Computer Science

In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian (...) register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics Platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu. (shrink)

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...) of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe. (shrink)

Ranking theory is one of the salient formal representations of doxastic states. It differs from others in being able to represent belief in a proposition (= taking it to be true), to also represent degrees of belief (i.e. beliefs as more or less firm), and thus to generally account for the dynamics of these beliefs. It does so on the basis of fundamental and compelling rationality postulates and is hence one way of explicating the rational structure of doxastic states. Thereby (...) it provides foundations for accounts of defeasible or nonmonotonic reasoning. It has widespread applications in philosophy, it proves to be most useful in Artificial Intelligence, and it has started to find applications as a model of reasoning in psychology. (shrink)

In this paper, we argue that computer simulations can provide valuable insights into the performance of voting methods on different collective decision problems. This could improve institutional design, even when there is no general theoretical result to support the optimality of a voting method. To support our claim, we first describe a decision problem that has not received much theoretical attention in the literature. We outline different voting methods to address that collective decision problem. Under certain criteria of assessment akin (...) to extensions of the Condorcet Jury Theorem, we run simulations for the methods using MATLAB, in order to compare their performance under various conditions. We consider and respond to concerns about the use of simulations in the assessment of voting procedures for policymaking. (shrink)

Deep learning has a vital role in computer vision to discover things. Deep learning techniques, especially convolutional neural networks, are being exploited in identifying and extracting relevant features of a specific set of images. In this research we suggested that it could help in detecting the gender-type of individuals and classifying them using convolutional neural networks, as it achieved superior predictive performance in classifying individuals according to gender, and the experimental results showed that the proposed system works accurately and efficiently, (...) which gives an accuracy rate of 97.76%. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features derived from medical (...) images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

Abstract— In this paper We're going to talk about science and symptoms for the clinical diagnosis of osteoarthritis of the hip enroll. In our Paper the diagnosis of osteoarthritis based the Expert system based on radiographic appearance of the joint space according to the criteria of Calgon and Lauren's rather than clinical features. However, it shows that radiographic imaging and the amount of pain and limitation of patient experiences does not have as strong of a correlation, as once believed more, (...) so central sensitization and psychosocial factors might play an important role in explaining which patients with radiographic osteoarthritis experience pain and which don't the clinical classification criteria. The moderate clinical value the clinical classification criteria contained the presence of hip pain plus the following items one hip internal rotation range of motion. (shrink)

The use of language models in Web applications and other areas of computing and business have grown significantly over the last five years. One reason for this growth is the improvement in performance of language models on a number of benchmarks — but a side effect of these advances has been the adoption of a “bigger is always better” paradigm when it comes to the size of training, testing, and challenge datasets. Drawing on previous criticisms of this paradigm as applied (...) to large training datasets crawled from pre-existing text on the Web, we extend the critique to challenge datasets custom-created by crowdworkers. We present several sets of criticisms, where ethical and scientific issues in language model research reinforce each other: labour injustices in crowdwork, dataset quality and inscrutability, inequities in the research community, and centralized corporate control of the technology. We also present a new type of tool for researchers to use in examining large datasets when evaluating them for quality. (shrink)

The public discussion on artificial intelligence for public health often revolves around future applications like drug discovery or personalized medicine. But already deployed artificial intelligence for content recommendation, especially on social networks, arguably plays a far greater role. After all, such algorithms are used on a daily basis by billions of users worldwide. In this paper, we argue that, left unchecked, this enormous influence of recommendation algorithms poses serious risks for public health, e.g., in terms of misinformation and mental health. (...) But more importantly, we argue that this enormous influence also yields a fabulous opportunity to provide quality information and to encourage healthier habits at scale. We also discuss the philosophical, technical and socio-economical challenges to seize this immense opportunity, and sketch the outlines of potential solutions. In particular, we argue that it would be extremely helpful if public and private institutions could publicly take a stand, as this may then generate the necessary social, economical and political pressure to massively invest in the research, development and deployment of the potential solutions. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...) from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

I will present two different applications - Dual PECCS and the TCL reasoning framework - addressing some crucial aspects of commonsense reasoning (namely: dealing with typicality effects and with the problem of commonsense compositionality) in a way that is integrated or compliant with different cognitive architectures. In doing so I will show how such aspects are better dealt with at different levels of representation and will discuss the adopted solution to integrate such representational layers.

Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay “Excavating AI: The Politics of Images in Machine Learning Training Sets.” The present article recounts the (...) creation of the JAFFE dataset and unravels each of Crawford and Paglen’s fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction. (shrink)

This article analyses the ethical aspects of multistakeholder recommendation systems (RSs). Following the most common approach in the literature, we assume a consequentialist framework to introduce the main concepts of multistakeholder recommendation. We then consider three research questions: who are the stakeholders in a RS? How are their interests taken into account when formulating a recommendation? And, what is the scientific paradigm underlying RSs? Our main finding is that multistakeholder RSs (MRSs) are designed and theorised, methodologically, according to neoclassical welfare (...) economics. We consider and reply to some methodological objections to MRSs on this basis, concluding that the multistakeholder approach offers the resources to understand the normative social dimension of RSs. (shrink)

From a behaviorist perspective, the desire to upload “minds” is already being realized on a mass, hyper-industrial scale thanks to the convergence of cognitive computing and Big Data. The accusation is that the “mind” is not an entity that exists intracranially. Instead, it is conceived as a process of individuation, which occurs in different modes and numbers. Some narratives of mind-uploading and technics in popular culture are explored: Transcendence (2014, dir. Wally Pfister) and Player Piano by Kurt Vonnegut. The discussed (...) issues consider Bernard Stiegler’s phenomenological notion of originary default and Thierry Bardini’s analysis of junk. Several questions are raised regarding miscalculations, accidents, in addition to Nicolas Agar’s discussion on the end of humanity, and Daniel Dennett’s Multiple Drafts theory within the context of exteriorization, which is considered as constitutive of interiority. (shrink)

Journal bearings play an important role on many rotating machines placed on industrial environments, especially in steam turbines of thermoelectric power plants. Babbitt damage (BD) and excessive clearance (C) are usual faults of steam turbine journal bearings. This paper is focused on achieving an effective identification of these faults through an intelligent recognition approach. The work was carried out through the processing of real data obtained from an industrial environment. In this work, a feature selection procedure was applied in order (...) to choose the features more suitable to identify the faults. This feature selection procedure was performed through the computation of typical testors, which allows working with both quantitative and qualitative features. The classification tasks were carried out by using Nearest Neighbors, Voting Algorithm, Naïve Associative Classifier and Assisted Classification for Imbalance Data techniques. Several performance measures were computed and used in order to assess the classification effectiveness. The achieved results (e.g., six performance measures were above 0.998) showed the convenience of applying pattern recognition techniques to the automatic identification of BD and C. (shrink)

Abstract: In fact, people get knee problems due to something such as sports, or fall. They usually have difficulty moving to the doctor. In this paper an expert system was designed to help users to correctly diagnose knee problems (Fractured in knee, torn ligament, Bursitis, Chondromalacia patellae, Torn cartilage, Rheumatoid arthritis and Rheumatic fever, Osteoarthritis, Baker's cyst, Osgood-Schlatter disease) with some information about the disease and self-care. CLIPS expert system language was used to design and implement this expert system.

Abstract— Anatomy of the anterior compartment includes the tibia and the fibula. It also includes the tibialis anterior tendon, the extensor hallucis longus tendon and the extensor digitorum longus tendons. The anterior tibial artery and the deep peroneal nerve and finally the superior and inferior extensor retinaculum. In this paper an expert system was designed to help users to correctly diagnose ankle problems. There are many structures present at the anterior aspect of the ankle; these structures are often susceptible to (...) injury common injuries and conditions around the anterior ankle. Clips expert system language was used to design and implement this expert system. (shrink)

Abstract— Everyone knows that straight teeth give you a beautiful smile. Did you know that straighter teeth can also affect your overall health? Left untreated crooked teeth can lead to periodontal disease and create abnormal amounts of stress on teeth and jaws which can lead to premature wear the effects of crooked teeth can worsen. Over time and may adversely affect your overall health common types of misalignment include crowding spacing deep bite. Open byte crossed might edge to edge bite (...) and excessive overjet crooked teeth can affect your bite creating. Unnatural stress and pressure leading to premature wear teeth that are subject to excessive pressure can develop shipping and not just at the gumline. Over time the excessive force may also cause flattened or worn-down teeth or chipping. Fractures at the edges of teeth additionally a bad bite may produce improper forces on the jaw that may lead to pain in the jaw. (shrink)

Fruit has been recognized as a good source of vitamins and minerals, and for their role in preventing vitamin C and vitamin A deficiencies. People who eat fruit as part of an overall healthy diet generally have a reduced risk of chronic diseases. Fruit are important sources of many nutrients, including potassium, fiber, vitamin C and folate (folic acid). One of important types of fruit is Grapefruit . Grapefruit is a tropical citrus fruit known its sweet and somewhat sour taste. (...) It's rich in nutrients, antioxidants and fiber, making it one of the healthiest citrus fruits you can eat. Research shows that it may have some powerful health benefits, including weight loss and a reduced risk of heart disease. In this paper we presented a system that recognize the two types of Grapefruit Pink and white based on deep learning using python on colab editor . This system may help people to automate their factories , restaurants or anything else need to classify these two types for different use . (shrink)

Jonathan Adams, David Pendlebury, Gordon Rogers, Martin Szomszor. 全球研究报告——东南亚[J]. 科学观察, 2020, 15(3): 54-65.

In this paper we describe a Artificial Neural Network model of children's development on the balance scale task. In balance scale experiments, the child is asked to predict the outcome of placing certain numbers of equal weights at various distances to the left or right of a fulcrum. Both stage progressions and information salience effects have been found with children on this task. Artificial Neural Network provided better fits to these human data than did previous models, whether rule-based or connectionist. (...) The ANN network model was used to generate a variety of novel predictions for psychological research. The dataset was collected from UCI Machine learning repository. The model was trained and validated using Just Neural Network Tool. The accuracy rate was 93%. (shrink)

Forest fires are a major environmental issue, creating economical and ecological damage while endangering human lives. Fast detection is a key element for controlling such phenomenon. To achieve this, one alternative is to use automatic tools based on local sensors, such as provided by meteorological stations. In effect, meteorological conditions (e.g. temperature, wind) are known to influence forest fires and several fire indexes, such as the forest Fire Weather Index (FWI), use such data. In this work, we explore a Just (...) Neural Network (JNN) approach to predict the burned area of forest fires were tested on recent real-world data collected from the northeast region of Portugal. The best accuracy we achieved was 98.75 percent. Such knowledge is particularly useful for improving firefighting resource management (e.g. prioritizing targets for air tankers and ground crews). (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

A cherry is the fruit of many plants of the genus Prunus, and is a fleshy drupe (stone fruit), Michigan's Northwest Lower Peninsula is the largest producer of tart cherries in the United States. In fact, grow 75% of the country's variety of mighty Montmorency cherries. We use these Ruby Red Morsels of Joy in over 200 cherry products like Salsas, Chocolate Covered Cherries, Cherry Nut Mixes, and much more. Cherry fruits are rich in vitamins and minerals, and it is (...) one of the natural sources that can supply the body with abundant amounts of potassium and energy. There are two types of sour cherries, sour or tart, with a little acidity, and the red cherries have a blackish color. Cherry blossoms are very beautiful and in Japan cherry trees are celebrated in bloom. In this paper, machine learning based approach is presented for identifying type Cherry with a dataset that contains 7,002 images use 3,444 images for training, 2,410 images for validation and 1,148 images for testing. A deep learning technique that extensively applied to image recognition was used. 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

This is the 2nd edition of Computational logic. Vol. 1: Classical deductive computing with classical logic. This edition has a wholly new chapter on Datalog, a hard nut to crack from the viewpoint of semantics when negation is included.

Unreproducibility stemming from a loss of data integrity can be prevented with hash functions, secure sketches, and Benford's Law when combined with the historical practice of a Pli Cacheté where scientific discoveries were archived with a 3rd party to later prove the date of discovery. Including the distinct systems of preregistation and data provenance tracking becomes the starting point for the creation of a complete ontology of scientific documentation. The ultimate goals in such a system--ideally mandated--would rule out several forms (...) of dishonesty, catch computational and database errors, catch honest mistakes, and allow for automated data audits of large collaborative open science projects. (shrink)

Artificial Intelligence is part of the Industrial Revolution 4.0 and already exists today. This shows that the future has come and everyone must prepare for the implementation of Artificial Intelligence to face the transformation of the digital era, especially the world of education. The community service workshop was attended by 66 participants, namely students, teachers, and structural officials of SMK Negeri 2 Singkawang. The workshop was held using demonstration methods, lectures, discussions and question and answer. This workshop provides information to (...) teachers and students about the importance of Artificial Intelligence (AI) in the digital transformation process. For teachers and students at SMKN 2 Singkawang it was introduced that algorithms or artificial intelligence methods could be given simply by representing problems into simple solutions with several examples of implementing artificial intelligence using Microsoft Excel and utilizing VBA macros. (shrink)

Abstract: Peach, (Prunus persica), fruit tree of the rose family (Rosaceae), grown throughout the warmer temperate regions of both the Northern and Southern hemispheres. Peaches are widely eaten fresh and are also baked in pies and cobblers; canned peaches are a staple commodity in many regions. Yellow-fleshed varieties are especially rich in vitamin A. Peach trees are relatively short-lived as compared with some other fruit trees. In some regions orchards are replanted after 8 to 10 years, while in others trees (...) may produce satisfactorily for 20 to 25 years or more, depending upon their resistance to diseases, pests, and winter damage. in the body.[1] In this paper, machine learning based approach is presented for identifying type peach with a dataset that contains 2,306 images use 1,212 images for training, 520 images for validation and 574 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...) "White" grapes are actually green in color, and are evolutionarily derived from the purple grape. Mutations in two regulatory genes of white grapes turn off production of anthocyanins, which are responsible for the color of purple grapes. Grapes are typically an ellipsoid shape resembling a prolate spheroid. In this paper, machine learning based approach is presented for identifying type Grapes with a dataset that contains 4,565 images use 2,393 images for training, 1,026 images for validation and 1,146 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...) "White" grapes are actually green in color, and are evolutionarily derived from the purple grape. Mutations in two regulatory genes of white grapes turn off production of anthocyanins, which are responsible for the color of purple grapes. Grapes are typically an ellipsoid shape resembling a prolate spheroid. In this paper, machine learning based approach is presented for identifying type Grapes with a dataset that contains 4,565 images use 2,393 images for training, 1,026 images for validation and 1,146 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Associations can grow, succeed, and sustain if their employees are committed. The main assets of an association are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, Associations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or (...) will be habitually absent. For some Associations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the association. In this paper, we propose An Artificial Neural Networks algorithm that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 99.00% accuracy. The proposed model will provide a useful mechanism to associations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. (shrink)

A neural network exploits the non-linearity of a problem to define a set of desired inputs. Neural networks are important in realizing a better way for classification in machine learning and finds application in various fields such as data mining, pattern recognition, forensics etc. In this paper, our focus is to classify of patient records obtained from clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The (...) Lymphography dataset comprises of 18 attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of EasyNN backbrapagation calssification algorithm in classifying predictor attributes and highlights its performance on Lymphography dataset. The accuracy we have reached is 97.78 percent in classification accuracy with the predictor feature. (shrink)

To reach sustainability transitions, we must learn to leverage social systems into tipping points, where societies exhibit positive-feedback loops in the adoption of sustainable behavioral and cultural traits. However, much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human (...) behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns. We validate the model against data on the evolution of cycling and driving behaviors in Copenhagen. Our model gives further evidence and justification for policies that make pro-environmental behavior psychologically salient, easy, and the path of least resistance. (shrink)

In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the price range of a mobile phone. We used a dataset that contains mobile phones information, and there was a number of factors that influence the classification of mobile phone price. Factors as battery power, CPU clock speed, has dual sim support or not, Front Camera mega pixels, has 4G or not, has Wi-Fi or not, etc…. 20 attributes were used as input variables for the (...) ANN model. A model based on the Multilayer Perceptron Topology was developed and trained, using data set, which its title is “Mobile Price Classification” and was obtained from Kaggle online community, and it is created by Abhishek Sharma. Test data evaluation shows that the ANN model is able to correctly predict the mobile price renge with 96.31 accuracy. (shrink)

Access to data in crime and place research has traditionally been reserved for those who have the means to collect fresh data themselves, pay for access, or obtain data through formal data sharing agreements. Even when access is granted, the usage of these data often comes with conditions that circumscribe how the data can be used through licensing or policy (Kitchin, 2014). Even the public dissemination of findings which emerge from analysis might be subject to restrictions. This can lead to (...) unequal access, controlled usage and curb the diffusion of findings, severely limiting the insight that can be obtained from data. (shrink)

Two art exhibitions, “Training Humans” and “Making Faces,” and the accompanying essay “Excavating AI: The politics of images in machine learning training sets” by Kate Crawford and Trevor Paglen, are making substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis underlines the non-negotiability (...) of informed consent when using human data in artistic and other contexts, and clarifies issues relating to the description of ML training sets. (shrink)

Abstract: In worldwide, there are several hundred cultivars of mango. Depending on the cultivar, mango fruit varies in size, shape, sweetness, skin color, and flesh color which may be pale yellow, gold, or orange. Where there are more than 15 types of manga. In this paper, two types Mango classification approach is presented with a dataset that contains approximately 1200 images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The (...) results found that CNN-driven Mango classification applications when used in classification automation it enables people to know the type of mango properly. The trained model achieved an accuracy of 100% on test set, demonstrating the feasibility of this approach. (shrink)

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

The concept of modes of existence of semiotic entities underlies (post)Greimasian semiotics, yet it seems to have received little attention. Modes of existence can be used in different senses. For Greimas, from the perspective of narrative semiotics, when Michelangelo first receives a block of marble and decides to sculpt the David, his intention is in a virtual mode; as Michelangelo progresses he ends up bringing the David into existence, and his intention comes to the realized mode. In Fontanille’s tensive semiotics, (...) however, modes of existence can have to do with how one can narrow or broaden the scope of our apprehension of the David as our eyes look at it in order to produce a meaningful experience. In this work, the perspectives of narrative and tensive semiotics are contrasted both theoretically and practically applying both to a number of examples. In order to identify all possible modes of existence and all the possibilities of transitioning from one to the other in the examples presented, we resort to the method of finite-state automata from computer science. In the end, we propose a robust narrative account of modes of existence that relies on narrative semiotics for its definition, but into which intent and apprehension from tensive semiotics can be integrated. This work calls for the need of establishing a syntax of modes of existence, since both Greimas and Fontanille construe them as being necessary to account for the production of signification. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. A number of factors were identified that may affect of oxygen consumption of thylakoid membranes from spinach. Factors such as curve, herbicide, dose, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some inhibition of photosynthesis in farms. The (...) evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Apple is a plant species that follows the apple genus, which is a fruit because it contains seeds of the pink family. It is one of the most fruit trees in terms of agriculture. The apple tree is small in length from 3 to 12 meters. Several recent studies have shown many health benefits of apples. It helps with the strengthening of the brain, heart, and stomach. It is used in the treatment of joint pain and limberness. It is opposite. (...) It stops vomiting. It goes to dyspnea. It corrects the liver, purifies the blood of toxins, strengthens the heart muscle, and kills the abdominal dentures. Which are richer in cell nutrition and development, bone strengthening and neuronal regeneration is an important source of detoxification, viruses, bacteria, and microbes in the body. In this paper, machine learning based approach is presented for identifying type of Apple with a dataset that contains 8,554 images. We used 4,488 images for training, 1,928 images for validation and 2,138 images for testing. A deep learning technique that extensively applied to image recognition was used. 70% from images were used for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The evaluation (...) of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

In this paper an Artificial Neural Network (ANN) model, was developed and tested for diagnosing Autism Spectrum Disorder (ASD). A dataset collected from ASD screening app was used in this paper, it contains ASD tests results based upon questions answers from users. Test data evaluation shows that the ANN model is able to correctly diagnose ASD with 100% accuracy.

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

Predication is an application of Artificial Neural Network (ANN). It is a supervised learning due to predefined input and output attributes. Multi-Layer ANN model is used for training, validating, and testing of the data. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether it is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JustNN software was used to training and validating the data. (...) The most important attributes of the data set were identified, and the accuracy of the predication of whether Mushroom is edible or Poisonous was 99.25%. (shrink)

We proposed an Artificial Neural Network (ANN) in this paper for predicting the rate category of movies. A dataset used obtained from UCI repository created for research purposes. Our ANN prediction model was developed and validated; validation results showed that the ANN model is able to 92.19% accurately predict the category of movies’ rate.

The study aimed to identify increasing efficiency of Palestinian university performance through the implementation of e-learning strategies in Palestinian universities (Al-Azhar University, Islamic university, Al-Aqsa University). The analytical descriptive approach was used for this purpose, and relying on the questionnaire as a main tool for data collection. The study society is from the senior management, where the number of senior management in the universities in question is 206. The random stratified sample was selected, and the Statistical Analysis of Social Sciences (...) (SPSS) program was used. The study found a significant relationship between e-learning strategies and the efficiency of university performance. The study also showed that senior management in Palestinian universities does not care about providing the appropriate budget for e-learning. The study also showed that senior management in Palestinian universities does not help to adhere to the performance standards set by the university. The study also found that the senior management in the Palestinian universities participates in the research that develops the university performance but does not care about following the policies of implementing the development of scientific research. The study recommended that the senior management of Palestinian universities should provide an e-learning budget and encourage employees to continue using e-learning strategies. It also recommended that senior management should focus on developing measures that would help to increase efficiency of performance. The study also recommended that senior management in Palestinian universities should adopt distinguished research and support, and the need to encourage interest in the implementation of policies for the development of scientific research. (shrink)

Abstract: One of the indelible events that happened between August and October 2012 was a massive flood in Nigeria which affected nine states with Kogi precisely Ibaji and Lokoja LGA’s as the worst hit. The havoc caused by the flood has huge impact on the people especially in the study areas where great devastation on the built environment was recorded. In April, 2013, Kogi state government commenced the post flood housing scheme with 272 housing units for the 2012 flood victims (...) and six (6) years after, the intervention is yet to be completed. This paper attempt to evaluate professionals’ perception on the post disaster housing reconstruction (PDHR) process so as to unravel problems that hindered and proffer solutions where necessary. The study employed quantitative method using structured questionnaires. 60 questionnaires were distributed and 49 (82%) were screened and analysed using SPSS. The findings reveal insufficient knowledge on PDHR, politicization of PDHR by the government in favour of urban areas, and home owner driven model as the most favoured. Further research focuses on bottom-up approach (owner driven model) for the sustainability of post disaster housing and livelihood recovery interventions. (shrink)