Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstacles and Challenges of ICT Adoption by SMEs: Perspective from Istanbul

This work studies the possible obstacles for SMEs in Information Communication Technology adoption by determining the most used technologies then investigate the perceived value added by the use of these technologies and finally by shedding light to different obstacles for SMEs to adopt these Information Communication Technologies (ICT). The sample used in the study is comprised of manufacturing 112 SMEs in Istanbul chosen according to the database of The Union of Chambers and Commodity Exchanges of Turkey. Using an on-line questionnaire the middle and top level managers of these enterprises provided the research with the data and accordingly using descriptive statistics and multivariate analysis useful findings emerged. The primary concern for the managers is unsurprisingly the immediate return on investment, needed hardware and infrastructure and possible need for extra personnel. ; accordingly, more advanced ICT use is low. The personal characteristics of managers also play an important role especially in production integrating ICT. This study reveals the internal and external obstacles in ICT adoption for SMEs, extending the current theory and recommendations for SME practitioners and policy makers. Suggestions for further areas of exploration and future research are also presented.
Vulnerability of SDN Network Architecture and Proposed Countermeasures on Enhancing Security

The current problems raising as a horizon in the computational and networking sector is based on the unimaginable increase of high numbers of users which in turn results in high data traffic, limitations over products which are vendor specific, incurring high expenses in maintaining the existing network. This dilutes a major part of the beneficiaries in the sector to move towards Cloud Networks. All these happenings in the past has quietly increased the risks and challenges in the aspect of security considering both data and the infrastructure accommodating the data. In an attempt to address almost a major portion of the existing above said problems, Software Defined Networking was highly anticipated, however, it was considered as a theoretical approach. After the implementation of SDN networks by industrial giants like Google, the SDN concepts again managed to reach the safer hands of the researchers in the movement of enhancement. A very rapid and high speed research work has been initiated by researchers all around the globe in analysing the risk factors and implementation barricades stated in the Software Defined Networking architecture. The research work focus on adding values to the Quality of Service, Latency, Load Balancing and most importantly the security aspects in various metrics of the Software Defined Networking Architecture. The odd man out architecture of Software Defined Networking by decoupling data and control plane allows the network to be configured and maintained in a real time scenario pertaining to pose a complete view of the network and its flow. The fact that is considered as an advantage itself is a factor of question in the case of security in the overall SDN architecture. This paper focuses on a detailed view of SDN architecture with the existing security feature and continues with the expected threats and classifying the weak points in the SDN. This paper also briefs about the pros and cons of the existing applications in the SDN architecture.
Digital Image Segmentation Using Median Filtering and

Digital color image segmentation of noisy images is nowadays an important area of research in the field of computer vision. This paper advocates an effective image segmentation approach for noisy images. The approach can broadly be divided into application of two strategies namely noise removal from noisy images using median filtering on initial digital color image and secondly applying watershed algorithm using distance transform over noise free images obtained after median filtering of noisy images. Comparative analysis is also shown in this paper between application of only watershed algorithm on noisy image and application of proposed approach over watershed algorithm. Statistical results are included in the paper to . support our approach of median filtering and morphological segmentation of noisy digital color image.
A STUDY OF THE EXISTENTIAL MEANING OF SECONDARY SCHOOL TEACHERS IN RELATION TO THEIR GENDER AND WORK EXPERIENCE

The present study has tried to explore the existential meaning of secondary school teachers IN relation to their gender and years of experience. A representative sample of 150 teachers from the secondary schools of New Delhi was randomly selected. A tool developed by the researchers was used to assess the existential meaning of the secondary school teachers. The study revealed that there was no significant difference between existential meaning of secondary school male and female teachers. However, contrasting results were found out for teachers with work experience of less than 10 years and more than 10 years and the study showed that there was a significant difference between the existential meaning of these groups. Significant difference was also not found between the existential meaning of male secondary school teachers with less than 10 years and more than 10 years as their work experience. There was a significant difference in the existential meaning of female secondary school teachers with less than 10 years and above 10 years of work experience.
Motorcyclists’ Mortality Pattern in Colombia from 2000 to 2013: A Longitudinal Study

Background: Road traffic injuries and fatalities have increased in Colombia despite that 2011-2020 had been declared as the Decade of Action for Road Safety in the world. Then, this paper aims to describe the trends of motorcycle mortality patterns in Colombia from 2000-2013. Methods: A longitudinal study is carried out using mortality records from the Colombian National Department of Statistics, and following the International Classification of Diseases -ICD-10 V200 to V299. Results: From 2000 to 2013 the study founded a near 100% increase in the number of motorcycle fatalities in Colombia; the occurrence rate increased from 3.6 to 5.7 per 100,000 populations. 65.4% of the fatalities involve individuals from 15 to 34 years of age. Conclusions: Motorcycle fatalities have become a serious public health issue in Colombia. For the 14-year of the study period, slightly more than 28,800 motorcyclist’s deaths have been attributed to different factors and social determinants.
Secure Internet Banking with Visual Authentication Protocol

Keylogging or keyboard capturing, is the action of recording (or logging) the keys struck on a keyboard typically in a covert manner so that the person using the keyboard is unaware that their actions are being monitored. It also has very legitimate uses in studies of human-computer interaction. There are numerous keylogging methods ranging from hardware and software-based approaches Involving human in authentication protocols, while promising, is not easy because of their limited capability of computation and memorization. We demonstrate how careful visualization design can enhance not only the security but also the usability of authentication. We propose two visual authentication protocols: one is a one-time-password protocol, and the other is a password-based authentication protocol. Our approach for real-world deployment: we were able to achieve a high level of usability while satisfying stringent security requirements.
AN EMPIRICAL STUDY ON JOB SATISFACTION AMONG FACULTIES IN SELECTED PERSONAL UNIVERSITIES OF UTTAR PRADESH

Job Satisfaction is that the basic need of the workers. Worker productivity is additionally directly proportionate to the work satisfaction. Many previous researched has been done on the work satisfaction of company sectors, but only a few studies has been done and been centered for the work satisfaction of the academicians of latest personal universities. The in depth analysis that has been done on levels of job satisfaction could have distinctive applications to tutorial school. This is often very true once the separation between satisfaction and discontentment is viewed irrelevance the intrinsic and extraneous characteristics of educational employment. The aim of this descriptive correlation study was to look at factors poignant job satisfaction of school members of personal universities instead explained by Herzberg job incentive and hygiene factors.
Performance of Hybrid Ensemble Classification Techniques for Prevalence of Heart Disease Prediction

In medical science, heart disease is being considered as fatal problem and in every seconds most of the people dies due to this problem. In heart disease, typically heart stops blood supply to other parts of the body. Hence, proper functioning of body stopped and affected. In this way, timely and accurate prediction of heart disease is an important concern in medical science domain. Diagnosing of heart patients with previous medical history is not being considered as reliable in many aspects. However, machine learning techniques have mystery to classify heart disease data efficiently and effectively and provide reliable solutions. In the past, prediction of heart disease problem various machine learning tools and techniques have been adopted. In this study, hybrid ensemble classification techniques like bagging, boosting, Random Subspace Method (RSM) and Random Under Sampling (RUS) boost are proposed and performance is compared with simple base classification techniques like decision tree, logistic regression, Naive Bays, Support Vector Machine, k-Nearest Neighbor (KNN), Bays Net (BN) and Multi Layer Perceptron (MLP). The heart disease dataset from Kaggle data source containing 305 samples and Matlab R2017a machine learning tool are considered for performance evaluation. Finally, the experimental results stated that hybrid ensemble classification methods outperforms than simple base classification methods in terms of accuracy
A NODE-TAG PREDICTION ALGORITHM FOR TOUCH SPAM DETECTION IN MOBILE AD NETWORKS

: A touch user interface (TUI) is a computer-pointing technology based upon the sense of touch (haptics). Touch-spam is a type of fraud that occurs over TUI gadgets ex. Smartphones, tablets, phablets, touch laptops etc. It advertising networks being a key beneficiary of this spam. In present days, smartphone gaming applications actually happens in TUI applications when a person, automated script, computer program or robotic action imitates a legitimate user of a TUI application touching on an advertisement (ad), for the purpose of generating a charge per touch without having actual interest in the target of the ad’s popup. Touch-spam is becoming an issue due to the (apps) are playing a vital role to attract mobile-advertisements (ads) since their pocket portability and other versatile features. Popular apps are able to read the user personalized data to process user interests helping to generate customized ads. Touch-spam in smart phone apps is a fraudulent or invalid tap or touch on online ads, where the user has no actual interest in the advertiser’s site. It requires a user touch on online ads that pop-up dynamically in smartphone gaming apps. It all need the user to tap the screen close to where the ad is displayed . While the ad networks continue taking active measures to block click-spam today, the touch-spam still creeping under the TUI. It is being used by spammers to misappropriate the advertising revenue. The presence of touch-spam is largely unknownThen we propose a node-tag propagation algorithm on click-through logs to identify spam Apps in Smartphone-game Apps. We validate our methodology using click/touch-log data from major ad network. Our findings highlight the extremity of the spam in mobile advertising.
EFFICIENT CHANNEL SELECTION FOR COGNITIVE RADIO AD-HOC NETWORK

: “Cognitive radio” has emerged as a new model for next-generation wireless networks whose target is to increase utilization of the scarce radio spectrum (both licensed and unlicensed). The cognitive radio transceiver Learn from surrounding environment and adopt itself according to the environment. Intelligent algorithms are used to learn the surrounding environment, and the information obtained is utilized by the transceiver to choose the frequency band (i.e., channel) for transmission as well as transmission parameters to achieve the best performance ,this paper put forward a scheme for efficiently selecting channel for transmission in a multichannel cognitive radio ad-hoc network. The hidden PU (primary user) problem is also addressed in this paper.
Metric Base Analysis and Modeling Experiments of Routing Protocols in MANETs and VANETs Wireless Network using Real Time Scenarios

Ad hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, it may be necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its destination, due to the limited range of each mobile host’s wireless transmissions. Mobile ad hoc networks (MANET) do not rely on any fixed infrastructure but communicate in a self-organized way. Vehicular Ad hoc Network (VANET) is a subordinate of Mobile Ad hoc Network (MANET). In this paper, we have discussed experimental metrics base framework in which Data Drop (DD) and throughput are measured and analyzed for Mobile Ad-hoc Network (MANETs) and Vehicular Ad-hoc Network (VANETs). Moreover, the authors have contributed the performance comparison of routing protocols. Novel contribution in this paper is to compare different routing protocols in both MANETs and VANETs. The proposed system classifies the changing in MANET nodes and VANET vehicles data drop, and throughput. It was observed from the results that in form of high through put and low packet drop DSR shows better performance compared to DSDV and AODV in both VANET and MANET. Protocols which are being analyzed in this paper are: AODV, DSR and DSDV. The performance parameter includes Data Dropped and throughput.
Conceptual Algorithm for Clustering Search Result

- Search Result Clustering problem means clustering of search result returned by search engine. Web contains large amount of data. So finding relevant information is a difficult task. Lingo algorithm is used for clustering search result. This algorithm finds out meaningful cluster label. Documents are grouped under that cluster label. It retrieves the document based on conceptual content rather than term matching method. The Lingo algorithm combines common phrase discovery and latent semantic indexing techniques to separate search results into meaningful groups. It looks for meaningful phrases to use as cluster labels and then assigns documents to the labels to form groups. Lingo algorithm is different from traditional method because traditional method groups the documents first. After this it decides the cluster label. So Lingo is description-comes-first algorithm. In this paper we have proposed Lingo Algorithm for clustering search result.
Health Care Patient Monitoring using IoT and Machine Learning

: Security and privacy is the most essential thing in Big Data environment, there are many algorithms have been proposed in existing approaches for data privacy as well as security. In many applications like Healthcare are banking applications having available data where third party attacker can easily access the privacy of victims. In Internet of Things (IoT) environment there is the major issue of data security. In this paper we proposed high dimensional Healthcare big data security as well as disease prediction using machine learning approach. Basically the system has categorized into two sections first we implement IoT based environment which generates the data of patient body. This section be used some wearable devices like ECG sensor, BP sensor temperature sensor heart rate sensor etc. Once data has generated from various sensors it will upload on cloud database. In the second phase we monitor the data which is generated by various sensors. Here we have generated Android base graphical user interface with monitors the data 24 by 7. Where machine learning algorithms are has used to predict the disease of patients. The authentication mechanism will achieve role based access control for specific users and proposed machine learning algorithms provides the patient disease probability according to given parameters. The experiment analysis has done based on the partial implementation of system which provide proposed system is more effective than some existing IoT systems.
A new outlier detection approach to discover low hit web pages using sequential frequent pattern mining to improve website’s design

The Internet offers huge volume of data to the users and grows rapidly every day. The web server creates log files regarding details about the page, IP address of the user, browser, and operating system used and time/date stamp regarding browsing patterns and this data is mined to extract useful information using web usage mining. The primary objective of this paper is to find the low hit pages of a website from the log files using finding outliers in sequential mining concept. To cater to the need of this objective, a new algorithm named “Detect Anomaly in Sequential Pattern Algorithm (DASPAT)” is proposed. The proposed algorithm creates candidates using Apriori like approach and discovers the unusual browsing behavior of the users, and the detected UBB are treated as outliers. This paper introduces a new approach to find the low hit web pages in tandem to enable the designers to understand how the user browses the site and allow them to redesign the web site.
BRAIN COMPUTER INTERFACE BASED WHEELCHAIR: A ROBOTIC ARCHITECTURE

People with physical disabilities depend on technology for assistance and physical control. This paper presents non-invasive brain controlled wheelchair. Electroencephalogram (EEG) signals are used for controlling the wheelchair movement. Proposed design includes a novel approach for control wheelchair using Brain Computer Interface (BCI) technology. For validation of design a robotic module has been developed which can move under the control of human thoughts.
SEASONAL TOURISM DEMAND IN LEBANESE SKI RESORTS

Purpose of the study: The main objective of this study is to analyze the profile of Lebanese ski resorts’ visitors, their motivations, preferences, and perceptions. Thus, authors seek to analyze the characteristics of seasonal tourism demand in Lebanese ski resorts for the sake of reducing seasonality. Methodology: Quantitative methods were used and four out of the five ski resorts in Lebanon were analyzed. An online questionnaire was distributed to visitors, using a mix of self-selection and snowball sampling techniques. The questionnaire resulted in 206 respondents. Responses showed high internal validity with a Cronbach’s alpha of 0.83. Main Findings: The relationship between place attachment and annual visitations was validated and authors were able to point the deteriorated value for money among resorts. Furthermore, the K-mean clustering resulted in segmenting visitors into three groups of interest to be targeted in order to reduce seasonality. Implications: This study helps the winter and snow tourism stakeholders in Lebanon to reduce tourism seasonality and consequently develop sustainable tourism products. It also enhances previous studies conducted on seasonal tourism demand. Limitations: The main limitation is that not all stakeholders were examined. Further research is needed to be conducted on visitors’ decision-making processes and their actual behavior. The proposed segments in this study are subject to future tests and investigations. The novelty of the study: With no previous research attempting to analyze seasonal demand in Lebanese ski resorts; this study has a vital role to play in developing sustainable tourism products and helping winter and snow tourism stakeholders to reduce tourism seasonality. The clusters presented at the end of this paper are considered as a new proposition for ski resorts’ managers who are shifting toward all-season operations.
Forensic examination of a boar died during transport aimed at reimbursement of insured animal.

Th e long lasting transport of high-value animals during the import represents the signifi cant stress factor for animals, which can lead to severe health problems and even animal death. Th us, insurance of animals against the event of death is highly recommended. In case of the damage of the insured animal (death) it is necessary to implement all statutory and contractual measures for determining the reasons of the damage in order to eff ectively compensate the loss in the country, in which the animal is insured. In this article, we described the case control study, a death of breeding animal (boar) during the transport (import) from Denmark to Serbia. Th e animal was insured in exporting country and the forensic expertise was necessary to the purpose of adequate insurance compensation. On the basis of applied methods, which included medical history, records of clinical fi ndings, gross pathological fi ndings and laboratory examination, the cause of death of imported boar was identifi ed (heart failure and suff ocation). Th e Danish insurance company accepted the results of the examination and compensation has been successfully implemented on behalf of the dead
Anomalies Detection Based on the ROC Analysis using Classifiers in Tactical Cognitive Radio Systems: A survey

Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis based on the anomaly detection using classifiers for using them in research. In recent years have been increasingly adopted in the machine learning and data mining research communities. This survey gives definitions of the anomaly detection theory and how to use one ROC curve, what a ROC curve, when we use ROC curves.
Temperature Measurement of Nine Phase Induction Motor Drive Without Load and Various Load Conditions

Research and Development(R&D) in recent days towards the power electronic system and Motor Drives Concentrate their attention on Multi-phase motor drive systems, due to some inherent advantages over three-phase motor drives like high torque density, efficiency, heavy loads etc. Since variable speed of AC power drives need converters for AC to DC power supply the number of phases can be change very easily, this led to increase the interest of multiphase drive applications in the field of Agriculture, aircraft, Electronics Vehicles etc. Almost all loads Have nonlinear characteristics which exhibits heat at parts of the loads. When load is subjected to high current density cause equipment failure hence presented work focus on the Experimental analysis of Temperature measurement at various body parts of Nine Phase Induction Motor with No load and load condition using PWM drive. Data Analysis has been done to monitor temperature effect on stator, rotor and other parts of the motor by interfacing temperature module kit with laptop monitor screen using J type thermocouples. Keywords— Nine phase Induction Motor, Nine phase Inverter, PWM, Microcontroller kit, Temperature Modul kit, Power Analyzer.