International Journal of Data Science (16 papers in press)
Robust Computational Modeling of the Sodium Adsorption Ratio Using Regression Analysis and Support Vector Machine
by Alireza Rostami, Milad Arabloo, Alibakhsh Kasaeian, Khalil Shahbazi
Abstract: In present study, two new methods including least-square support vector machine (LSSVM) and regression-based model, were created for accurate estimation of the adsorption ratio of sodium in terms of ionic concentrations of calcium (Ca2+), magnesium (Mg2+), and sodium (Na+); the bicarbonate (HCO3-) to Ca2+ ratio; and salinity/conductivity of the used water so as to explain the impact of water quality on the irrigation water using a reliable literature database. The results of the developed models were compared with a commonly used model in literature using visual and statistical parameters. Consequently, the supremacy of the regression-based approach is demonstrated with the average absolute relative deviations (AARDs) of 0.06% for HCO3-/Ca2+ ratio?1 and 0.28% for HCO3-/Ca2+ ratio>1. Finally, it should be mentioned that the proposed methods are easy-to-apply and sufficiently accurate which require the less calculations leading to the rapid estimation of sodium adsorption ratio in wide range of operational conditions.
Keywords: Sodium adsorption ratio; Irrigation water; Salinity; Least square support vector machine; Error analysis.
Hierarchical non-Archimedean DEA models: Application on mobile money agents locations in the city of Harare
by Jacob Muvingi, Arshad Ahmud Iqbal Peer, Farhad Hosseinzadeh Lotfi
Abstract: Hierarchical non-Archimedean data envelopment analysis (DEA)rnmodels are proposed to evaluate the efficiency of two types decision making units (DMUs) which are integrated. The determination of non-Archimedean values was extended to cater for decision-making units (DMUs) with a hierarchical group structure. The proposed approach was applied in the location analysis of mobile money agents locations. In a bid to improve adjusted efficiency ratings of groups with unequal size, an adjustment value on selected groups average efficiency ratings was determined through the identification of ideal location groups proxies. Three district location efficiency ratings (DLER-1, DLER-2, and DLER-3), were respectively generated through the non-Archimedean DEA hierarchical method, the DEA hierarchical method where the non-Archimedean epsilon is ignored, and the treatment of district locations as a system made-up of suburb locations. The application of the non-Archimedean value on district locations efficiency analysis reduced the number of efficient district locations.
Keywords: Data envelopment analysis; Location; Mobile money agents;Hierarchical; Parallel systems; non-Archimedean value.
Reducing Feature Selection Bias Using a Model Independent Performance Measure
by Weizeng Ni, Nuo Xu, Honghao Dai, Samuel Huang
Abstract: Feature selection is an important step in the process of learning from data, especially when dealing with dataset with small sample size and high dimensionality. A popular approach for feature selection is the so called wrapper approach. In recent years, researchers have realized that wrappers have a feature selection bias due to data overfitting. External cross-validation or dual-loop cross-validation has been proposed to solve this problem. However, cross-validation approaches tend to bring in excessive variability for small sample size data with high dimensionality. This paper shows that a model-independent approach for feature selection; namely, minimum expected cost of misclassification (MECM), can reduce feature selection bias without the need of cross-validation. A designed experiment was conducted using a synthetic dataset. The results show that 10-fold dual-loop cross-validation based wrapper feature selection has around 33% higher error rate than the noise-free error rate and fails to identify discriminative features consistently in all 10 folds. On the other hand, MECM can select more discriminative features than dual-loop cross-validation and shows more robustness to different classification models than wrapper-based approach. A real-word colon cancer dataset is further used to demonstrate the effectiveness of MEMC.
Keywords: Feature Selection; Overfitting; Microarray Data; Model-independent.
Supervised Local Community Detection Algorithm
by Ali Choumane, Abbass Al-Akhrass
Abstract: Community detection aims to partition a network into internally densely connected groups of nodes. In huge networks, exploring the whole network as done in global community detection algorithms is computationally very expensive. Moreover, for applications such as antiterrorism, the spread of disease on networks and viral advertising, researchers are now more interested to find the community surrounding one or a few individuals. In this context, we propose a new local community detection algorithm that starts from a seed node and iteratively expands it to reach the community that resembles the most to a real-life community. The expansion process is controlled by a neural network classifier that decides which nodes to add to the community being expanded. To build this classifier, we propose three measures that allow quantifying nodes relationship strengths to their communities. We train our model using the well-known LFR benchmark networks. It is built in a way to be used on any new network without the need to train it again. Experiments achieved on both, an extensive set of LFR datasets and existing real-world networks from different application domains, proved the high performance of our method as compared to the baselines.
Keywords: network analysis; community detection; local community; supervised learning;.
Special Issue on: Scalable Provision of Semantically Relevant Web Content on Big Data Frameworks
Dynamic sorting and average skyline method for query processing in Spatial-Temporal Data
by John A, Shubham Kumar Singh, Adimoolam M, Ananth Kumar T
Abstract: With the continuous advancement in mobile computing and the development of positioning in devices, querying of moving objects on road networking is an important task in the internet world. As a result of this development, a huge amount of data management and query processing plays a vital role in spatial and temporal applications. A large amount of data that is being coupled with different query processing requires efficient indexing. The main problems in spatiotemporal are managing data indexing, update, and query processing. This work-related to query processing in Spatio-temporal data to update different dynamic queries of users. The previous work of query processing will not support all the end-users. The proposed dynamic sorting and average skyline method will support different kinds of queries. This method is dynamic sorting and average skyline (DSAS) and it produces effective query processing to different users at different locations. The skyline query processing technique produces the result for the dominating objects when compared with the other query processing techniques.
Keywords: Spatial-Temporal Data- Query processing – skyline Query Processing.
A NOVEL SECURITY SCHEME USING DEEP LEARNING BASED LOW OVERHEAD LOCALIZED FLOODING ALGORITHM FOR WIRELESS SENSOR NETWORK's
by T. Ananth Kumar, R. Raj Mohan, M. Adithya, R. Sunder
Abstract: A Wireless specially appointed system is a self-sorting out, self-arranging confederation of remote frameworks. WANET gadgets will interface and leave the system non-concurring freely, and there are no predefined customers or server. The dynamic topologies, portable correspondences structure, decentralized control, and namelessness makes numerous difficulties to the security of frameworks and system foundation in a WANET domain. Therefore, this outrageous type of dynamic and circulated model requires a revaluation of customary ways to deal with security implementations. Kill the spillage intrigue happened by the at least two Wireless gadgets imparting by means of ideal hand-off with decentralized Wireless hubs utilizing Wireless specially appointed system. The general deferral is decreased with increment in throughput. We propose a Deep Learning based Low Overhead Localized Flooding (DL-LOLF) strategy dependent on Query Localization system. The directing packets, which proliferate back to a source, are disposed of to lighten superfluous rebroadcasting. This venture contemplates the significant of remote correspondence under Attacker plot where identifying the dropper (spillage) hub. To give information about the security improvement in remote correspondence organizes by utilizing Network Layer calculation. Re-enactment results show that our proposed technique can decrease steering overhead and MAC impact rate without giving up parcel conveyance proportion contrasted with existing conventions
Keywords: WANET; Deep learning; Bait message; LOLF; Dropper node;.
Proficient Approaches for Scalability and Security in IoT through Edge/Fog/Cloud Computing: A Survey
by SURESH KUMAR K, Radhamani A.S, Sundaresan S
Abstract: Cloud computing has become an advanced computing standard which came into existence by the introduction of the technologies like 5G and Internet of Things (IoT). For data warehousing, cloud computing paves an important role in processing and implementation. The security related issues are identified when the information is stored into the cloud. As of now, the data available in the cloud is enormous, so it founds to be difficult in accessing and analysing the generated datas because of the existence of problems like limited bandwidth, limited resources, inactivity and more security challenges. For solving these kind of issues some of the other technologies like fog computing and edge computing is introduced along with the cloud computing. In recent days, security has become more challenging task in accessing real time datas. In this paper, the IoT secrecy is analysed with the usage of it along with Edge/Cloud/Fog Computing. The various algorithms used, objectives, the proposed methodologies and its advantages are discussed in this paper.
Keywords: Internet of Things (IoT); 5G; Data Ware Housing; Security Challenges; Cloud computing; Fog Computing and Edge computing.
Special Issue on: Healthcare Evolution in Big Data Analytics Challenges, Trends and Applications
A top-down Outlook on Artificial Intelligence applied to Healthcare Systems and possible Advantage of an Unsupervised Learning Tool to medical Issues
by Vicente Gonzalez-Prida, Jesús P. Zamora
Abstract: The objective of this paper is to discuss the implementation of AI in a sector like healthcare, providing a methodology based on Machine Learning for the pattern recognition in the behavior of a specific outbreak. With this purpose, this paper starts defining a conceptual map on Artificial Intelligence, making a difference between that area and the Machine Learning field. Afterwards, the document deepens in Machine Learning concepts linking them with the usual learning processes and their corresponding applications, providing a view as well on Artificial Neural Network, in order to observe advantages of this useful tool. Subsequently, possible applications for medical issues are depicted. With that, a case study is described proposing an unsupervised learning process for the pattern recognition applying the Jensen-Shannon Divergence. This example is based on the behavior of the reproductive number of a specific outbreak among different areas. Some conclusions are commented at the end, indicating as well possible future research lines on this field
Keywords: Artificial Intelligence; Healthcare System; Jensen-Shannon Divergence; Machine Learning; Medical Issues; Neural Network; Unsupervised Learning.
Intelligent Technique for Human Authentication using Hand vein
by Mona Abdel Aziz, Mohamed Roushdy, Abdel-Badeeh M. Salem
Abstract: In this paper, we propose a new intelligent technique to authenticate human using dorsal hand vein (DHV) pattern. Recently, authentication was adopted by smart hospitals in many countries as an intelligent tool for patient identification to prevent insurance and can connect the patient with his/her medical record securely. In this paper we developed an image analysis technique to extract region of interest (ROI) from DHV image. After extracting ROI we design a sequence of preprocessing steps to improve hand vein images using Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance hand vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation .This technique has been applied on the Bosphorus Hand Vein Database. The experimental results show that the result of (CRR) is 91.2 %
Keywords: Biometric; dorsal hand vein; computational intelligence; feature extraction; PCA; K-NN; machine learning.
Special Issue on: Trends and Applications of Data Science and Big Knowledge
Configuration of WiMAX Network Supporting VoIP Handover Using OPNET Simulator
by Dina Ibrahim
Abstract: Worldwide Interoperability for Microwave Access or WiMAX is considered as a promising technology for providing wireless connectivity. This is because it has a large coverage area, high-speed data rates, and low cost of deployment. Handover management for Voice over Internet Protocol (VoIP) service is one of the recent issues that face WiMAX technologies that require special configurations. In this paper, we study the performance of the WiMAX network during the handover of a mobile node from the base station to another. It is known that when the node moves away from its home agent it can receive packets but the sending process decrease with the handover appears. In this paper, we will also recover this problem using the OPNET simulator shown the network performance in traffic send and receive for a VoIP application.
Keywords: WiMAX; VoIP service; Handover; OPNET simulator.
The Impact of using SR-SIFT Algorithm on Various Banknotes
by Suha Aharbi
Abstract: Most commercial transactions are still done using physical currencies. Detection of fake currency can improve the reliability of ATMs and counting machines in order to ensure proper maintenance operations and confirm the value and authenticity of currency. Many paper currencies or banknotes are exposed to the presence of some problems, such as folding, rolling, and wrinkling. In this research, we study and analyze the impact of applying one of the currency recognition algorithms, which is Speeded up Robust Scale Invariant Feature Transform (SR-SIFT) algorithm in detecting the currency paper in the normal presentation and also in any troubles like rolled, wrinkled, or folded. The results show that the SR-SIFT algorithm can usefully recognize the currencies in a different situation with high accuracy.
Keywords: currency recognition; SR-SIFT; folded banknotes; wrinkledrnbanknotes; rolled banknotes.
Analyzing the Steps of Currency Recognition Systems
by Suha Aharbi
Abstract: Recently, we have noticed that there are many organizations that still use banknotes and most of the commercial transactions are still done using physical currencies Therefore, we need to have systems to recognize and detect the currency. Existing systems depend on many steps you go through in order to analyze the currency and know its type and its value. Examples of these systems Scale-Invariant Feature Transform (SIFT), speeded up robust features (SURF), and Speeded up Robust Scale Invariant Feature Transform (SR-SIFT). In this paper, we explain the most important steps that the currency analysis system goes through them like database setup, image preprocessing, image analysis, currency recognition and recognition analysis with an explanation of each stage and clarification with examples.
Keywords: currency recognition; image preprocessing; image analysis.
Technology-Based Services for Deaf and Dumb People
by Dina Ibrahim
Abstract: Nowadays, the deaf and dumb category represents a large number of people in our society, especially in Saudi Arabia. The sign language is considered to be the main way to interconnect with deaf and dumb, but most of the normal people have no ideas about this language. For this reason, there is a growing need to find alternative solutions for communicating with them. In this paper, we propose three different methods that can help deaf and dumb people to have a better and easy life. First, the ABSHER for Deaf service can help deaf people to use their account for the ministry of Interior which called ABSHER. Second, the Keyboard for Deaf feature can support the sign language images and symbols in the keyboard as a different feature to convert between the normal person language and the deaf language. Finally, Be Aware of your medicine that can help deaf people to understand all the information about the medicine given to them.
Keywords: Deaf and Dumb; Sign language; ABSHER; keyboard.
Safety Souls Mobile Application for Emergency Response System
by Maha Alotaibi
Abstract: Nowadays, there are numerous catastrophic events, which affect a large number of people in the world including crises, fires, floods, road accidents, earthquakes, and terrorist attacks. The majority of public people use mobile and internet all day long. From the literature review, we found that the coordination between agencies in emergency responses is not satisfactory. Different emergency organizations should share different types of resources such as information, equipment, vehicles, etc. For that reason, the emergency response teams need to use an effective coordination framework to mitigate the results of any emergencies such as loss of life. Under these circumstances, respondents, emergency services, and volunteers work hard to communicate and divide resources among themselves. The purpose of this paper is to investigate the coordination in emergency response with mobile techniques, by developing a flexible and dynamic mobile platform which offers tracking mechanism and information management.
Keywords: Emergency response; Coordination; Emergency organizations; Mobile Application platform.
Smart Gatekeeper using QR-Code
by Rawan Flifel
Abstract: Electronic Gatekeeper is a web system that helps parents or car drivers to call their students from an educational institution. In traditional, this task requires security man to authenticate the man who wants to take his student from an educational institution, especially in the morning, this case has a lot of problems and issues. Our target is to take advantage of the technology to solve problems, save time, and effort. The proposed system started by a calling process done by a call from the parent or the car driver to the student by our system, then the name of that student will appear on a monitor inside the institution also the name will be spoken by speakers. Finally, the student can exit from the gate by passing a special and unique QR code card to get permission to leave the institution by our proposed Gatekeeper system.
Keywords: Smart Gatekeeper; QR code; QR Reader; Educational institution.
Decision Making Using Document Driven Decision Support Systems
by Manal Abdullah, Naela Bahurmuz, Rishaa Alnajim, Zainab Al-Shingiti
Abstract: New innovations in the technologies have provided several opportunities to support the process of decision making by utilizing Decision Support Systems (DSS). Nowadays, DSS are one of the most important management systems in the modern environment of business. The understanding of DSS and how these systems can include business intelligence and successful management is critical to managers and decision-makers. Document-driven DSS is one of the most common types of DSS. It established to assist decision-makers and managers in the process of decision making by facilitating the steps of extracting information and knowledge management. Document-driven DSS searches for documents by particular keywords on a web page and utilizes a collection of processing and recovery technologies for complete documents recovery and analysis. This paper aims to introduce a basic understanding of Document-Driven Decision Support Systems (DDDSS) to decision-makers and managers. Also, it aims to help the researchers who interested in this field of DSS. Aggregately, this paper relies on the previous studies about DSS and document-driven DSS especially. It displayed the objectives of document-driven DSS and the types of documents that it used. Then, it presented the features of document-driven DSS and explained how does it work. It mentioned also the benefits of document-driven DSS. It showed the evolution of document-driven DSS. As well, the trends of it. The relation between the document-driven DSS and knowledge management was also clarified. Besides that, it covered some of the applications of document-driven DSS in the fields of enterprises and education
Keywords: Decision Support Systems (DSS); Document-Driven Decision Support Systems (DDDSS); Decision Making; Document Analysis; Document Recovery; Document Retrieval.