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Neighbor Discovery Protocol (NDP) is a network protocol used in IPv6 networks to manage communication between neighboring devices. NDP is responsible for mapping IPv6 addresses to MAC addresses and discovering the availability of neighboring devices on the network. The main risk of deploying NDP on public networks is the potential for hackers or attackers to launch various types of attacks, such as address spoofing attacks, denial-of-service attacks, and man-in-the-middle attacks. Although Secure Neighbor Discovery (SEND) is implemented to secure NDP, its complexity and cost hinder its widespread deployment. This research emphasizes the potential hazard of deploying IPv6 networks in public spaces, such as airports, without protecting NDP messages. These risks have the potential to crash the entire local network. To demonstrate these risks, the GNS3 testbed environment is used to generate NDP attacks and capture the resulting packets using Wireshark for analysis. The analysis results reveal that with just a few commands, attackers can execute various NDP attacks. This highlights the need to protect against the potential issues that come with deploying IPv6 on widely accessible public networks. In addition, the analysis result shows that NDP attacks have behavior that can be used to define various NDP attacks.

A water system is a technique for giving water to plants to grow and create. Water in plants or water systems is not a cutting-edge idea; it has been followed and applied from one century to another or decade to decade. Advances have developed in lockstep with time. At the point when we compare the 1980s and 2021, we see a sensational distinction in advances just as in the water system strategy. During the 1980s, the water system was accomplished utilizing furrows and wrinkles. A water system is a technique for providing water to the root zone at the ideal opportunity. Plants draw water from wet soil, and by the component of evapotranspiration (ET), plants come to pass water into the air while drawing supplements accessible in soil with water for root zone improvement. At the point when the stockpile of water in the root zone falls under a specific edge, plants can't draw supplements and water for sustenance. Thus, supply great water to the root zone before as far as possible is reached. This cutoff is controlled by the types of plants, soil, and environment. Since the edge cap changes relying upon the sort of plant. Logical booking requires the utilization of the proper volume of water at the fitting time frame and area inside the office. This requires consistent checking of soil dampness content at the root zone and the commencement of the water system as per a pre-modified timetable base on wear the idea of the plant, its turn of events, soil type, and environment. Accordingly, for logical water system booking, sensors should be planted close to the root zone in the dirt to get to the agent's dampness status. The signs created and recognized by soil dampness sensors should be prepared in a microcontroller for water system planning by predefined programming. The microcontroller ought to likewise be intended to hand off the sign to a far-off area where the siphoning and well framework for water system control is found. Also, the criticism of these sensors ought to be examined by the microcontroller as indicated by a foreordained program to end the water system relying upon the plant, its the degree of improvement, soil, and environment. This paper presents a cloud-based and web of things-based water system framework. This framework utilizes sensors to gather continuous water system information, stores information into the cloud, proprietor of information provides the order and suitable move is made on dependent on the outcome created.

Feature selection (FS) is the process of finding the least possible number of features that are able to describe a dataset in the same way as the original features. Feature selection is a crucial preprocessing step for data mining techniques as it improves the performance of the prediction process in terms of speed and accuracy and also provides a better understanding of stored data. The success of the FS process depends on achieving a balance between two important factors, namely selecting the minimal number of features and maintaining the maximum accuracy in the results. In this paper, two methods are proposed to improve the FS process. Firstly, the mine blast algorithm (MBA) is introduced to optimize the FS process in the exploration phase. Secondly, the MBA is hybridized with simulated annealing as a local search in the exploitation phase to enhance the solutions located by the MBA. The proposed approaches (MBA and MBA–SA) are tested on 18 benchmark datasets from the UCI repository, and the comprehensive experimental results indicate that MBA–SA achieved good performance when compared with five approaches in the literature.

The ongoing growth in the vast amount of digital documents and other data in the Arabic language available online has increased the need for classification methods that can deal with the complex nature of such data. The classification of Arabic plays a large and important role in many modern applications and interferes with other sciences, which start from search engines and do not end with the Internet of Things. However, addressing the Arab classification errors with high performance is largely insufficient to deal with the huge quantities to reveal the classification of Arab documents; while some work was tackled out on the classification of the Arabic text, most of the research has focused on English text. The methods proposed for English are not suitable for Arabic as the morphology of the two languages differs substantially. Moreover, morphologically, the preprocessing of Arabic text is a particularly challenging task. In this study, three commonly used classification algorithms, namely, the K-nearest neighbor, Naïve Bayes, and decision tree, were implemented for Arabic text in order to assess their effectiveness with and without the use of a light stemmer in the preprocessing phase. In the experiment, a dataset from Agency France Persse (AFP) Arabic Newswire 2001 consisting of four categories and 800 files was classified using the three classifiers. The result showed that the decision tree with light stemmer had the best accuracy rate for classification algorithm with 93%.

Arabic is one of the Semitic languages in antiquity and one of the six official languages of the UN. Also, Arabic classification plays a significant and essential role in modern applications. There is a big difference between handling English text and Arabic text classification; preprocessing is also challenging for Arabic text. This paper presents the implementation of a Naive Bayes classifier for Arabic text with and without stemmer. A set of four categories and 800 documents were used from the Text Retrieval Conference (TREC) 2001 dataset. The results showed that Naive Bayes with light stemmer achieves better results than Naive Bayes without stemmer. The findings of the classifier accuracy by employing stemmer and without stemmer are as preprocessing. It reveals that the accuracy resulted from the light stemmer was better than the classifier without stemmer detection, which Naive Bayes Classification with light stemmer got 35.0745 higher than the Naive Bayes Classification 33.831% without stemmer. After contrasting them, the stemmer got better accuracy than the classifier.

A wide number of reports and news on crimes, increasingly conducted almost every day, have resulted in making detection of such crimes more difficult if not complex. Therefore, the need for detecting and identifying such crimes emerges as a necessary way of detecting and identifying such crime patterns on the news. Document Clustering have been increasingly becoming an important task for obtaining good results with the unsupervised learning methods. It aims to automatically group similar documents in one cluster using different types of extractions and cluster algorithms. There are ongoing works done to improve Document Clustering techniques such as Extractions and Clustering approaches to overcome the difficulty in designing a general purpose document clustering for crime investigation and the ill posed problem of extraction and clustering. This paper discusses two major sequential stages in Document Clustering “Extraction Features and Clustering Algorithms” as well as the major challenges and the key issues in designing extraction features and clustering algorithms. In addition, the following approach assists the law enforcement officers and detectives to enhance performance and speed up the process of solving crimes.

Clustering of Arabic documents is considered as a vital aspect ‎of obtaining optimal results from unsupervised learning. ‎Its aim ‎is to automatically group similar documents into a single cluster ‎using different similarities or distance measures. ‎However, ‎diverse similarities and distance measures are available and their ‎effectiveness in document clustering with a ‎syntactic structure ‎of the stemming is still not obvious. Therefore,‎‏ this study aims to evaluate the impact of five ‎similarity/distance measures (i.e., cosine similarity, the Jaccard coefficient, Pearson’s correlation coefficient, Euclidean ‎distance, and averaged Kullback-Leibler divergence) with two stemming algorithms (i.e., morphology- and syntax-based ‎lemmatization; and morphology-based Information Science Research Institute (ISRI) stemming on clustering Arabic ‎text dataset. We aim to identify the best performing similarity and distance measures and determine which measure is ‎most suitable for Arabic document clustering. Our experimental method, which is based on syntactic structure and ‎morphology, outperformed other stemming methods that use any of the five similarity/distance measures for Arabic ‎document clustering. The best performing similarity/distance measures are cosine similarity and Euclidean distance‎, respectively.

Stemming is a process of reducing inflected words to their stem, stem or root from a generally written word form. One of the high inflected words in the languages world is Arabic Language. Stemming improve the retrieval performance by reducing words variants, and in lcrease the similarity between related words. However, an Arabic Information Retrieval (AIR) can use stemming algorithms to retrieve a greater number of documents related to the users’ query. Therefore, the aim of this paper is to evaluate the impact of three different Arabic stemmers (i.e. ‘Information Science Research Institute” (ISRI), morphological and syntax based lemmatization “Educated Text Stemmer” (ETS), and Light10 stemmer) on the Arabic Information Retrieval performance for Arabic language, we used the Linguistic Data Consortium (LDC) Arabic Newswire data set as benchmark dataset. The evaluation of the three different stemmers ranked the best performance was achieved by light10 stemmer in term of mean average precision.

Arabic Document Clustering has increasingly become an important task for obtaining good results with the unsupervised learning task. This paper aims to evaluate the impact of the five measures (Cosine similarity, Jaccard coefficient, Pearson correlation, Euclidean distance and Averaged Kullback- Leibler Divergence) for Document Clustering with two types of pre-processing morphology-based The Information Science Research Institute (ISRI) is equivalent to the root-based stemmer and light stemmer; and without stemming without morphology) for an Arabic dataset. Stemming is known as a computational process used to reduce words to their stems. For classification, it is categorised as a recall-enhancing or precision-enhancing component. It is concluded that the method of ISRI for words is proved to be better than without stemming methods which use a five similarities/distance measures for Document Clustering.