our experience and clinical results with surgical technique of discectomy and facet joint fusion. Our goal was to achieve pain free and stable back (segmental stability) after lumbar disc surgery and to assess the status of fusion and its relationship with clinical results.Study design :The proposed study is a prospective clinical study of low back pain/ leg pain due to prolapse intervertebral disc treated with discectomy with facet joint fusion (using titanium screws) A case of symptomatic disc prolapse not improving on at least six weeks of conservative management. Clinical and radiological follow up was done up to average of five years.Method: After laminectomy and disc removal. Denude the facet joint cartilage with help of fine curved curette and fine slivers of bone graft taken from spinous processes are impacted into facet joint space after distracting them with help of spreader. Titanium partially threaded cancellous screws with 2.7 mm drill bit without taping.Results Complete fusion across bilateral facets was achieved in 56.2 % levels. Partial fusion was achieved in 34.3% levels. No fusion at all was seen in only 3 levels (9.3%). On Applying chi square test between the 4 groups, chi square value came to be 44.92 with p value of < 0.001. This implied that Post-op Oswestry score & clinical results have statistically significant association with status of fusionConclusion: This study demonstrates that facet screw fixation has multiple advantages. The technique is not only easy to implement by placing a small screw through a facet joint without any excessive retraction of neural structures and any distraction of posterior elements thus preserving the segmental stability. It produces excellent clinical results that are comparable to other exhaustive surgical procedures and more bulky spinal instrumentation systems
Work Motivation Factors of Development Agents and Econometric Model Results: A Case Study in Selected Districts of Ethiopia

The study set out to investigate the work motivation level of Development Agents in Agarfa and Sinana Districts, Bale Zone, Oromia Regional State, Ethiopia and the factors that influence their motivation to work. For this study all the Development Agents of the two Districts with a total size of 135 development agents were included as the sample respondents. The ordinal logistic regression model was employed to determine the most significant variables influencing development agent’s motivation to work. The study found that a large majority of development agents (46.7%) belonged to medium level of work motivation, followed by 28.1% and 25.2% belonging to low and high level of work motivation, respectively. Results of the model showed, of the total eighteen variables considered in the model nine predictor variables were found to be of significance in explaining work motivation level of development agents. Accordingly, demographic factors (sex, marital status), work related environmental factors (promotion avenue), institutional factors (quality of work life, dependable supervisors, work incentive, recognition, policy environment of extension organization) and psychological factors (attitude of development agents towards their job) influenced work motivation at different significant level. It is therefore recommended that concerned agricultural extension managers, process owners, extension officers and supervisors should conduct periodic assessments to determine the level of work motivation of Development Agent’s and have to plan for improvement.
Automatic sensor node failure detection in WSNs for Patient monitoring system

Rapid growth of wireless infrastructure in following years will allow a range of new medical applications that will significantly improve the quality of health care. WSNs playing a vital role in various fields such as surveillance, Home security and safety system. In our paper we are using WSNs in the field of medicine for effective monitoring of patient round the clock. There is a possibility if a node gets failed, which will degrade the lifetime and QOS of the node. In our proposed method we encounter the detection of faulty node by means of comparing RTD with predefined threshold value. In our paper, the detection of faulty node in patient monitoring is done with zigbee technology and PIC microcontroller. For the better scalability, our proposal is stimulated by using NS2 software. The similarity between the hardware and stimulation proves that our proposal is quite applicable for patient monitoring.
EVALUATION OF THE POSSIBLE ANTIOXIDANT EFFECTS OF PEGANUM HARMALA AND GINKGO BILOBA IN AMELIORATING ALZHEIMER’S DISEASE IN RAT MODEL

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder leading to dementia in the elderly inhabitants. Increasing evidence advises that oxidative stress that is normally associated with aging is an obvious and early feature of AD and plays a role in its pathogenesis. The present study was designed to evaluate the possible neuroprotective activity of Ginkgo biloba (Gb) and / or Peganum harmala (Ph) extract against AD in a rat model. AD was induced by chronic administration intraperitoneal injection of scopolamine (0.7 mg kg -1 , i.p.) to rats for a period of 7 days. The Gb (120 mg/kg b.wt.) and Ph (187.5 mg/kg b.wt.) were administrated to AD rat group orally daily for a period of 30 days. The results revealed that the levels of thiobarbituric acid reactive substances and Xanthine oxidase were significantly increased, even though the activities of superoxide dismutase and catalase, as well as the reduced glutathione, were significantly decreased in the brain homogenate of Alzheimer group. Additionally, brain acetylcholinesterase as well as alkaline phosphatase, acid phosphatase, alanine aminotransferase, aspartate aminotransferase and lactate dehydrogenase activities were significantly increased. On the other hand, the administration of Gb, Ph and coadministration of those test herbs acquired potential therapeutic effects on improving the neurodegenerative disorder in rats through suppressing lipid peroxidation, augmenting endogenous antioxidant enzymes, and reducing acetylcholinesterase activity in the brain. It might be concluded that a mixture extract, by its antioxidant constituents, could modulate scopolamine-induced oxidative stress and enzyme activities in the brain. INTRODUCTION: Alzheimer’s disease is a pathological brain disease and the most frequent cause of dementia 1, 2 . Patients with AD suffer a gradual deterioration of memory and other cognitive functions, which eventually causes a complete incapacity and fatal within 3 to 9 years
Fixed Frequency Sliding Mode Controller for Direct Driven Wind Energy Conversion System

This paper presents a simple control strategy for a direct driven PMSG wind energy power system (WECS). The dc link voltage is controlled by the generator side converter by using Fixed frequency sliding mode control (FFSMC). The Grid side converter (GSC) controls the grid active power for maximum power point tracking. The validity of the proposed control scheme has been verified by simulation. The merits of the proposed control scheme are, it does not require the measurement of wind velocity,good dynamic response of DC link voltage, PMSG stator currents are sinusoidal and power injected into the grid at unity power factor. Modeling of Wind turbine, PMSG, controlling of generator side switch mode converter, controlling of GSC and Simulation results are presented. Simulation is done in MATLAB/POWERSYSTEM environment.
Do Motivational Incentives Facilitate Drug Addiction Therapy?

Objectives: Motivation to change behavior is an important therapeutic factor that affects treatment outcomes in patients with substance-use disorders. The motivation level influences treatment engagement, retention, and outcomes. Assessment of motivation is an important aspect of management of these patients. Methods: A total of 26 patients admitted in Kasr Al-Ainy Addiction Unit fulfilling Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria of one or more substance dependence disorder were included. Patients were assessed using the Stage of change readiness and the treatment-eagerness scale (SOCRATES) and Addiction Severity Index before attending 4 motivation-incentives group psychotherapy sessions: 1 session/week. Patients were reassessed using SOCRATES after the sessions. Results: SOCRATES increased 3 scales after the motivation-incentives groups; however, recognition and ambivalence scales showed significant increase (P=0.005 and 0.012, respectively), whereas the increase in the taking steps scale was not statistically significant, with P=0.125. A best-fit model of regression was calculated for factors that may predict SOCRATES before the group. Age of the patient, education years, main substance of abuse (either tramadol or other substances), history of suicide, previous treatment trial, positive or negative medical history, history of alcohol use, history of legal problems, presence of family issues, and psychiatric complications were predictors of the recognition, ambivalence, and taking steps components of motivation. Conclusions: Short-motivation incentives group therapy may improve the readiness to change in patients with polysubstance dependence.
A REVIEW ON PHISHING DETECTION APPROACHES

- Phishing is web-based attack that uses social engineering techniques to exploit internet users and acquire sensitive data. Most phishing attacks work by creating fake version of the real site's web interface to gain user's trust. Despite the fact that these phishing sites look identical or nearly identical to real sites they imitate, user studies have shown that they ignore browser-based indicators and often use the appearance of site to judge the authenticity of sites, just as they use the appearance of physical sites to judge their authenticity. To protect users against phishing, various anti-phishing techniques have been proposed that follows different strategies. We applied different methods for detecting phishing using known as well as new features. In this paper we used the different approaches for detecting phishing websites such as heuristic approach, Blacklist-Whitelist based approach, Fuzzy rule based approaches, Machine learning approaches, CANTINA based approaches, Image based approaches, etc.
Low Level Feature Extraction Techniques in Content Based Image Retrieval: A Review

: A database is a collection of information that is organized for easy storage, retrieval and update. This information can berepresented in many forms like text, image, chart, table, and graph etc. Here we focus on information in the form of images that is stored. Image Retrieval in the current scenario is the basic requirement of today's life. Due to the huge amount of different image types in the database from different sources for image recovery, different processing types are required to extract the relevant features from them. Techniques for Content Based Image Retrieval (CBIR) appeared in the 1990s. To describe image content, it uses low-level features such as color, texture and shape, and breaks through the limitation of traditional text query technique. Content Based Image Retrieval (CBIR) is a meaningful and increasingly popular approach that helps to retrieve image data from a large digital image database because it requires relatively less human interference.Content Based Image Retrieval (CBIR) is now a solution and source of accurate and rapid recovery. CBIR uses visual content to obtain relevant images from large databases in accordance with the interests of the user. The visual content (color, texture, shape, etc.) serves as the image characteristics. Features are ultimate interest measurements analyzed from a picture. It cannot be an amicable solution for accuracy and efficiency to use a single feature extraction for image retrieval. High-dimensional feature reduces the efficiency of the query, low-dimensional feature reduces the accuracy of the query, so that multiple features are better used for image recovery. The most important visual features are color, texture and shape.
Design of an Adaptive Nonlinear Controller for an Autonomous Underwater Vehicle

The paper addresses the problem of trajectory tracking control of underactuated underwater vehicles in the presence of parametric uncertainty. Based on backstepping design approach, an adaptive control law for 6 DOF model is derived for the trajectory tracking problem. The desired trajectory does not need to be of a particular type and in fact can be any sufficiently smooth bounded curve parameterized by time. The reference yaw velocity has not to satisfy various kinds of persistently exciting conditions which is often needed in previous works. It is assumed that system parameters are unknown and have to be estimated accurately. The stability analysis is carried out using the Lyapunov theorem. The effectiveness of the proposed control method is demonstrated through numerical simulations.
Analysis Of Barriers For Implementing Green Supply Chain Management In Small and Medium Sized Enterprises ( SMEs ) of India

Green Supply Chain Management (GSCM) is recent innovation for enhancing Supply Chain efficiency. The ̳Green‘ component stresses upon the need of environment friendly Supply Chain by focusing on the reduction of waste. The requirement of GSCM in India has increased due to several factors including corporate responsibility, environmental & legislative reasons and competition. Nevertheless, many manufacturing firms in India have not adopted the GSCM yet. This study aims to find out the major barriers in implementing GSCM in Small and Medium scale Enterprises (SMEs) of India and rank them according to their effect. Barriers have been identified through literature review. A research questionnaire has been used to conduct primary data collection. Two Multi Criteria Decision Making tools, namely Interpretive Structural Modeling (ISM) and fuzzy Analytical Hierarchy Process (AHP), are used to rank different barriers. The results may help SMEs in establishing an economically and environmentally sound supply chain network.
Synthetic N-(Alkyl/Aralkyl)-N-(2,3-Dihydro-1,4-Benzodioxin-6-Yl)-4-Methylbenzenesulfonamides as Acetyl cholinesterase Inhibitors

Introduction The basic sulfonamide group SO2NH is found in numerous biological active compounds including antiviral, anticancer, anti-thyroid, antimicrobial, anti-inflammatory and antibiotics drugs along with inhibitors of carbonic anhydrase [1]. Because of their less cost, less toxicity and astonishing activity, these are extensively used as antibacterial agents [2-6]. Furthermore, sulfonamides are also employed as, anti-leprotic, diuretics, antitumor agents, tuberculostatics and oral hypoglycemic drugs [7-8]. Aliphatic sulfonamide derivatives act as antifungal agents [9]. Sulfonamide based antibiotics are utilized as veterinary medicines to treat infections in livestock herds [10]. Compounds bearing benzodioxane ring system exhibits different biological activities such as anti-oxidant [11], anti-hepatotoxic and antiinflammatory [12,13]. Aryl sulfonamides having benzodioxane moiety have been recognized as inhibitors of ExoU [14]. Because of their non-interaction to defence mechanism of host and broad spectrum activity some effective derivatives of sulfonamides are widely used to treat gastrointestinal and urinary tract infections [15]. Some sulfonamides were also found to be potent carbonic anhydrase, COX-2 and caspase inhibitors [16-18].
Study of analgesic and anti-inflammatory activity of different fractions from Kydia calycina Roxb

The present study was carried out to evaluate the analgesic and anti-inflammatory activity of various fractions of toluene, ethyl acetate, butan-2-one and n-butyl alcohol of Kydia calycina Roxb (Malvaceae). The fractions of the plant material were evaluated for the analgesic activity by acetic acid-induced writhing test, Eddy’s Hot plate test in mice and formalin-induced test and anti-inflammatory activity was screened by Carrageenan-induced rat paw edema and cotton pellet induced granuloma formation in rat models. The test fractions were showed analgesic and antiinflammatory effect in dose dependent manner and ethyl-acetate fraction was found to be most potent among the test fractions. At 150mg/Kg b.w. p.o dose Ethyl-acetate fraction significantly inhibited 69.02% writhing response and 64.28%, 78.21% Formalin induced analgesic in mice. The fraction with same dose showed significant 70.87% inhibition of Carrageenan induced rat paw edema and 42.72, 39.16% anti-proliferative effect of cotton pellet in rats and also different fractions of Kydia calycina Roxb showed prompt analgesic and anti-inflammatory activity due to dual inhibition properties on COX-2 and 5-LOX.