bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sentiment classification of delta robot trajectory control using word embedding and convolutional neural network
Authors: Iklima Zendi , Kadarina Trie Maya , Hajar Muhammad Hafidz Ibnu 
Published: April 2022

Sentiment classification (SC) is an important research field in natural language processing (NLP) <span lang="EN-US">that classifying, extracting and recognizing subjective information from unstructured text, including opinions, evaluations, emotions, and attitudes. Human-robot interaction (HRI) also involves natural language processing, knowledge representation, and reasoning by utilizing deep learning, cognitive science, and robotics. However, sentiment classification for HRI is rarely implemented, especially to navigate a robot using the Indonesian Language which semantically dynamics when written in text. This paper proposes a sentiment classification of Bahasa Indonesia that supports the delta robot to move in particular trajectory directions. Navigation commands of the delta robot were vectorized using a word embedding method containing two-dimensional matrices to propose the classifier pattern such as convolutional neural network (CNN). The result compared the particular architecture of CNN, GloVe-CNN, and Word2Vec-CNN. As a classifier method, CNN models trained, validated, and tested with higher accuracy are 98.97% and executed in less than a minute. The classifier produces four navigation labels: right means </span><em><span lang="EN-US">'kanan'</span></em><span lang="EN-US">, left means </span><em><span lang="EN-US">'kiri</span></em><span lang="EN-US">', top means </span><em><span lang="EN-US">'atas</span></em><span lang="EN-US">', bottom means </span><em><span lang="EN-US">'bawah</span></em><span lang="EN-US">', and multiplier factor. The classifier result is utilized to transform any navigation commands into direction along with end-effector coordinates.</span>
Spam detection by using machine learning based binary classifier
Authors: Abdul Kadir Mohd Fadzil , Abidin Ahmad Faisal Amri , Mohamed Mohamad Afendee , Hamid Nazirah Abdul 
Published: April 2022

<span lang="EN-US">Because <span lang="EN-US">of its ease of use and speed compared to other communication applications, email is the most commonly used communication application worldwide. However, a major drawback is its inability to detect whether mail content is either spam or ham. There is currently an increasing number of cases of stealing personal information or phishing activities via email. This project will discuss how machine learning can help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. A binary classifier will be used to classify the text into two different categories: spam and ham. This research shows the machine learning algorithm in the Azure-based platform predicts the score more accurately compared to the machine learning algorithm in visual studio, hybrid analysis and JoeSandbox cloud.</span></span>
Identifying significant elements of the digital transformation of organizatios in Kuwait
Authors: Alshehab Abdullah , Alfozan Thalaya , Gaderrab Hesham F. , Alahmad Mohammad Abdulateef , Alkandari Abdulrahman 
Published: April 2022

The adoption and <span>implementation of digital transformation (DT) plans and initiatives have become compulsory for organizations worldwide during this flourishing era of advanced technology. However, it is estimated that 70% of all DT initiatives do not achieve their goals, with billions of dollars going to waste. This study identified the main elements of digital transformation initiatives from the literature by scholars around the world and then applied these elements to organizations in Kuwait. 33 organizations (private and public) in Kuwait participated in a survey to investigate the level of readiness of organizations attempting to execute digital transformation plans and initiatives. Some insights were found: having a well-defined strategic vision in organizations, digital leadership, technical talent, and having digital expertise are significant elements in implementing digital transformation plans.</span>
Multilayer perceptron artificial neural networks-based model for credit card fraud detection
Authors: Kasasbeh Bassam , Aldabaybah Balqees , Ahmad Hadeel 
Published: April 2022

Nowadays, <span>credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.</span>
Convolutional neural network for the detection of coronavirus based on X-ray images
Authors: Ahmed Essam Hammodi , Alsemawi Majid Razaq Mohamed , Hasan Mutar Mohammed , Hanoosh Hatem Oday , Abbas Ali Hashem 
Published: April 2022

Nowadays, <span lang="EN-US">the coronavirus disease (COVID-19) is considered an ongoing pandemic that spread quickly in most countries around the world. The COVID-19 causes severe acute respiratory syndrome. Moreover, the technique of chest computed tomography (CT) is a method used in the detection of COVID-19. However, the CT method consumes more time and higher-cost as compared with chest X-ray images. Therefore, this paper presents convolutional neural network (CNN) algorithm in the detection of COVID-19 by using X-ray images. In this method, we have used a balanced image database for the normal (healthy) and COVID-19 subjects. The total number of image database is 188 samples (94 healthy samples and 94 COVID-19 samples). Furthermore, there are several evaluation measurements are used to evaluate the proposed model such as accuracy, precision, specificity, sensitivity, F-measure, G-mean, and others. According to the experimental results, the proposed model obtains 98.68% accuracy, 100% precision, and 100% specificity. Besides, the proposed model achieves 97.37%, 98.67%, and 98.68% for sensitivity, F-measure, and G-mean, respectively. The performance of the proposed model by using CNN algorithm shows promising results in the detection of COVID-19. Also, it has outperformed all its comparatives in terms of detection accuracy.</span>
Novel approach for semantic similarity cross ontology
Authors: Benaissa Kaddar Leila , Ben-Naoum Farah 
Published: April 2022

Measuring <span>semantic similarity between terms is a crucial step in information retrieval and integration since it necessitates semantic content matching. Even though several models have been proposed to measure semantic similarity, these models are not able to effectively quantify the weight of relevant items that affect the semantic similarity judgment process. In this study, we present a new method for measuring semantic similarity between cross-ontologies, that consists of hybridizing node-based approaches such as WuP and Reda with the weight of similarity computed using WordNet. The proposed approach has been experimented to show its efficiency with two ontologies, configuration management tool (CMT) and ConfOf, from the conference domaine in the web ontology language (OWL) ontologies benchmark OAEI 2015 and evaluated using two metrics: density and cohesion.</span>
Two phase secure data collection technique for wireless sensor networks
Authors: Thahniyath Gousia , Mishra Priti , Moorthy Sundar Raj 
Published: April 2022

Wireless <span>sensor networks (WSNs) are the sensors that are dispersed in a different location that can sense the accumulated data in real-time and send it to the central location for the process of data aggregation. During the transfer of the information using the data nodes from the WSNs to the central location, there may be chances that the data node could be compromised by sensor failure or by an attack from a malicious user. To overcome this problem, we propose a two-phase secure data collection (TPSDC) technique for wireless sensor networks which provides confidentiality and integrity for the data nodes that are being sent to the central location and also during the data aggregation in the central location. Various existing methods have been proposed to secure the data when sent from the WSNs to the internet of things (IoT) devices but they lack to provide both confidentiality and integrity at the same time. Hence our model provides both integrity and confidentiality by providing security to the data nodes. Experimental results show that our model TPSDC performs better in terms of misclassification rate, detection rate, throughput, network lifetime analysis of the node, and communication overhead of the node when compared with the existing methods.</span>
Turbo polar code based on soft-cancelation algorithm
Authors: Alebady Wallaa Yaseen , Hamad Ahmed Abdulkadhim 
Published: April 2022

Since the first polar code of Arikan, <span>the research field of polar codes has been continuously active. Improving the performance of finite-code-length polar codes is the central point of this field. In this paper, the parallel concatenated systematic turbo polar code (PCSTPC) model has been proposed to improve the polar codes performance in a finite-length regime. On the encoder side, two systematic polar encoders are used as constituent encoders. While on the decoder side, two single iteration soft-cancelation (SCAN) decoders are used as soft-in-soft-out (SISO) algorithms inside the iterative decoding algorithm of the parallel concatenated systematic turbo polar code (PCSTPC). As compared to the optimized turbo polar code with SCAN and BP decoders, the proposed model has about 0.2 dB and 0.48 dB gains at BER=10<sup>(-4)</sup>, respectively, in addition to 0.1 dB, 0.31 dB, and 0.72 dB gains over the TPC-SSCL32, TPC-SSCL16, and TPC-SSCL8 models, respectively. Moreover, the proposed model offers less complexity in comparison with other models, therefore requiring less memory and time resources.</span>
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue
Authors: Hussain Mashitah Mohd , Zakaria Zuhaina , Dahlan Nofri Yenita , Ilham Nur Iqtiyani , Hussin Zhafran , Rahman Noor Hasliza Abdul , Md Yasin Md Azwan 
Published: April 2022

This article aims <span lang="EN-US">to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved.<br /> A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles.</span>
Elastic net feature selected multivariate discriminant mapreduce classification
Authors: Nakkiran Arunadevi , Thulasiraman Vidyaa 
Published: April 2022

Analyzing the <span>big stream data and other valuable information is a significant task. Several conventional methods are designed to analyze the big stream data. But the scheduling accuracy and time complexity is a significant issue. To resolve, an elastic-net kernelized multivariate discriminant map reduce classification (EKMDMC) is introduced with the novelty of elastic-net regularization-based feature selection and kernelized multivariate fisher Discriminant MapReduce classifier. Initially, the EKMDMC technique executes the feature selection to improve the prediction accuracy using the Elastic-Net regularization method. Elastic-Net regularization method selects relevant features such as central processing unit (CPU) time, memory and bandwidth, energy based on regression function. After selecting relevant features, kernelized multivariate fisher discriminant mapr classifier is used to schedule the tasks to optimize the processing unit. Kernel function is used to find higher similarity of stream data tasks and mean of available classes. Experimental evaluation of proposed EKMDMC technique provides better performance in terms of resource aware predictive scheduling efficiency, false positive rate, scheduling time and memory consumption.</span>
“Ah Lai’s Crossing” – Malaysia’s first artificial road canopy bridge to facilitate safer arboreal wildlife crossings
Authors: Yap Jo Leen , Rosely Nik Fadzly Nik , Mahadzir Mazrul , Benedict Mark Louis , Muniandy Vikneswaran , Ruppert Nadine 
Published: April 2022

Abstract
Roads negatively affect wildlife populations globally, causing habitat fragmentation that can facilitate mortality by access to forest for poaching and wildlife collisions with vehicles. Primate habitats are vulnerable to fragmentation exposing them to anthropogenic threats. Endangered dusky langurs (Trachypithecus obscurus) in Malaysia are often found in urban areas and have been frequent victims of vehicle collisions. To mitigate the hazardous impact of a busy asphalt road on wild dusky langurs in Teluk Bahang, Penang, we built the first artificial road canopy bridge in Malaysia in February 2019 to assist arboreal wildlife to move between habitat fragments more safely. Four days after the bridge construction, the first long-tailed macaque (Macaca fascicularis) was recorded via a camera trap crossing the bridge, and plantain squirrels (Callosciurus notatus) and dusky langurs first crossed one and eight months later, respectively. The bridge was upgraded from a single-firehose rope to a double-rope in August 2020 to assess the effectiveness of the two designs. In total, 2128 animal crossings comprising three mammal species were recorded between March 2019 and May 2021. Plantain squirrels crossed the bridge most frequently with 2075 crossings, long-tailed macaques crossed 32 times, and dusky langurs 21 times. There was a significant difference in bridge use according to species between the two designs (Chi-Square, p < 0.001). Since the construction of the bridge, the rate of previously frequently reported primate roadkills near the bridge location has dropped to zero. This successful pilot project has spurred the efforts to implement more canopy bridges country-wide to support primate conservation efforts and better protect arboreal wildlife from the negative impacts of linear infrastructure.
Afterword: On Global Nation-States and Rooted Universalisms
Authors: Weller Robert P. 
Published: April 2022

How far can wings of belief carry us? What kinds of new social and moral systems evolve out of transnational religious movements? The recent literature on globalisation has shown only little interest in religion, in spite of the cutting-edge global role of beliefs like evangelical Christianity. The literature has attended even less to the movement of non-Western traditions. We do, however, have numerous alternative models of how global ideas in general spread: they can seep through the cracks within and between systems of control based in nation-states; they can flow through mobile cosmopolitans with a global sense of belonging; they can reside in diasporas centred around the idea of a common homeland; they can exist in the institutions of transnational villages; and so on.
These various images of global culture are not mutually exclusive, of course, and none of them alone captures the full complexity of the possibilities. Here I want to explore briefly just two dialectical relationships that characterise some of the features of the transnational Chinese religions discussed in the essays collected here. The first is the relationship between global culture and the continuing institutional power of the nation-state. The second is the counterintuitive combination of universalising beliefs with a strong sense of rootedness in specific places or ethnicities.
Liberal Nationalism in Substantive and Performative Perspectives
Authors: Lægaard Sune 
Published: April 2022

Abstract
This paper sketches some developments in the discussion of liberal nationalism since the early 1990s and proposes a generic understanding of nationalism according to which its main feature is the act of sorting people into members and non-members of the nation with a view to regulating access to political goods linked to the state. One discussion of liberal nationalism that has recently received renewed attention is the relation between nationalism and multiculturalism. Liberal nationalism sees nationalism as a response to increased diversity and involves normative demands on nationalism for accommodating this diversity. In light of the proposed generic understanding of nationalism, the question arises whether such a liberal nationalism is coherent. This question requires us to distinguish between a substantive and a performative perspective on nationalism, raising the possibility that liberal nationalism can be substantively coherent but performatively incoherent.
Inclusive Membership as Fairness? A Rawlsian Argument for Provisional Immigrants
Authors: Baycan-Herzog Esma 
Published: April 2022

Abstract
Infamously, Rawls assumed a democratic society to be “a complete and closed social system,” in that “entry into it is only by birth and exit from it is only by death.” Since the beginning of the present millennium, however, debates about the ethical issues related to immigration have been prominent. In this context, these methodological departure points seem long outdated, if not simply biased. This paper will rework Rawls’s theory of migration for application to the case of provisional immigrants by reworking its theoretical underpinnings. I will argue that once his assumptions are adjusted, Rawlsian notions of ‘stability’ in conjunction with his idea of a ‘society understood as a fair system of cooperation’ justify inclusive membership regimes. In other words, Rawlsian domestic justice requires inclusive membership regimes for provisional immigrants.
Varus-Tilted Distal Tibial Plafond Is a Risk Factor for Recurrent Ankle Instability After Arthroscopic Lateral Ankle Ligament Repair
Authors: Yoshimoto Kensei , Noguchi Masahiko , Maruki Hideyuki , Tominaga Ayako , Ishibashi Mina , Okazaki Ken 
Published: April 2022

Background: Although varus-tilted distal tibial deformity is an established risk factor for chronic lateral ankle instability (CLAI), no studies have reported whether this deformity influences ankle instability after arthroscopic lateral ankle ligament repair (ALLR) for CLAI. Methods: A total of 57 ankles from 57 patients who underwent ALLR for CLAI were retrospectively analyzed. Tibial articular surface (TAS) angles were measured on preoperative plain radiograph. After 12 months of follow-up, recurrent ankle instability and talar tilt angles on stress radiograph were evaluated as outcomes. Relationships between the TAS angle and these outcomes were assessed. Results: Recurrent ankle instability was observed in 10 ankles. The TAS angles of patients with recurrent instability were significantly lower (85.2 degrees vs 87.9 degrees). The receiver operating characteristic curve analysis revealed that the cutoff value of TAS angle for recurrent instability was 86.2 degrees. Based on this cutoff value, our patients were divided into 2 groups: low-TAS and high-TAS group. Univariate and multivariate analysis revealed that low TAS was an independent risk factor for recurrent ankle instability and greater postoperative talar tilt angles. Conclusion: Varus-tilted distal tibial plafond appears to be a risk factor for recurrent ankle instability after ALLR.
Short-term eye movement desensitization and reprocessing (EMDR) therapy to treat children with posttraumatic stress symptoms after single trauma: A case series
Authors: Lempertz Daniela , Vasileva Mira , Brandstetter Luise , Bering Robert , Metzner Franka 
Published: April 2022

Background Traumatic experiences can lead to posttraumatic stress disorder (PTSD). For young children, even minor, inconspicuous looking events can lead to posttraumatic stress symptoms. Trauma-focused treatment with Eye Movement Desensitization and Reprocessing (EMDR) offers children an age-adapted intervention to help them successfully process traumatic experiences. So far, there has been a lack of well evaluated trauma-focused treatments for young children. Methods These case series examine the effectiveness of a short-term treatment with EMDR therapy for children showing PTSD symptoms after experiencing a single incident induced trauma. Five children between 5 and 10 years of age who developed PTSD after a single incident trauma received a manualized EMDR treatment for 6 weeks (mean number of sessions: seven including a mean number of EMDR sessions: 3.4). Posttraumatic stress symptoms were assessed prior to treatment, following treatment and at a 3-month follow-up with standardized instruments for caregivers and children. Results PTSD symptoms decreased for all children after completing the treatment from clinical to non-clinical level. Reductions in vegetative hyperarousal, fears and clinging behaviour were achieved. Furthermore, reductions in the parental stress levels, as well as a recovery of everyday routine and everyday stability were observed. Conclusion Short-term EMDR treatment appears to be a promising treatment for single incident trauma in young children providing a potentially successful quick and safe psychological treatment for children. Results contribute to the knowledge of feasibility and acceptability of short-term, trauma-focused treatments of children with EMDR. Replication of the results of these case series in larger samples using a randomized controlled design is warranted.