Enhancement of Web Security Against External Attack

The security of web-based services is currently playing a vital role for the software industry. In recent years, many technologies and standards have emerged in order to handle the security issues related to web services. This paper shows techniques to enhance the security of web services, and some of the recent challenges and recommendations of a proposed model to secure web services. It shows the security process of a real life web application, which includes; HTML5 forms, login security, and a single signon solution. This paper also aim to discuss the ten (10) most common web security vulnerabilities and how to prevent the web application from three (3) of the vulnerabilities. Amongst them are; SQL Injection, Cross Site Scripting and Broken Authentication, and Session Management

A Novel Design and Implementation of 8-3 Encoder Using Quantum-dot Cellular Automata (QCA) Technology

In recent years Quantum-dot Cellular Automata (QCA) has been considered one of the emerging nano-technology for future generation digital circuits and systems. QCA technology is a promising alternative to Complementary Metal Oxide Semiconductor (CMOS) technology. Thus, QCA offers a novel electronics paradigm for information processing and communication system. It has attractive features such as faster speed, higher scale integration, higher switching frequency, smaller size and low power consumption compared to the transistor based technology. It is projected as a promising nanotechnology for future Integrated Circuits (ICs). A quantum dot cellular automaton complex gate is composed from simple 3-input majority gate. In this paper, a 8-3 encoder circuit is proposed based on QCA logic gates: the 4-input Majority Voter (MV) OR gate. This 7-input gate can be configured into many useful gate structures such as a 4-input AND gate, a 4-input OR gate, 2-input AND and 2-input OR gates, 2-input complex gates, multi-input complex gates. The proposed circuit has a promising future in the area of nano-computing information processing system and can be stimulated with higher digital applications in QCA

Region-Based Distance Analysis of Keyphrases: A New Unsupervised Method for Extracting Keyphrases Feature from Articles

Due to the exponential growth of information’s and web sources, Automatic keyphrase extraction is still a challenging issue in the current research area. Keyphrases are very helpful for several tasks in natural language processing (NLP) and information retrieval (IR) systems. Feature extractions for those keyphrases execute a vital role in extracting the top-quality keyphrases and summarising the documents at a superior level. This paper proposes a new region-based distance analysis of keyphrases (RDAK) unsupervised technique for feature extraction of keyphrases from articles. The proposed method comprises six phases: data acquisition and preprocessing, data processing, distance calculation, average distance, curve plotting, and curve fitting. At first, the system inputs the collected different datasets to the preprocessing step by employing some text preprocessing techniques. Afterwards, the preprocessed data is applied to the data processing phase, and then after distance calculation, it is passed to the region-based average calculation process, then curve plotting analysis, and afterwards, the curve fitting technique is utilized. Finally, the proposed system has tested and evaluated the performance through implementing them on benchmark datasets. The proposed system will significantly improve the performance of existing keyphrase extraction techniques

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains

Keyphrase distance analysis technique from news articles as a feature for keyphrase extraction: An unsupervised approach

Due to the rapid expansion of information and online sources, automatic keyphrase extraction remains an important and challenging problem in the field of current study. The use of keyphrases is extremely beneficial for many tasks, including information retrieval (IR) systems and natural language processing (NLP). It is essential to extract the features of those keyphrases for extracting the most significant keyphrases as well as summarizing the texts to the highest standard. In order to analyze the distance between keyphrases in news articles as a feature of keyphrases, this research proposed a region-based unsupervised keyphrase distance analysis (KDA) technique. The proposed method is broken down into eight steps: gathering data, data preprocessing, data processing, searching keyphrases, distance calculation, averaging distance, curve plotting, and lastly, the curve fitting technique. The proposed approach begins by gathering two distinct datasets containing the news items, which are then used in the data preprocessing step, which makes use of a few preprocessing techniques. This preprocessed data is then employed in the data processing phase, where it is routed to the keyphrase searching, distance computation, and distance averaging phases. Finally, the curve fitting method is used after applying a curve plotting analysis. These two benchmark datasets are then used to evaluate and test the performance of the proposed approach. The proposed approach is then contrasted with different approaches to show how effective, advantageous, and significant it is. The results of the evaluation also proved that the proposed technique considerably improved the efficiency of keyphrase extraction techniques. It produces an F1-score value of 96.91% whereas its present keyphrases are 94.55%

Keyphrases Frequency Analysis from Research Articles: A Region-Based Unsupervised Novel Approach

Due to the advancement of technology and the exponential proliferation of digital sources and textual data, the extraction of high-quality keyphrases and the summarizing of content at a high standard has become increasingly difficult in current research. Extracting high-quality keyphrases and summing texts at a high level demands the use of keyphrase frequency as a feature for keyword extraction, which is becoming more popular. This article proposed a novel unsupervised keyphrase frequency analysis (KFA) technique for feature extraction of keyphrases that is corpus-independent, domain-independent, language-agnostic, and length-free documents, and can be used by supervised and unsupervised algorithms. This proposed technique has five essential phases: data acquisition; data pre-processing; statistical methodologies; curve plotting analysis; and curve fitting technique. First, the technique begins by collecting five different datasets from various sources and then feeding those datasets into the data pre-processing phase using text pre-processing techniques. The preprocessed data is then transmitted to the region-based statistical process, followed by the curve plotting phase, and finally, the curve fitting approach. Afterward, the proposed technique is tested and assessed using five (5) standard datasets. Then, the proposed technique is compared with our recommended systems to prove its efficacy, benefits, and significance. Finally, the experimental findings indicate that the proposed technique effectively analyses the keyphrase frequency from articles and delivers the keyphrase frequency of 70.63% in 1st region and 10.74% in 2nd region of the total present keyphrase frequency

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11

KDA: An unsupervised approach for analyzing keyphrases distance from news articles as a feature of keyphrase extraction

Automatic keyphrase extraction remains a significant and difficult issue in the current research domain because of the exponential explosion of information and internet sources. Various activities involving natural language processing and information retrieval systems greatly benefit from the use of keyphrases. To extract the best keyphrases and summarize the documents to the highest standard, feature extractions for those keyphrases are crucial. This paper proposes an unsupervised region-based KDA technique for analyzing the distance of keyphrases from news articles as feature of keyphrase extraction. The proposed technique is divided into eight phases: data collection, data pre-processing, data processing, keyphrase searching, distance calculating, distance averaging, curve-plotting, and curve-fitting. At first, the proposed technique collects two different datasets that contain the news articles; it is then applied to the data pre-processing step that uses a few preprocessing algorithms. Then this pre-processing data is used in the data processing stage, where it is sent to the keyphrase searching step, the distance calculation process, and then the distance averaging steps. Curve plotting analysis is then applied, and finally the curve fitting technique is used. Afterwards, the performance of the proposed technique is put to test and evaluated using two of the most accessible benchmark datasets. The proposed method is then compared to other available methods in order to demonstrate its efficiency, advantages, and importance. Lastly, the results of the experiment demonstrated that the proposed approach efficiently analyzed the keyphrase distance from news articles, produced an F1-score of 96.91%, and presented keyphrases of 94.55%, as well as greatly improved the effectiveness of the current keyphrase extraction methods

An Analysis of QoS in ZigBee Network Based on Deviated Node Priority

ZigBee is an IEEE 802.15.4 standardized communication protocol. It forms a flawless Wireless Sensor Network (WSN) standard for interoperability at all levels of the network, particularly the application level which most closely touches the user. A large number of devices from different vendors can work seamlessly. These devices act as a network and send huge data traffic to the Coordinator. End devices at different zones have different roles in communication with each other. There has been a lack in executing their requests in a synchronized way based on task priority. This lack leads to massive data traffic loss and degrades the Quality of Service (QoS). One of the challenges is to analyze the QoS parameters in ZigBee network that help to detect the overall network performance. The contribution of this paper is twofold; first, a ZigBee Network is implemented based on node priority. It demonstrates a method to generate a new priority of devices with respect to their existing priority and zones’ priority as well. Second, the QoS is analyzed based on the new priority status for tasks preference purposes. The outcome of this paper shows that the QoS of the network is more conspicuous than non-priority based network