Showing papers in "International Journal of Advanced Computer Science and Applications in 2021"

An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Abstract: Bangladesh and India are significant paddy-cultivation countries in the globe Paddy is the key producing crop in Bangladesh In the last 11 years, the part of agriculture in Bangladesh's Gross Domestic Product (GDP) was contributing about 1508 percent But unfortunately, the farmers who are working so hard to grow this crop, have to face huge losses because of crop damages caused by various diseases of paddy There are approximately more than 30 diseases of paddy leaf and among them, about 7-8 diseases are quite common in Bangladesh Paddy leaf diseases like Brown Spot Disease, Blast Disease, Bacterial Leaf Blight, etc are very well known and most affecting one among different paddy leaf diseases These diseases are hampering the growth and productivity of paddy plants which can lead to great ecological and economical losses If these diseases can be detected at an early stage with great accuracy and in a short time, then the damages to the crops can be greatly reduced and the losses of the farmers can be prevented This paper has worked on 4 types of diseases and one healthy leaf class of the paddy The main goal of this paper is to provide the best results for paddy leaf disease detection through an automated detection approach with the deep learning CNN models that can achieve the highest accuracy instead of the traditional lengthy manual disease detection process where the accuracy is also greatly questionable It has analyzed four models such as VGG-19, Inception-Resnet-V2, ResNet-101, Xception, and achieved better accuracy from Inception-ResNet-V2 is 9268%

Fake News Detection in Arabic Tweets during the COVID-19 Pandemic

Abstract: In March 2020, the World Health Organization declared the COVID-19 outbreak to be a pandemic. Soon afterwards, people began sharing millions of posts on social media without considering their reliability and truthfulness. While there has been extensive research on COVID-19 in the English language, there is a lack of research on the subject in Arabic. In this paper, we address the problem of detecting fake news surrounding COVID-19 in Arabic tweets. We collected more than seven million Arabic tweets related to the corona virus pandemic from January 2020 to August 2020 using the trending hashtags during the time of pandemic. We relied on two fact-checkers: the France-Press Agency and the Saudi Anti-Rumors Authority to extract a list of keywords related to the misinformation and fake news topics. A small corpus was extracted from the collected tweets and manually annotated into fake or genuine classes. We used a set of features extracted from tweet contents to train a set of machine learning classifiers. The manually annotated corpus was used as a baseline to build a system for automatically detecting fake news from Arabic text. Classification of the manually annotated dataset achieved an F1-score of 87.8% using Logistic Regression (LR) as a classifier with the n-gram-level Term Frequency-Inverse Document Frequency (TF-IDF) as a feature, and a 93.3% F1-score on the automatically annotated dataset using the same classifier with count vector feature. The introduced system and datasets could help governments, decision-makers, and the public judge the credibility of information published on social media during the COVID-19 pandemic. © 2021. All Rights Reserved.

Fake Reviews Detection using Supervised Machine Learning

Abstract: With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviors of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviors of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews with and without features extracted from users behaviors. In both cases, we compare the performance of several classifiers; KNN, Naive Bayes (NB), SVM, Logistic Regression and Random forest. Also, different language models of n-gram in particular bi-gram and tri-gram are taken into considerations during the evaluations. The results reveal that KNN(K=7) outperforms the rest of classifiers in terms of f-score achieving best f-score 82.40%. The results show that the f-score has increased by 3.80%when taking the extracted reviewers behavioral features into consideration.

Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak

Abstract: The COVID-19 pandemic, is also known as the coronavirus pandemic, is an ongoing serious global problem all over the world The outbreak first came to light in December 2019 in Wuhan, China This was declared pandemic by the World Health Organization on 11th March 2020 COVID-19 virus infected on people and killed hundreds of thousands of people in the United States, Brazil, Russia, India and several other countries Since this pandemic continues to affect millions of lives, and a number of countries have resorted to either partial or full lockdown People took social media platforms to share their emotions, and opinions during this lockdown to find a way to relax and calm down In this research work, sentiment analysis on the tweets of people from top ten infected countries has been conducted The experiments have been conducted on the collected data related to the tweets of people from top ten infected countries with the addition of one more country chosen from Gulf region, i e Sultanate of Oman A dataset of more than 50,000 tweets with hashtags like #covid-19, #COVID19, #CORONAVIRUS, #CORONA, # StayHomeStaySafe, #Stay Home, #Covid_19, #CovidPandemic, #covid19, #Corona Virus, #Lockdown, #Qurantine, #qurantine, #Coronavirus Outbreak, #COVID etc posted between June 21, 2020 till July 20, 2020 was considered in this research Based on the tweets posted in English a sentiment analysis was performed This research was conducted to understand how people from different infected countries cope with the situation The tweets were collected, pre-processed and then text mining algorithms used and finally sentiment analysis have been done and presented with the results The purpose of this research paper to know about the sentiments of people from COVID-19 infected countries

Blockchain in Insurance: Exploratory Analysis of Prospects and Threats

Abstract: Ever since the first generation of blockchain technology became very successful and the FinTech enormously benefited from it with the advent of cryptocurrency, the second and third generations championed by Ethereum and Hyperledger have explored the extension of blockchain in other domains like IoT, supply chain management, healthcare, business, privacy, and data management. A field as huge as the insurance industry has been underrepresented in literature. Therefore, this paper presents how investments in blockchain technology can profit the insurance industry. We discuss the basics of blockchain technology, popular platforms in use today, and provide a simple theoretical explanation of the insurance sub-processes which blockchain can mutate positively. We also discuss hurdles to be crossed to fully implement blockchain solutions in the insurance domain.

Security, Privacy and Trust in IoMT Enabled Smart Healthcare System: A Systematic Review of Current and Future Trends

Abstract: In the past decades, healthcare has witnessed a swift transformation from traditional specialist/hospital centric approach to a patient-centric approach especially in the smart healthcare system (SHS). This rapid transformation is fueled on account of the advancements in numerous technologies. Amongst these technologies, the Internet of medicals things (IoMT) play an imperative function in the development of SHS with regard to productivity of electronic devices in addition to reliability, accuracy. Recently, several researchers have shown interest to leverage the benefits of IoMT for the development of SHS by interconnecting with the existing healthcare services and available medical resources. Though the integration of IoMT within medical resources enable to revolutionize the patient healthcare service from reactive to proactive care system, the security of IoMT is still in its infancy. As IoMT are mainly employed to capture extremely sensitive individual health data, the security and privacy of IoMT is of paramount importance and very crucial in safeguarding the patient life which could otherwise adversely affect the patient health state and in worse case may also lead to loss of life. Motivated by this crucial requirement, several researchers in tandem to the advancement in IoMT technologies have continuously made noteworthy progress to tackle the security and privacy issues in IoMT. Yet, many possible potential directions exist for future investigation. This necessitates for a complete overview of existing security and privacy solutions in the field of IoMT. Therefore, this paper aims to canvass the literature on the most promising state-of-the-art solutions for securing IoMT in SHS especially in the light of security, privacy protection, authentication and authorization and the use of blockchain for secure data sharing. Finally, highlights the review outcome briefing not only the benefits and limitation of existing security and privacy solutions but also summarizing the opportunities and possible potential future directions that can drive the researchers of next decade to improve and shape their research committed on safe integration IoMT in SHS.

Topic based Sentiment Analysis for COVID-19 Tweets

Abstract: The incessant Coronavirus pandemic has had a detrimental impact on nations across the globe The essence of this research is to demystify the social media's sentiments regarding Coronavirus The paper specifically focuses on twitter and extracts the most discussed topics during and after the first wave of the Coronavirus pandemic The extraction was based on a dataset of English tweets pertinent to COVID-19 The research study focuses on two main periods with the first period starting from March 01,2020 to April 30, 2020 and the second period starting from September 01,2020 to October 31, 2020 The Latent Dirichlet Allocation (LDA) was adopted for topics extraction whereas a lexicon based approach was adopted for sentiment analysis In regards to implementation, the paper utilized spark platform with Python to enhance speed and efficiency of analyzing and processing large-scale social data The research findings revealed the appearance of conflicting topics throughout the two Coronavirus pandemic periods Besides, the expectations and interests of all individuals regarding the various topics were well represented © 2021 All rights reserved

Integrating Cost-231 Multiwall Propagation and Adaptive Data Rate Method for Access Point Placement Recommendation

Abstract: A new approach has been developed to provide an overview about signal behavior in indoor environments using Cost-231 Multiwall Model (Cost-231 MWM) and Adaptive Data Rate (ADR) method. This approach used as a reference for access point (AP) placement for campus building. The Cost-231 MWM plays a role in estimating the measured power received by user (usually called as Received Signal Strength Indicator/RSSI) by considering the existence of obstacles around the transmitter (AP). We used Institut Asia Malang environments as the case study and gave some recommendations for AP placement: ten optimal placements for the first, third and fourth floor, also seven optimal placements for the second floor. These recommendations were based on the RSSI for good and excellent level signal (-50 dBm until -10dBm). This research also uses the Adaptive Data Rate (ADR) mechanism approach to reduce the amount of packet loss (kbps) resulting from obstacles that cause attenuation (-dB). With the Adaptive Data Rate mechanism, it means increasing the number of access points, the signal attenuation (-dB) occurs from the obstacles (Walls) that are penetrated by the Radio Frequency device and causes attenuation (-dB), the more Access points on the Multi-Wall, will allow communication and data transmitting stability.

Optimal Allocation of DG and D-STATCOM in a Distribution System using Evolutionary based Bat Algorithm

Abstract: In this work, a methodology to find the optimal allocation (i.e., sizing and location) of Distributed Generators (DGs) and Distribution-static compensators (D-STATCOM) in a radial distribution system (RDS) is proposed. Here, the voltage stability index (VSI) is utilized to find the optimal location for the D-STATCOM, and loss sensitivity factor (LSF) method is utilized to find the optimal location for distributed generation. In this work, the proposed work is formulated as a non-linear optimization problem and it is solved using the meta-heuristic/evolutionary-based algorithm. The evolutionary-based Bat algorithm is used to find optimal sizes of D-STATCOM and DGs in RDSs. To check the validity and feasibility and validity of the proposed optimal allocation approach, two standard IEEE 34 and 85 bus RDSs are considered in this paper. The simulation results show reduction in power losses and enhancement in bus voltages in the RDSs.

Fungal Blast Disease Detection in Rice Seed using Machine Learning

Abstract: The economy of Pakistan mainly relies upon agriculture alongside other vital industries. Fungal blast is one of the significant plant diseases found in rice crops, leading to reduction of agricultural products and hindrance in the country's economic development. Plant disease detection is an initial step towards improving the yield and quality of agricultural products. Manual Analyzation of plant health is tiresome, time taking and costly. Machine learning offers an alternate inspection method providing benefits of automated inspection, ease of availability, and cost reduction. The visual patterns on the rice plants are processed using the machine learning classifiers such as support vector machine (SVM), logistic regression, decision tree, Naive Bayes, random forest, linear discriminant analysis (LDA), principal component analysis (PCA), and based on classification results plants are recognized as healthy or unhealthy. For this process, a dataset containing 1000 images of rice seed crop is collected from different fields of Kashmore, and whole analysis of image acquisition, pre-processing, and feature extraction is done on the rice seed only. The dataset is annotated with healthy and unhealthy samples with the help of a plant disease expert. The algorithms used for processing data are evaluated in terms of F1-score and testing accuracy. This paper contains results from traditional classifiers, and alongside these classifiers, transfer learning has been used to compare the results. Finally, a comparative analysis is done between the results of traditional classifiers and deep learning networks.

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Abstract: Artificial Intelligence in legal research is transforming the legal area in manifold ways. Pendency of court cases is a long-lasting problem in the judiciary due to various reasons such as lack of judges, lack of technology in legal services and the legal loopholes. The judicial system has to be more competent and more reliable in providing justice on time. One of the major causes of pending cases is the lack of legal intelligence to assist the litigants. The study in this paper reviews the challenges faced by judgment prediction system due to lengthy case facts using deep learning model. The Legal Judgment prediction system can help lawyers, judges and civilians to predict the win or loss rate, punishment term and applicable law articles for new cases. Besides, the paper reviews current encoding and decoding architecture with attention mechanism of transformer model that can be used for Legal Judgment Prediction system. Natural Language Processing using deep learning is an exploring field and there is a need for research to evaluate the current state of the art at the intersection of good text processing and feature representation with a deep learning model. This paper aims to develop a systematic review of existing methods used in the legal judgment prediction system and about the Hierarchical Attention Neural network model in detail. This can also be used in other applications such as legal document classification, sentimental analysis, news classification, text translation, medical reports and so on. Keywords—Legal judgment prediction; hierarchical attention neural network; text processing; transformer

An Integrated Imbalanced Learning and Deep Neural Network Model for Insider Threat Detection

Abstract: The insider threat is a vital security problem concern in both the private and public sectors. A lot of approaches available for detecting and mitigating insider threats. However, the implementation of an effective system for insider threats detection is still a challenging task. In previous work, the Machine Learning (ML) technique was proposed in the insider threats detection domain since it has a promising solution for a better detection mechanism. Nonetheless, the (ML) techniques could be biased and less accurate when the dataset used is hugely imbalanced. Therefore, in this article, an integrated insider threat detection is named (AD-DNN), which is an integration of adaptive synthetic technique (ADASYN) sampling approach and deep neural network technique (DNN). In the proposed model (AD-DNN), the adaptive synthetic (ADASYN) is used to solve the imbalanced data issue and the deep neural network (DNN) for insider threat detection. The proposed model uses the CERT dataset for the evaluation process. The experimental results show that the proposed integrated model improves the overall detection performance of insider threats. A significant impact on the accuracy performance brings a better solution in the proposed model compared with the current insider threats detection system.

The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem

Abstract: In most conditions, it is a problematic mission for a machine-learning model with a data record, which has various attributes, to be trained. There is always a proportional relationship between the increase of model features and the arrival to the overfitting of the susceptible model. That observation occurred since not all the characteristics are always important. For example, some features could only cause the data to be noisier. Dimensionality reduction techniques are used to overcome this matter. This paper presents a detailed comparative study of nine dimensionality reduction methods. These methods are missing-values ratio, low variance filter, high-correlation filter, random forest, principal component analysis, linear discriminant analysis, backward feature elimination, forward feature construction, and rough set theory. The effects of used methods on both training and testing performance were compared with two different datasets and applied to three different models. These models are, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest classifier (RFC). The results proved that the RFC model was able to achieve the dimensionality reduction via limiting the overfitting crisis. The introduced RFC model showed a general progress in both accuracy and efficiency against compared approaches. The results revealed that dimensionality reduction could minimize the overfitting process while holding the performance so near to or better than the original one.

Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process

Abstract: Now-a-days, Cybersecurity attacks are becoming increasingly sophisticated and presenting a growing threat to individuals, private and public sectors, especially the Denial Of Service attack (DOS) and its variant Distributed Denial Of Service (DDOS). Dealing with these dangerous threats by using traditional mitigation solutions suffers from several limits and performance issues. To overcome these limitations, Machine Learning (ML) has become one of the key techniques to enrich, complement and enhance the traditional security experiences. In this context, we focus on one of the key processes that improve and optimize Machine Learning DOS-DDOS predicting models: DOS-DDOS feature selection process, particularly the wrapper process. By studying different DOS-DDOS datasets, algorithms and results of several research projects, we have reviewed and evaluated the impact on used wrapper strategies, number of DOS-DDOS features, and many commonly used metrics to evaluate DOS-DDOS prediction models based on the optimized DOS-DDOS features. In this paper, we present three important dashboards that are essential to understand the performance of three wrapper strategies commonly used in DOS-DDOS ML systems: heuristic search algorithms, meta-heuristic search and random search methods. Based on this review and evaluation study, we can observe some of wrapper strategies, algorithms, DOS-DDOS features with a relevant impact can be selected to improve the DOS-DDOS ML existing solutions.

Arabic Sign Language Recognition using Faster R-CNN

Abstract: Deafness does not restrict its negative effect on the person’s hearing, but rather on all aspect of their daily life. Moreover, hearing people aggravated the issue through their reluctance to learn sign language. This resulted in a constant need for human translators to assist deaf person which represents a real obstacle for their social life. Therefore, automatic sign language translation emerged as an urgent need for the community. The availability and the widespread use of mobile phones equipped with digital cameras promoted the design of image-based Arabic Sign Language (ArSL) recognition systems. In this work, we introduce a new ArSL recognition system that is able to localize and recognize the alphabet of the Arabic sign language using a Faster Region-based Convolutional Neural Network (R-CNN). Specifically, faster R-CNN is designed to extract and map the image features, and learn the position of the hand in a given image. Additionally, the proposed approach alleviates both challenges; the choice of the relevant features used to encode the sign visual descriptors, and the segmentation task intended to determine the hand region. For the implementation and the assessment of the proposed Faster R-CNN based sign recognition system, we exploited VGG-16 and ResNet-18 models, and we collected a real ArSL image dataset. The proposed approach yielded 93% accuracy and confirmed the robustness of the proposed model against drastic background variations in the captured scenes.

IoT Soil Monitoring based on LoRa Module for Oil Palm Plantation

Abstract: Internet of Things (IoT) Soil Monitoring based on Low Range (LoRa) Module for Palm Oil Plantation is a prototype that sends data from the sender to the receiver by using LoRa technology. This realises the implementation of Industrial Revolution 4.0 in the agriculture sector. Also, this prototype uses the TTGO development board for Arduino with built-in ESP32 and LoRa, pH sensor and moisture level sensor as main components. The prototype utilises the LoRa communication between the sender and the receiver. The sensors will detect soil pH along with the moisture level. The data then will be sent to the receiver, where it will be displayed in the Organic Light-Emitting Diodes (OLED) display. At the same time, the data will be uploaded to the database named ThingSpeak by using wireless communication. Users can monitor the data collected by accessing ThingSpeak's website using smartphones or laptops. The prototype is easy to set up and use to help users monitor the pH level and moisture level percentage. For future enhancement, the project can be enhanced by combining temperature and tilt sensors to get comprehensive data about the soil’s condition.

Impact of Mobile Applications for a Lima University in Pandemic

Abstract: The current global pandemic situation has forced universities to opt for distance education, relying on digital tools that are currently available, such as course management platforms like Moodle, videoconferencing applications like Google Meet or Zoom, or instant messaging apps like WhatsApp. In this study it is detailed that these tools have made virtual education an effective alternative to provide education without having a physical space where teachers and students can concentrate. In addition, this document shows that in this form of teaching learning it is not necessary to have a computer, it is enough to have a cell phone to access this type of education in Peru, since most of the country’s homes have a smartphone . Both students and teachers affirm that, although a little more time is invested than usual, this teaching method is satisfactory. The result obtained is that the use of mobile applications plays a very important role in virtual classes since the vast majority of students use the cell phone. In conclusion, teaching and learning in higher university education with the use of mobile applications, both teachers and students said that it was of great help due to the interaction through communication with WhatsApp, zoom, Google meet, among others. In addition, being in constant communication with the students through the applications strengthened the teaching.

Comprehensive Survey and Research Directions on Blockchain IoT Access Control

Abstract: The Internet of Things (IoT) is a widely used technology in the last decade in different applications. The Internet of things is wirelessly or wired to communicate, store, compute and track various real-time scenarios. This survey mainly discussed the core problems of Internet of things security and access control to unauthorized users and security requirements for IoT. The Internet of things is a heterogeneous device and has low memory, less processing power because of the small sizes. Nowadays, IoT systems are not sure and powerless to protect themselves against cyber attacks. It is mainly due to inadequate space in IoT gadgets, immature standards, and the lack of protected hardware and software design, development, and deployment. To meet IoT requirements, the authors discussed the limitations of traditional access control. Then the authors examined the potential to spread access control by implementing the safe architecture accommodated by the Blockchain. The authors also addressed how to use the Blockchain to work with and resolve some of the standards relevant to IoT security issues. In the end, an analysis of this survey shows future, open-ended problems, and challenges. It offers how the Blockchain potentially ensures reliable, scalable, and more efficient security solutions for IoT and further research work.

Analysis of the Use of Videoconferencing in the Learning Process During the Pandemic at a University in Lima

Abstract: Due to the health emergency situation, which forced universities to stop using their centers as a means of teaching, many of them opted for virtual education. Affecting the learning process of students, which has predisposed many of them to become familiar with this new learning process, making the use of virtual platforms more common. Many educational centers have come to rely on digital tools such as: Discord, Google Meet, Microsoft Team, Skype and Zoom. The objective of the research is to report on the impact of student learning through the use of the aforementioned videoconferencing tools. Surveys were conducted with teachers and students who stated that 66% were not affected in their educational development. Most of them became familiar with the platforms; however, less than 24% qualified that their academic performance has improved, some teachers still have difficulties at a psychological level due to this new teaching modality. In conclusion, teachers and students agree that these tools are a great help for virtual classes.

A Hybridized Deep Learning Method for Bengali Image Captioning

Abstract: An omnipresent challenging research topic in com-puter vision is the generation of captions from an input image. Previously, numerous experiments have been conducted on image captioning in English but the generation of the caption from the image in Bengali is still sparse and in need of more refining. Only a few papers till now have worked on image captioning in Bengali. Hence, we proffer a standard strategy for Bengali image caption generation on two different sizes of the Flickr8k dataset and BanglaLekha dataset which is the only publicly available Bengali dataset for image captioning. Afterward, the Bengali captions of our model were compared with Bengali captions generated by other researchers using different architectures. Additionally, we employed a hybrid approach based on InceptionResnetV2 or Xception as Convolution Neural Network and Bidirectional Long Short-Term Memory or Bidirectional Gated Recurrent Unit on two Bengali datasets. Furthermore, a different combination of word embedding was also adapted. Lastly, the performance was evaluated using Bilingual Evaluation Understudy and proved that the proposed model indeed performed better for the Bengali dataset consisting of 4000 images and the BanglaLekha dataset.

Text Coherence Analysis based on Misspelling Oblivious Word Embeddings and Deep Neural Network

Abstract: Text coherence analysis is the most challenging task in Natural Language Processing (NLP) than other subfields of NLP, such as text generation, translation, or text summarization. There are many text coherence methods in NLP, most of them are graph-based or entity-based text coherence methods for short text documents. However, for long text documents, the existing methods perform low accuracy results which is the biggest challenge in text coherence analysis in both English and Bengali. This is because existing methods do not consider misspelled words in a sentence and cannot accurately assess text coherence. In this paper, a text coherence analysis method has been proposed based on the Misspelling Oblivious Word Embedding Model (MOEM) and deep neural network. The MOEM model replaces all misspelled words with the correct words and captures the interaction between different sentences by calculating their matches using word embedding. Then, the deep neural network architecture is used to train and test the model. This study examines two different types of datasets, one in Bengali and the other in English, to analyze text consistency based on sentence sequence activities and to evaluate the effectiveness of this model. In the Bengali language dataset, 7121 Bengali text documents have been used where 5696 (80%) documents have been used for training and 1425 (20%) documents for testing. And in the English language dataset, 6000 (80%) documents have been used for training and 1500 (20%) documents for model evaluation out of 7500 text documents. The efficiency of the proposed model is compared with existing text coherence analysis techniques. Experimental results show that the proposed model significantly improves automatic text coherence detection with 98.1% accuracy in English and 89.67% accuracy in Bengali. Finally, comparisons with other existing text coherence models of the proposed model are shown for both English and Bengali datasets.

Personalized Book Recommendation System using Machine Learning Algorithm

Abstract: As the amounts of online books are exponentially increasing due to COVID-19 pandemic, finding relevant books from a vast e-book space becomes a tremendous challenge for online users Personal recommendation systems have been emerged to conduct effective search which mine related books based on user rating and interest Most of these existing systems are user-based ratings where content-based and collaborativebased learning methods are used These systems' irrationality is their rating technique, which counts the users who have already been unsubscribed from the services and no longer rate books This paper proposed an effective system for recommending books for online users that rated a book using the clustering method and then found a similarity of that book to suggest a new book The proposed system used the K-means Cosine Distance function to measure distance and Cosine Similarity function to find Similarity between the book clusters Sensitivity, Specificity, and F Score were calculated for ten different datasets The average Specificity was higher than sensitivity, which means that the classifier could re-move boring books from the reader's list Besides, a receiver operating characteristic curve was plotted to find a graphical view of the classifiers' accuracy Most of the datasets were close to the ideal diagonal classifier line and far from the worst classifier line The result concludes that recommendations, based on a particular book, are more accurately effective than a user-based recommendation system © 2021, International Journal of Advanced Computer Science and Applications All Rights Recerved

Implementation of an e-Commerce System for the Automation and Improvement of Commercial Management at a Business Level

Abstract: At present micro and small businesses engaged in the production and marketing of products and have a single means of sale, whether stalls or physical stores, have been affected by the current crisis that is happening due to the pandemic, which came in early 2020 to Europe and different countries in Latin America, which is causing terrible damage to the economy of enterprises, since it does not have a means of virtual sales, where they can offer and market their products so that trade continues to operate during the pandemic In this way, we designed a prototype e-commerce system meeting the requirements required by the organizations Where it was based on the Scrum methodology as an agile development framework for the realization of the project The use of the Marvel design tool allowed the creation of web platform prototypes Obtaining as a result, prototypes according to an e-commerce system complying with the development procedures established by the Scrum team, which gives you a novel proposal and a productive approach to start implementing e-commerce within the sales processes of each business area Therefore, this e-commerce system prototype proposal can be implemented by the different micro companies that wish to have a new online sales method and improve their commercial area process, allowing the increase of their client portfolio, as well as their production

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Abstract: In recent years, the Industrial Internet of things (IIoT) is a fastest advancing innovative technology with a poten-tial to digitize and interconnect many industries for huge business opportunities and development of global GDP. IIoT is used in diverse range of industries such as manufacturing, logistics, transportation, oil and gas, mining and metals, energy utilities and aviation. Although IIoT provides promising opportunities for the development of different industrial applications, they are prone to cyberattacks and demands for higher security require-ments. The enormous number of sensors present in the IIoT network generates a large amount of data and has attracted the attention of cybercriminals across globe. The intrusion detection system (IDS) that monitors the network traffic and detects the behaviour of the network is considered as one of the key security solution for securing IIoT application from attacks. Recently, the application of machine and deep learning techniques have proved to mitigate multiple security threats and enhance the performance of intrusion detection. In this paper, we present a survey of deep learning-based IDS technique for IIoT. The main objective of this research is to provide the various deep learning-based IDS detection methods, datasets and comwparative analysis. Finally, this research aims to identify the limitations and challenges of existing studies, solutions and future directions.

Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018

Abstract: Jordan is suffering a chronicle water resources shortage. Rainfall is the real input for all water resources in the country. Acceptable accuracy of rainfall prediction is of great importance in order to manage water resources and climate change issues. The actual study include the analysis of time series trends of climate change regards to rainfall parameter. Available rainfall data for five stations from central Jordan where obtained from the Ministry of water and irrigation that cover the interval 1938- 2018. Data have been analyzed using Nonlinear Autoregressive Artificial Neural Networks NAR-ANN) based on Levenberg-Marquardt algorithm. The NAR model tested the rainfall data using one input layer, one hidden layer and one output layer with a different combinations of number of neuron in hidden layer and epochs. The best combination was using 25 neurons and 12 epochs. The classification performance or the quality of result is measured by mean square error (MSE). For all the meteorological stations, the MSE values were negligible ranging between 4.32*10-4 and 1.83*10-5. The rainfall prediction result show that forecasting rainfall values in the base of calendar year are almost identical with those estimated for seasonal year when dealing with long record of years. The average predicted rainfall values for the coming ten-year in comparison with long-term rainfall average show; strong decline for Dana station, some decrees for Rashadia station, huge increase in Abur station, and relatively limited change between predicted and long-term average for Busira and Muhai Stations.

A Review on Feature Selection and Ensemble Techniques for Intrusion Detection System

Abstract: Intrusion detection has drawn considerable interest as researchers endeavor to produce efficient models that offer high detection accuracy. Nevertheless, the challenge remains in developing reliable and efficient Intrusion Detection System (IDS) that is capable of handling large amounts of data, with trends evolving in real-time circumstances. The design of such a system relies on the detection methods used, particularly the feature selection techniques and machine learning algorithms used. Thus motivated, this paper presents a review on feature selection and ensemble techniques used in anomaly-based IDS research. Dimensionality reduction methods are reviewed, followed by the categorization of feature selection techniques to illustrate their effectiveness on training phase and detection. Selection of the most relevant features in data has been proven to increase the efficiency of detection in terms of accuracy and computational efficiency, hence its important role in the design of an anomaly-based IDS. We then analyze and discuss a variety of IDS-based machine learning techniques with various detection models (single classifier-based or ensemble-based), to illustrate their significance and success in the intrusion detection area. Besides supervised and unsupervised learning methods in machine learning, ensemble methods combine several base models to produce one optimal predictive model and improve accuracy performance of IDS. The review consequently focuses on ensemble techniques employed in anomaly-based IDS models and illustrates how their use improves the performance of the anomaly-based IDS models. Finally, the paper laments on open issues in the area and offers research trends to be considered by researchers in designing efficient anomaly-based IDSs.

Energy Storage and Electric Vehicles: Technology, Operation, Challenges, and Cost-Benefit Analysis

Abstract: With ever-increasing oil prices and concerns for the natural environment, there is a fast-growing interest in electric vehicles (EVs) and renewable energy resources (RERs), and they play an important role in a gradual transition. However, energy storage is the weak point of EVs that delays their progress. The world’s EV industry is accelerating to faster adoption with appropriate incentives to the EV owners, policy support, and encouraging local manufacturing. The increasing demand for EV’s has presented itself as an authentic alternative to internal combustion engines (ICE). The main feature of the RERs is their variability and intermittency. These drawbacks are overcome by integrating more than one renewable energy source including backup sources and storage systems. This paper presents various technologies, operations, challenges, and cost-benefit analysis of energy storage systems and EVs.

Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

Abstract: Since the number of healthy people is much more than that of ill people, it is highly likely that the problem of imbalanced data will occur when predicting the depression of the elderly living in the community using big data When raw data are directly analyzed without using supplementary techniques such as a sample algorithm for datasets, which have imbalanced class ratios, it can decrease the performance of machine learning by causing prediction errors in the analysis process Therefore, it is necessary to use a data sampling technique for overcoming this imbalanced data issue As a result, this study tried to identify an effective way for processing imbalanced data to develop ensemble-based machine learning by comparing the performance of sampling methods using the depression data of the elderly living in South Korean communities, which had quite imbalanced class ratios This study developed a model for predicting the depression of the elderly living in the community using a logistic regression model, gradient boosting machine (GBM), and random forest, and compared the accuracy, sensitivity, and specificity of them to evaluate the prediction performance of them This study analyzed 4,085 elderly people (≥60 years old) living in the community The depression data of the elderly in the community used in this study had an unbalance issue: the result of the depression screening test showed that 875% of subjects did not have depression, while 125% of them had depression This study used oversampling, undersampling, and SMOTE methods to overcome the unbalance problem of the binary dataset, and the prediction performance (accuracy, sensitivity, and specificity) of each sampling method was compared The results of this study confirmed that the SMOTE-based random forest algorithm showing the highest accuracy (a sensitivity ≥ 06 and a specificity ≥ 06) was best prediction performance among random forest, GBM, and logistic regression analysis Further studies are needed to compare the accuracy of SMOTE, undersampling, and oversampling for imbalanced data with high dimensional y-variables