Logic Machine Research Articles

Public opinion mining using natural language processing technique for improvisation towards smart city.

In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information’s, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.

Logic Programming and Machine Ethics

Transparency is a key requirement for ethical machines. Verified ethical behavior is not enough to establish justified trust in autonomous intelligent agents: it needs to be supported by the ability to explain decisions. Logic Programming (LP) has a great potential for developing such perspective ethical systems, as in fact logic rules are easily comprehensible by humans. Furthermore, LP is able to model causality, which is crucial for ethical decision making.

Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis.

IntroductionThe aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes.Research design and MethodsOverall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005–2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain.ResultsThe combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control.ConclusionsTreating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention.

Extractive text summarization using F-RBM

The Intense growth of data over the internet and process extracting predominating details from it is a time-consuming task. A new application of making this task simplified is text-summarization. This paper presents the integration of Restricted Boltzmann Machine and Fuzzy Logic methods. Both of these processes have different ways of giving precision to a summary but together being part of the unsupervised world has surpassed their working alone to summarize the text. Integrated algorithm i.e. called FRBM (fuzzy Restricted Boltzmann Machine) provides enhanced depiction in the modeling of probability over visible and hidden units than the RBM.

Machine Logic Program Development and Electrical Design of H Gantry Automation System for Compressor Housing

Automation is one of the growing fields ,and is being used at levels from small scale industries to very a large scale industries due to the advantage of increase in productivity and quality, along with it recently new revolution in the automation 4.0enabling the data monitoring possible which helps in better control and monitoring of machineries and equipments. The objective of the work is to design electrical circuit and perform suitable control actions such as loading and unloading of heavier components to machines, indexing, providing suitable safety for the devices and improvising productivity and quality rate in the production line. These activities are being done by a control system adopted to gantry system, in such control systems there will be provision for manual control, auto mode, jog mode &amp; edit mode to enable the work to be carried out smoothly and effectively. Such complete system is designed to reach the customer required production cycle time with 6 axis (y,z1,z2,x1,x2,c), these axis are controlled by CNC controller and all other Stations like IPC, OPC, tilting stations are controlled by the PMC controller, which is the part of CNC.

Human experts vs. machines in taxa recognition

The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hierarchy in detail. We compare the results of Convolutional Neural Networks to human experts and support vector machines. Our results revealed that human experts using actual specimens yield the lowest classification error (CE¯=6.1%). However, a much faster, automated approach using deep Convolutional Neural Nets comes close to human accuracy (CE¯=11.4%) when a typical flat classification approach is used. Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts (CE¯=13.8%). Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field.

Detection of breast cancer tumours based on feature reduction and classification of thermograms

ABSTRACT The patients having malignant breast tumours if detected in early stage have a better chance of survival. It is observed that the analysis of the texture features of the breast thermograms helps in providing the right information for diagnosis to a greater extent. In this study, the breast thermograms of 56 subjects having temperature recordings available at Database Mastology Research (DMR), visual labs are considered. Further, the texture features in the Gray level Run Length Matrix (GLRLM) and Gray level Co-occurrence Matrix (GLCM) are extracted from these images. The correlation of features gives a linear relationship between the variables that help to analyse the quantitative relationship between the variables. The features are selected by using unsupervised feature reduction techniques, i.e. Principal Component Analysis (PCA) and Autoencoder (AE). The features selected are observed to be relevant in detecting the abnormality between healthy and unhealthy breast. Different classifiers viz. support vector machine, decision tree, random forest, K-NN, linear Regression, and fuzzy logic are then applied to the selected features for detecting the presence of malignancy in breast. Among all the classifiers, Random Forest (RF) with PCA has been observed to yield an accuracy of 95.45% in classifying the benign and malignant tumours.

Unifying logic rules and machine learning for entity enhancing

This paper proposes a notion of entity enhancing, which unifies entity resolution and conflict resolution, to identify tuples that refer to the same real-world entity and at the same time, correct semantic inconsistencies. We propose to unify rule-based and machine learning (ML) methods for entity enhancing, by embedding ML classifiers as predicates in logic rules. We model entity enhancing by extending the chase. We show that the chase warrants correctness justification and the Church-Rosser property. Moreover, we settle fundamental problems associated with entity enhancing, including the enhancing, consistency, satisfiability, and implication problems, ranging from NP-complete and coNP-complete to Π 2 -complete. Taken together, these provide a new theoretical framework for unifying entity resolution and conflict resolution.

Designing hybrid classifiers based on general type-2 fuzzy logic and support vector machines

Designing hybrid classifiers based on general type-2 fuzzy logic and support vector machines

Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

Modelling, Simulation and Optimization of Diagnosis Cardiovascular Disease Using Computational Intelligence Approaches

Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.

BEHAVIORAL COMPONENTS IN RELATIONSHIPS OF ECONOMIC AGENTS (EXAMPLE OF THE UKRAINIAN CAR MARKET)

In neoclassical economics a concept of individual is seen as a logical thinking machine, which accurately analyzes all information received and, based on it, makes decisions that maximize his/her personal gain and minimize risks while achieving his/her goals. Such behavior of a person is called rational. Such concept serves as a source of neoclassicism for the construction of supply and demand models, operation of tax systems, business cycles, inflation etc. It was not accepted to pay attention to the psychology of decision-making, it was believed, that these transient, random factors pale against the grandeur of economic incentives. Psychologists are interested in solutions that are emotional, casual, pursued by some sort of cultural ideas, norms, rules, and may even be phobias, conversely economics is the science of rational behavior. The purpose of this paper is to identify and analyze the behavioral components and their influence on the interaction of economic agents in the commodity market. The study used methods of constructing a multivariate regression model, OLS, Student and Fisher criteria, statistical research, sociological surveys and expert opinion. Authors will analyze the behavioral aspects of market relationships on the example of used car market in Ukraine This paper provides several different situations and tested them with real market data to demonstrate that both buyers and sellers may not act rationally on the market, set prices, based on their personal beliefs, subjective and psychological factors and that must be considered when building economical models.

An Energy Storage System for Recycling Regenerative Braking Energy in High-Speed Railway

This paper proposes an energy storage system (ESS) for recycling the regenerative braking energy in the high-speed railway. In this case, a supercapacitor-based storage system is integrated at the DC bus of the back to back converter that is connected to the two power phases of the traction power system (TPS). In order to ensure the suitability of the ESS in the TPS, the operation modes are classified by considering the load conditions of the TPS and the state-of-charge limit of the supercapacitor. Then, a master-slave control strategy with a central controller is proposed. In which, the central controller realizes the flexible management for all operation modes, by means of the state machine logic. Meanwhile, the master-slave control serves for coordinating the operation of the multiple converters according to the commands from the central controller. Finally, the capabilities of the proposed ESS are validated by sufficient experiments under different operation modes and the simulation based on the field data.

CLASSIFICATION OF NETWORK PROTOCOLS WITH APPLICATION OF MACHINE LEARNING METHODS AND FUZZY LOGIC ALGORITHMS IN TRAFFIC ANALYSIS SYSTEMS

This paper presents a new effective approach to analyzing network traffic in order to determine the protocol of information exchange. A brief description of the structure of the algorithm for classifying network packets by belonging to one of the known network protocols is given. To define the protocol, the principle of high-speed one-packet classification is used, which consists in analyzing the information transmitted in each particular packet. Elements of behavioral analysis are used, namely, the transition states of information exchange protocols are classified, which allows to achieve a higher level of accuracy of classification and a higher degree of generalization in new test samples. The topic of the article is relevant in connection with the rapid growth of transmitted traffic, including malicious traffic, and the emergence of new technologies for transmitting and processing information. The article analyzes the place of traffic analysis systems among other information security systems, describes the tasks that they allow to solve. It is shown that when recognizing the internal state in which a particular protocol may be in the process of information exchange at the handshake stage, a classifier of network packets of the application level can be useful. To classify network packets, we used fuzzy logic algorithms (Mamdani model) and machine learning methods (neural network solutions based on logistic regression). The paper presents 4 stages of developing a network packet classifier – monitoring and collecting packet statistics of the most famous network traffic protocols, preprocessing primary packet statistics, building a classifier for network packets and testing. The test results of the constructed software module capable of identifying network protocols for information exchange are demonstrated.

Network Communication: Algorithmic and Risky Connection

The article presents methods of reasoning about the modern algorithmic culture defined by the communicative modes of networks. It is emphasized that, regardless of the biases and accentuations of theoretical thought regarding the information environment and digital communications, the transformation of operator statuses and the mechanics of exchanges, the conceptual space is increasingly leaning towards variations in modeling the interaction of human and non-human agents, in parity or enhancing the privileges of everyone. In support of this, cases of mutual observability of complex algorithms of status online platforms and financial markets, that increases their micro-temporal predictability; the use of botnets of infected computers without the consent of their users in an attack on targeted websites, demonstrating the capabilities of internal machine logic; broadcasting “fake news” in informative practices inspired by human intentionality and adjusted by network generativity are considered. In attempts to discuss socio-technical media reality, discourses of power systems, hierarchical structures and civil society are combined with an understanding of the processes of “social submission” and “machine enslavement”(Deleuze, Guattari), the “technical mentality” (Simondon), as the ontogenetic ability of complex machines to autonomous and self-referential development, that should not be ignored or exaggerated. The search for “non-technical” semantics for describing the media state, updating an acceptable language for sociology, without losing the patterns and facts of media reality, providing a speedy universal connection, producing new solidarity and disunity, changing meanings, but also exuding virulence, remains urgently needed.

Wandering Machines

The development of automatic narrative systems has been largely driven by the engineering tendency to anthropomorphize the machine logic so they can ‘tell stories’ similar to how humans do. From the artists’ perspective, however, the experimentation with their media is often more important than the (plausibility of) storytelling, and it often unfolds in non-verbal events that have a potential to generate diverse narratives through experience of the audience. We discuss the emergence of the creative practices that enrich the poetic repertoire of new media art by playfully utilizing the machine flaws, irregularities, errors and systemic technical imperfections thus revealing the human biases and fallacies entangled with technology. One of the implications of these practices is that if the AI research opens up a broader space in which a machine could achieve its own authorial voice, our concept and understanding of the narrative would need to be reconsidered.

Secuenciador dinámico para el control de movimiento de robot hexápodo en una arquitectura FPGA

A hexapod robot control system hardware architecture is presented. The control system is integrated in an FPGA XC6SLX16-Spartan6. The design is developed using hardware description language, VHDL. A robust architecture, divided into three logical state machine blocks, is implemented in the FPGA. Block one consists of a serial communication system for the information interchange between the user and the FPGA. The second block corresponds to the sequential circuit and data manager in charge of controlling the functions of the entire architecture. The third block is an 18-channel PWM pulse generator with a progressive duty cycle change control to perform robot movements. The system is designed to allow the user to download a complex movements sequence by combining different positions and waiting times. The architecture allows to save up to 10 movements different sequences in the internal memory block. Additionally, it allows modifying any sequence in real time, without affecting the robot operation. The architecture controls 18 servomotors allowing 18 degrees of freedom to the robot. The performing and movement test of the robot are presented.

Knowledge Authoring and Question Answering with KALM

Knowledge representation and reasoning (KRR) is one of the key areas in artificial intelligence (AI) field. It is intended to represent the world knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance the expert systems to perform querying and inference tasks. Currently, constructing large scale knowledge bases (KBs) with high quality is prohibited by the fact that the construction process requires many qualified knowledge engineers who not only understand the domain-specific knowledge but also have sufficient skills in knowledge representation. Unfortunately, qualified knowledge engineers are in short supply. Therefore, it would be very useful to build a tool that allows the user to construct and query the KB simply via text. Although there is a number of systems developed for knowledge extraction and question answering, they mainly fail in that these system don't achieve high enough accuracy whereas KRR is highly sensitive to erroneous data. In this thesis proposal, I will present Knowledge Authoring Logic Machine (KALM), a rule-based system which allows the user to author knowledge and query the KB in text. The experimental results show that KALM achieved superior accuracy in knowledge authoring and question answering as compared to the state-of-the-art systems.

Querying Knowledge via Multi-Hop English Questions

AbstractThe inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace.Knowledge and query authoringusing natural language, especiallycontrollednatural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced theKALMsystem (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introducesKALM-QA(KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, calledMetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.

What metrics should we use to measure commercial AI?

In AI Matters Volume 4, Issue 2, and Issue 4, we raised the notion of the possibility of an AI Cosmology in part in response to the "AI Hype Cycle" that we are currently experiencing. We posited that our current machine learning and big data era represents but one peak among several previous peaks in AI research in which each peak had accompanying "Hype Cycles". We associated each peak with an epoch in a possible AI Cosmology. We briefly explored the logic machines, cybernetics, and expert system epochs. One of the objectives of identifying these epochs was to help establish that we have been here before. In particular we've been in the territory where some application of AI research finds substantial commercial success which is then closely followed by AI fever and hype. The public's expectations are heightened only to end in disillusionment when the applications fall short. Whereas it is sometimes somewhat of a challenge even for AI researchers, educators, and practitioners to know where the reality ends and hype begins, the layperson is often in an impossible position and at the mercy of pop culture, marketing and advertising campaigns. We suggested that an AI Cosmology might help us identify a single standard model for AI that could be the foundation for a common shared understanding of what AI is and what it is not. A tool to help the layperson understand where AI has been, where it's going, and where it can't go. Something that could provide a basic road map to help the general public navigate the pitfalls of AI Hype.