In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant (...) number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use. (shrink)

Knearest neighbor search is an important problem in location-based services and has been well studied on static road networks. However, in real world, road networks are often time-dependent; i.e., the time for traveling through a road always changes over time. Most existing methods forkNN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time-dependent road networks because the shortest distances always change over time. (...) To address the problem ofkNN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Furthermore, we propose a novel balanced tree, namedV-tree, which is a secondary level index on voronoi-based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time-dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real-life datasets. (shrink)

With the Hegel Lecture 2010, held by Homi K. Bhabha, the Dahlem Humanities Center is launching the Open Access publication of the series. In his talk, Bhabha evokes the spirit of Hegel in an attempt to understand contemporary issues of ethical witness, historical memory and the rights and representations of minorities in the cultural sphere. Who is our neighbour today? What does hospitality mean for our times? Why is the recognition of others such an agonizing encounter with the alterity of (...) the self? The lecture examplifies how the “Third Space” - one of the key theories of Postcolonialism - helps us to establish a new understanding of cosmopolitanism and hospitality in a globalized world, based on the right of difference in equality. (shrink)

Level statistics and nodal point distribution in a rectangular semiconductor quantum dot are studied for different degrees of spin-orbit coupling. The chaotic features occurring from the spin-orbit coupling have no classical counterpart. Using experimental values for GaSb/InAs/GaSb semiconductor quantum wells we find that level repulsion can lead to the semi-Poisson distribution for nearest level separations. Nodal lines and nodal points are also investigated. Comparison is made with nodal point distributions for fully chaotic states.

In this book, Daniel K. Miller articulates a new vision of human and animal relationships based on the foundational love ethic within Christianity. Framed around Jesus' parable of the Good Samaritan, Animal Ethics and Theologythoughtfully examines the shortcomings of utilitarian and rights-based approaches to animal ethics. By considering the question of animals within the Christian concept of neighbourly love, Miller provides an alternative narrative for understanding the complex relationships that humans have with other animals. This book addresses significant theological questions (...) such as: Does being created in the image of God present a meaningful distinction between humans and other animals? What does it mean for humans to have dominion over animals? Is meat eating a moral problem for Christians? In addition to drawing out the significance of Christian theology for field of animal ethics this book also engages environmental and feminist ethics. Miller brings a theological perspective to such questions as: Should care for animals be distinguished from care for the environment, and what role should human emotions play in our ethical dealings with other animals? As the title suggests, this book provides fresh insight into the theological significance of human relationships with other animals. mental and feminist ethics. Miller brings a theological perspective to such questions as: Should care for animals be distinguished from care for the environment, and what role should human emotions play in our ethical dealings with other animals? As the title suggests, this book provides fresh insight into the theological significance of human relationships with other animals. (shrink)

This paper offers a non-traditional account of what was really at stake in debates over the legitimacy of teleology and teleological explanations in the later medieval and early modern periods. It is divided into four main sections. The first section highlights two defining features of ancient and early medieval views on teleology, namely, that teleological explanations are on a par (or better) with efficient causal explanations, and that the objective goodness of outcomes may explain their coming about. The second section (...) argues that it was the first thesis – the thesis of explanatory parity –that came under attack by mainstream philosophers in the later medieval and early modern traditions, rather than teleology per se. The third section argues similarly that it was the second thesis – the thesis of explanatory goodness – that was the object of Spinoza’s critique of final causes, rather than teleology itself. Finally, the fourth section argues that Leibniz’s claim to be a champion of traditional teleology rests on his willingness to defend both the theses of explanatory parity and goodness against the very charges that led to their demise. The essay concludes by contrasting the narrative offered here with Don Garrett’s recent account according to which “it is not Leibniz but Spinoza who holds the position on teleology and teleological explanation nearest to that of Aristotle” (312). (shrink)

In a recent review article, Jeff Popke (2006, p. 510) calls for a ‘more direct engagement with theories of ethics and responsibility’ on the part of human geographers, and for a reinscription of the social as a site of ethics and responsibility. This requires that we also continue to develop ways of thinking through our responsibilities toward unseen others—both unseen neighbours and distant others—and to cultivate a renewed sense of social interconnectedness. Popke suggests that a feminist-inspired ethic of care might (...) be instrumental in developing this expanded, relational and collective vision of the social, which is particularly prescient given the contemporary economic downturn throughout the globe. Thus, as the ‘moral turn’ in geography continues to evolve, this special issue seeks to bring together geographers working within feminist or feminist-inspired frameworks, and with a shared interest in the changing geographies of ethics, responsibility and care. The collection of papers has its origins in conference sessions on Care-full Geographies, organised by the Guest Editors at the Annual Meeting of the Association of American Geographers in 2007. In this editorial we seek to position the papers within broader debates about care, responsibility and ethics that have emerged in geography and the wider social sciences in recent years, and to highlight the key issues that have framed these debates. (shrink)

Ethical theories consistent with Christian moral sensibilities must assure each neighbor's dignity and recognize his or her unique needs. While utilitarian ethics and duty-based ethics may fail to some degree in these respects, virtue ethics offers perspectives that echo more faithfully the parable of the Good Samaritan.

In lieu of an abstract, here is a brief excerpt of the content:Whisper Before You GoJohn K PettyDavid came with a bang.1A momentary prelude from a dysphonic chorus of pagers announce “Level 1 Pediatric Trauma—MVC ejected” before the abrupt crescendo of the trauma bay doors opening. He is maybe two. Maybe three–years–old. It is hard to tell when a child is strapped in, strapped down, nonverbal, intubated, and alone.The flight team speaks for him, “Four–year–old boy improperly restrained in a single–vehicle (...) crash into a ditch. He was partially ejected. He was unresponsive and pulseless at the scene. He got CPR and code drugs, and they got a pulse back. Emergency medical service (EMS) took him to the nearest hospital where they needled his chest, called for air transport, and scanned him up. He has been shocky for us since we picked him up. Glasgow Coma Scale (GCS) has been three the whole time. They said his head scan showed subarachnoid hemorrhage.”With the words of the flight team as background narrative, we get about doing our thing—hopefully more method and less madness. Bang, bang, bang! Check the tube. Vital signs. IV fluids. Place a chest tube. Hang the blood. Quick X–ray. Neuro exam—nothing. Get him off the board. Warm him up. Place the orogastric (OG) tube. Bang, bang, bang!The neurosurgery resident opens the CT scans from the other hospital. The brain and cervical spine scans show a disaster: some swelling and bleeding in the higher brain, but the worst damage is in the lower brain, brainstem, and top of the spinal cord. I have heard a neurosurgeon describe this pattern before as “internal decapitation.” Such language never makes it into the patient’s chart. For David the last two words of the neurosurgery consult note will eventually read, “Prognosis grim.”He remains unstable in the trauma bay. Inaction is death. We ultrasound his heart, abdomen, and pelvis. He has fluid that shouldn’t be around his heart. He is dying of the neuro injury we cannot control, but the pericardial fluid might be giving him shock, and we can control that. We can’t ignore the fluid. Shouldn’t ignore it. At least, wouldn’t ignore it. So, we bang up to the operating room. Squeezing blood. Spiking fluid. Oxygen tank and crash cart. Up we go.In the operating room, we do much and we do well. We release the fluid around the heart and find it was CPR, not heart injury that put it there. In the pericardial flash we get a view of the cardiac athlete, pounding like the 26th mile of the marathon. We explore the abdomen and find it swollen but innocent of an injury that would take his life. We place our lines and tubes as the neurosurgeons place theirs. If nothing else, we will be able to measure what lies ahead.“Good work, team,” and we bump the door on the way to the pediatric intensive care unit (PICU).The care in the PICU is more Apache brave than Gaelic knight—more bottles, fewer scissors. The curtains and carpets mute the tones. Our efforts continue, but I get a word that David’s father has arrived. He is sitting in a small, private waiting room off of the main PICU waiting room. The PICU staff colloquially refer to this as “the bad news room.” I take a seat next to a solitary man who is having the worst day of his life. His worst day so far.“I am Dr. Petty, and I am one of the doctors who is taking care of your son. I am very sorry about what has happened. Maybe you can tell me a little bit about what you understand is going on, and I’ll tell you what I know.” [End Page 17]He tells me that his wife was driving David back from her sister’s house, just the two of them. He doesn’t know what happened, but he got a call from the other hospital about the crash. His wife was badly hurt. He heard that David had died, but they got him back. Both were transferred up to the... (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Christian Ethics: A Very Short Introduction, and: Christian Ethics: A Brief History, and: Behaving in Public: How to Do Christian EthicsBeth K. HaileChristian Ethics: A Very Short Introduction D. Stephen Long Oxford: Oxford University Press, 2010. 144 pp. $11.95Christian Ethics: A Brief History Michael Banner West Sussex, UK: Wiley-Blackwell, 2009. 160 pp. $24.95Behaving in Public: How to Do Christian Ethics Nigel Biggar Grand Rapids, Mich.: Eerdmans, 2011. 142 (...) pp. $16.00In calling ethics “Christian,” the question of distinctiveness, or the normative force of theological claims, inevitably arises. Three new books explore what Christian ethics looks like if it is to take seriously its theological basis for engaging the world.Stephen D. Long, professor of systematic theology at Marquette University, attends to the difficulties in predicating “Christian” of “ethics,” yet he is concerned more with explaining how Christianity can be ethical rather than vice versa. His Christian Ethics: A Very Short Introduction opens with a quote from Christopher Hitchens and later dedicates almost an entire chapter to analyzing the failures of Christian ethics. From the Crusades to colonialism, the Galileo affair to slavery, Long provocatively asks whether modern ethics emerges as a result of the failure of Christian moral claims. While not sidestepping the difficulties in Christian history, Long is careful to note how this history is often distorted to serve the goals of contemporary secular politics, goals which he points out have resulted in the bloody failures of the twentieth century. Perhaps a renewed interest in Christian ethics, Long notes, is a result of the failures of secularism.Long manages to cover a surprising breadth of material in his brief book, from a sweeping historical overview of Christian theology to a final chapter on [End Page 195] the practical matters of sex, money, and political power. Long also summarizes some important theological differences among Christians that lead to practical disagreements about the nature of Christian ethics. His overview of the Catholic-Lutheran convergence on matters of faith and works is particularly helpful in establishing the theological basis for an ecumenical Christian ethics. This historical survey serves to illustrate that ethics is both an integral part of and a contradiction to Christianity. Still, argues Long, while Christianity may claim to be more than ethical, it may never claim to be less.While commendable in breadth, the genre of the “very short introduction” precludes depth and must necessarily omit important information. In his summary of Catholic ethics, for example, Long addresses nominalism and probabilism but omits the Catholic social tradition, a particularly glaring omission given his topic. In the end, the target audience is the educated and curious layperson, more suitable for a church group than a classroom, though this is reflective of the nature of “the very short introduction” series and does not indicate any limitation in Long’s skill as a scholar.In the spirit of brevity, Michael Banner, Fellow of Trinity College in Cambridge, offers Christian Ethics: A Brief History, which, like any brief history, paints with broad and admittedly selective strokes. The aim of this work is not to evaluate the normative value of Christian claims but rather to establish the ethical implications of Christian belief. Unlike Long, who directly addresses the ethical failures of the church, Banner is more apologetic. In clarifying the difficulties of writing a text such as this one, he observes that Christian history has not been guided by texts as much as by action. In this vein, Banner begins not chronologically but with the Rule of St. Benedict and its call to live in a relationship with God and neighbor characterized by love.Banner grounds the theoretical dimension of Christian ethics in this emphasis on practice. Given the way of life Christians are called to, what are its limitations and possibilities? Banner turns to Augustine’s notion of the weakness of the will and the human creature’s need for grace, which he then places in creative tension with Aquinas’s emphasis on the harmony between faith and reason. In somewhat breathtaking conciseness, Banner describes how the Scholastic emphasis on natural law, casuistry, and the categorizing of sins led to what he calls... (shrink)

Apoptosis is now widely recognized as a common form of cell death and represents a mechanism of cell clearance in many physiological situations where deletion of cells is required. Peptide growth factors, initially characterised as stimulators of cell proliferation, have now been shown to inhibit death in many cell types. Deprivation of growth factors leads to the induction of apoptosis, i.e. condensation of chromatin and degradation in oligonucleosomesized fragments, formation of plasma and nuclear membrane blebs and cell fragmentation into apoptotic (...) bodies which can be taken up by neighbouring cells. Here we discuss the mechanism(;s) by which growth factors may inhibit apoptosis. (shrink)

The author enters an already old dispute, that is, whether a countеrpart of the notion of philosophy could be encountered in the traditional India, upholds the view that the term ānvīkṣikī (lit. “investigation”) was nearest to it and traces its meaning along the texts on dharma, politics, poetics and philosophy properly. Two main avenues to the understanding of philosophy’s vocations in India have been paved in the Mānavadharmaśāstra, along with the commentaries thereon and by Kamandaki, the author of the (...) Nītisāra (as the knowledge of Ātman) and in the Arthaśāstra and the Nyāya texts composed by Vātsyāyana and Uddyotakara (as a metascience helping the other branches of knowledge bear their fruits). Therefore philosophy in India as well was regarded as the duality of ideological and methodological constituents, while the emphasis on analytic practice in the definitions of ānvīkṣikī (Wittgenstein’s conception of philosophy as a practice is also referred to in this context) paves a good promise for comparative philosophy. (shrink)

This book provides a history of the ethnic persecution of the Rohingyas in Myanmar and their disputed ethnic and national identity. It focuses on how the crisis has morphed into a geopolitical encounter among Bangladesh, China, India, and Myanmar. It further explores the moral, ethnographic, and public policy issues in the humanitarian response to the crisis of the Rohingya people. The volume analyzes the question of citizenship for the Rohingyas by analyzing historical documents and interviews which chronicle the status and (...) identity of the community and their past involvement in the government and politics of Myanmar. The authors focus specifically on the changing geopolitical context of state formation in South Asia and the tense relationships between Myanmar and its neighbours - Bangladesh, China, and India. The book examines the alliances and disputes in the South and Southeast Asia region, which are predicated on economic and strategic gains, and their impact on the Rohingya crisis. It also looks at the failure of bilateral and multilateral negotiations among these countries to adequately address or alleviate the plight of the stateless Rohingyas. This volume will be of great interest to scholars and researchers of international studies, peace, human rights and conflict studies, sociology, ethnic studies, border studies, migration and diaspora studies, discrimination and exclusion studies, public policy, and Asian Studies. It will also be useful for professionals working in the media, nongovernmental organizations, think tanks, and policy makers, as well as general readers interested in the history of the persecution of the Rohingya people. (shrink)

Should we as religious ethicists seek to understand what traditions other than our own or our own area of scholarship have to say about particular ethical issues as knowledge parallel to our own, or can we allow what we learn to make a difference in our own ethical positions? Writing from the perspective of Christian ethics, the author argues that we can indeed allow what we learn to inform Christian ethical evaluations and to shape ethical conclusions, with neighbor love (...) and concern for the common good as grounds for doing this. Using forms of commitments to political nonviolence as a test case, the author first reviews major positions on war and peace ethics within Christianity, Judaism, and Islam, and then explores how Jewish and Muslim ethical assessments can inform a choice between Christian absolute pacifism and practical pacifism. (shrink)

With the development of mobile communication technology, location-based services are booming prosperously. Meanwhile privacy protection has become the main obstacle for the further development of LBS. The k-nearest neighbor search is one of the most common types of LBS. In this paper, we propose an efficient private circular query protocol with high accuracy rate and low computation and communication cost. We adopt the Moore curve to convert two-dimensional spatial data into one-dimensional sequence and encrypt the points of interest (...) information with the Brakerski-Gentry-Vaikuntanathan homomorphic encryption scheme for privacy-preserving. The proposed scheme performs the secret circular shift of the encrypted POIs information to hide the location of the user without a trusted third party. To reduce the computation and communication cost, we dynamically divide the table of the POIs information according to the value of k. Experiments show that the proposed scheme provides high accuracy query results while maintaining low computation and communication cost. (shrink)

Culture, defined as shared behavior or information within a community acquired through some form of social learning from conspecifics, is now suggested to act as a second inheritance system. Cultural processes are important in a wide variety of vertebrate species. Birdsong provides a classic example of cultural processes: cultural transmission, where changes in a shared song are learned from surrounding conspecifics, and cultural evolution, where the patterns of songs change through time. This form of cultural transmission of information has features (...) that are different in speed and form from genetic transmission. More recently, culture, vocal traditions and an extreme form of song evolution has been documented in cetaceans. Humpback whale song ‘revolutions’, where the single population-wide shared song type is rapidly replaced by a new, novel song type introduced from a neighbouring population, represents an extraordinary example of ocean basin-wide cultural transmission rivalled in its geographic extent only by humans. In this review, we examine the cultural evolutions and revolutions present in some birdsong and whale song, respectively. By taking a comparative approach to these cultural processes, we review the existing evidence to understand the similarities and differences for their patterns of expression and the underlying drivers, including anthropogenic influences, which may shape them. Finally, we encourage future studies to explore the role of innovation vs. production errors in song evolution, the fitness information present in song, and how human-induced changes in population sizes, trajectories and migratory connections facilitating cultural transmission may be driving song revolutions. (shrink)

The importance of the Low Countries as a centre for the study of foreign languages is well-known. The mutual relationship between the Dutch grammatical tradition and the Western European context has, however, been largely neglected. In this collection of papers on the history of linguistics in the Low Countries the editors have made an effort to present the Dutch tradition in connection with that of the neighbouring countries. Three articles by Claes, Dibbets and Klifman deal with the earliest stages of (...) the development of a grammar for the Dutch vernacular. Several important European figures worked in the Low Countries; their contribution to linguistics is discussed in articles on Vossius (Rademaker), Spinoza (Klijnsmit), and one of the most original phoneticians of European linguistics, Montanus (Hulsker). Vivian Salmon's article is a survey on the relations between English and Dutch linguistics in the field of foreign language teaching. In the 19th century Dutch linguistics had a special relationship with German general and historical linguistics; four articles deal with this period (Jongeneelen, van Driel, le Loux-Schuringa, Noordegraaf). Finally, there are three articles by Kaldewij, Hagen and van Els/Knops on the development of three branches of linguistics in the 20th century: structuralism, dialectology and applied linguistics. This volume should be of interest for all specialists in the history of linguistics in Europe, who are interested in the interdependence of the various traditions. (shrink)

Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an (...) extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency. (shrink)

Social media contain abundant information about the events or news occurring all over the world. Social media growth has a greater impact on various domains like marketing, e-commerce, health care, e-governance, and politics, etc. Currently, Twitter was developed as one of the social media platforms, and now, it is one of the most popular social media platforms. There are 1 billion user’s profiles and millions of active users, who post tweets daily. In this research, buzz detection in social media was (...) carried out by the semantic approach using the condensed nearest neighbor (SACNN). The Twitter and Tom’s Hardware data are stored in the UC Irvine Machine Learning Repository, and this dataset is used in this research for outlier detection. The min–max normalization technique is applied to the social media dataset, and additionally, missing values were replaced by the normalized value. The condensed nearest neighbor (CNN) is used for semantic analysis of the database, and based on the optimized value provided by the proposed method, the threshold is calculated. The threshold value is used to classify buzz and non-buzz discussions in the social media database. The result showed that the SACNN achieved 99% of accuracy, and relative error is less than the existing methods. (shrink)

We have developed a new method for Japanese-to-English translation of tense, aspect, and modality that uses an example-based method. In this method the similarity between input and example sentences is defined as the degree of semantic matching between the expressions at the ends of the sentences. Our method also uses the k-nearest neighbor method in order to exclude the effects of noise; for example, wrongly tagged data in the bilingual corpora. Experiments show that our method can translate tenses, (...) aspects, and modalities more accurately than the top-level MT software currently available on the market can. Moreover, it does not require hand-craft rules. (shrink)

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit (...) was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. (shrink)

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it (...) is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation. (shrink)

In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim (...) of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases. (shrink)

With the development of communication and computer technology, the application of big data technology has become increasingly widespread. Reasonable, effective, and fast retrieval methods for querying information from massive data have become an important content of current research. This article provides an image retrieval method based on the weighted nearest neighbor label prediction for the problem of automatic image annotation and keyword image retrieval. In order to improve the performance of the test method, scientific experimental verification was implemented. (...) The nearest neighbor weights are determined by maximizing the training image annotation, and experiments are carried out from multiple angles based on the Mahalanobis metric learning integration model. The experimental results show that the proposed tag correlation prediction propagation model has obvious improvements in accuracy, recall rate, break-even point, and overall average accuracy performance compared with other widely used algorithm models. (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (...) and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam (...) product reviews is a challenging issue in NLP. Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron, K-Nearest Neighbour, and Random Forest and predicts the outcome of the review as spam or real, based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers and state-of-the-art boosting approaches like Generalized Boost Regression Model, Extreme Gradient Boost, and AdaBoost Regression Model in terms of classification accuracy. (shrink)

Impostor syndrome or Impostor phenomenon is a belief that a person thinks their success is due to luck or external factors, not their abilities. This psychological trait is present in certain groups like women. In this paper, we propose a neutrosophic trait measure to represent the psychological concept of the trait-anti trait using refined neutrosophic sets. This study analysed a group of 200 undergraduate students for impostor syndrome, perfectionism, introversion and self-esteem: after the COVID pandemic break in 2021. Data labelling (...) was carried out using these neutrosophic trait measures. Machine learning models like Support Vector Machine(SVM), K-nearest neighbour (K-NN), and random forest were used to model the data; SVM provided the best accuracy of 92.15%. (shrink)

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of (...) 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine, adaptive boosting, random forest, and K-nearest neighbor models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination, Nash–Sutcliffe coefficient, and error in the root mean square ratio to the standard deviation of the measured data. The results demonstrated that the XGBoost model has the highest prediction performance with, followed by the SVM model with, RF, the AdaBoost model with and the KNN model with. A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable. (shrink)

Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting (...) data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further. (shrink)

In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to (...) draw predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the (...) analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and (...) user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours, decision rules, decomposition tree, and local transfer function classifier for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem. (shrink)

In order to obtain continuous stratum information during TBM tunneling, using TBM tunneling parameters, stratum recognition is carried out through the K-nearest neighbor model, and the model is improved by the entropy weight method to improve the stratum recognition rate. By analyzing the correlation between TBM tunneling characteristic parameters and stratum, the tunneling characteristic parameter vector which is most sensitive to the stratum is obtained by sensitivity analysis, and the stratum recognition model based on the K-nearest (...) class='Hi'>neighbor algorithm is established. Aiming at the problem that the model has a large error in complex formation recognition, a formation recognition model based on the entropy weight K-nearest neighbor algorithm is established, and the wrong data of the K-nearest neighbor model is recalculated. The recognition rate of the stratum in the new model is increased from 90.95% to 98.55%. The results show that the K-nearest neighbor model has a better recognition effect for the interval with single stratum distribution, and the recognition rate of entropy weight K-nearest neighbor model for complex stratum is significantly improved, which provides an effective method to obtain stratum information by using tunneling characteristic parameters. (shrink)

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number of (...) irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research. (shrink)

This paper presents an approach for monitoring exercises of hand rehabilitation for post stroke patients. The developed solution uses a leap motion controller as hand-tracking device and embeds a supervised machine learning. The K-nearest neighbor methodology is adopted for automatically characterizing the physiotherapist or helper hand movement resulting a unique movement pattern that constitutes the basis of the rehabilitation process. In the second stage, an evaluation of the patients rehabilitation exercises results is compared to the movement pattern of (...) the patient and results are presented, saved and statistically analyzed. Physicians and physiotherapists monitor and assess patients’ rehabilitation improvements through a web application, furthermore, offer medical assisted rehabilitation processes through low cost technology, which can be easily exploited at home. Recorded tracked motion data and results can be used for further medical study and evaluating rehabilitation trends according to patient’s rehabilitation practice and improvement. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree, K (...) class='Hi'>nearest neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning and deep learning models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized (...) artificial neural network, have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score. (shrink)

This paper presents an approach for monitoring exercises of hand rehabilitation for post stroke patients. The developed solution uses a leap motion controller as hand-tracking device and embeds a supervised machine learning. The K-nearest neighbor methodology is adopted for automatically characterizing the physiotherapist or helper hand movement resulting a unique movement pattern that constitutes the basis of the rehabilitation process. In the second stage, an evaluation of the patients rehabilitation exercises results is compared to the movement pattern of (...) the patient and results are presented, saved and statistically analyzed. Physicians and physiotherapists monitor and assess patients’ rehabilitation improvements through a web application, furthermore, offer medical assisted rehabilitation processes through low cost technology, which can be easily exploited at home. Recorded tracked motion data and results can be used for further medical study and evaluating rehabilitation trends according to patient’s rehabilitation practice and improvement. (shrink)

To effectively identify network rumors and block their spread, this paper uses fractal theory to analyze a network rumor spreading situation time series, reveal its inner regularity, extract features, and establish a network rumor recognition model. The model is based on an empirical mode decomposition correlation dimension and K-nearest neighbor approach. Firstly, a partition function is used to determine if the time series of the rumor spreading situation is a uniform fractal process. Secondly, the rumor spreading situation is (...) subjected to EMD to obtain a series of intrinsic mode functions, construct the IMF1–IMF6 components containing effective feature information as the principal components, and reconstruct the phase space of the principal components, respectively. Finally, the correlation dimensions of the principal components IMF1–IMF6 as obtained by the Grassberger-Procaccia algorithm are used as feature parameters and are imported into the KNN model for rumor recognition. The experimental results show that the correlation dimension of a spreading situation can better reflect the characteristic information; as combined with the KNN model for identifying rumors, the recognition rate reaches 87.5%. This result verifies the effectiveness of fractal theory in network rumors recognition, expands the thinking for the research of rumors recognition, and provides theoretical support for rumor governance. (shrink)

Parkinson’s Disease can be treated with the use of microelectrode recording and stimulation. This paper presents a data stream classifier that analyses raw data from micro-electrodes and decides whether the measurements were taken from the subthalamic nucleus (STN) or not. The novelty of the proposed approach is based on the fact that distances based on raw data are used. Two distances are investigated in this paper, i.e. Normalized Compression Distance (NCD) and Lempel-Ziv Jaccard Distance (LZJD). No new features needed to (...) be extracted due to the fact that in the case of high-dimensional data the process is extremely time-consuming. The k-nearest neighbour (k-NN) was chosen as the classifier due to its simplicity, which is essential in data stream classification. Results obtained from classifiers based on k-NN: k-NN, k-NN were compared with Probabilistic Approximate Window (k-NN with PAW); k-NN with Probabilistic Approximate Window and Adaptive Windowing (k-NN with PAW and ADWIN); and Self Adjusting Memory k-NN (SAM k-NN), which use the proposed distances, with the performance of the same classifiers but using standard Euclidean distance. Prequential accuracy was chosen as the performance measure. The results of the experiments performed with the described approach are in most cases better, i.e. the performance measures for kNN classifiers that use NCD and LZJD distances are better by up to 8.5 per cent and 14 per cent, respectively. Moreover, the proposed approach performs better when compared with other stream classification algorithms, i.e. Hoeffding Tree, Naive Bayes, and Leveraging Bagging. In the discussed case, an improvement of classification rate of up to 17.9 per cent when using Lempel-Ziv Jaccard Distance instead of the Euclidean was noted. (shrink)

Neurodegenerative Parkinson's Disease is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite and intelligent Motor Assessment Tests, produced within (...) the i-PROGNOSIS European project, are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 and Stage 3 produced from their interaction with PGS/iMAT are analyzed. Five feature vector scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor, Support Vector Machines, and Random Forest, to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation method is adopted for testing the classification performance. The experimental results show that a high classification accuracy is achieved from both data sources, justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions. (shrink)

The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National (...) Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases. (shrink)

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work (...) presents that Social Media Analysis can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree, with the help of Term Frequency and Inverse Document Frequency. For experimental analysis, two datasets are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques. (shrink)

With the rapid development of science and technology, UAVs have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects (...) of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm, Gradient Boosting Decision Tree algorithm, K Nearest Neighbor algorithm, and Decision Tree algorithm. Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield. (shrink)