Search results for: state machine

Authors: Joachim Onyike

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The Federal Government of Nigeria in 2012 came up with a new National Housing Policy; one of its major objectives was to make housing affordable to the poor. Six years down the line, this study was carried out to find out whether the poor have fared better under the new housing policy. Owerri, the capital of Imo State, was adopted as a case study to mirror the situation nationwide. The study population was made up of low-income civil servants, i.e., grade levels 1–6 in the Imo State Civil Service. The study looked at household size, household income, rental levels, house prices, costs of major building materials, land values, land tenure, the interest rate on mortgages, inflation rate, and the status of government interventions, owing to their obvious effect on housing affordability by the low-income earners. The study made use of physical observations, questionnaires, and interviews as well as library studies to elicit relevant information. Housing affordability by the subject population did not improve. It rather dropped. The study came to the conclusion that in spite of the new National Housing Policy, housing affordability by the low-income earners has not improved. The policy as it affects the poor has not been duly implemented by both Federal and State Governments.

Keywords: house prices, housing affordability, housing policy, land values, low-income earners

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Authors: Gaoguang Yang, Shan Fu

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Human-machine interface (HMI) intermediate system information to human operators to facilitate human ability to manage and control the system. Well-designed HMI would enhance human ability. An evaluation must be performed to confirm that the designed HMI would enhance but not degrade human ability. However, the prevalent HMI evaluation techniques have difficulties in more thoroughly and accurately evaluating the suitability and fitness of a given HMI for the wide variety of uncertainty contained in both the existing HMI evaluation techniques and the large number of task scenarios. The first limitation should be attributed to the subjective and qualitative analysis characteristics of these evaluation methods, and the second one should be attributed to the cost balance. This study aims to explore the connection between objective HMI characters and ergonomic safety and step forward toward solving these limitations with objective, characterized HMI parameters. A simulation experiment was performed with the time needed for human operators to recognize the HMI information as characterized HMI parameter, and the result showed a strong correlation between the parameter and ergonomic safety level.

Keywords: Human-Machine Interface (HMI), evaluation, objective, characterization, simulation

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Authors: Balasaheb Bansode, L. Ladusingh

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The public distribution system is an important component of food security. It is a massive welfare program undertaken by Government of India and implemented by state government since India being a federal state; for achieving multiple objectives like eliminating hunger, reduction in malnutrition and making food consumption affordable. This program reaches at the community level through the various agencies of the government. The paper focuses on the accessibility of PDS at household level and how the present policy framework results in exclusion and inclusion errors. It tries to explore the sanctioned food grain quantity received by differentiated ration cards according to income criterion at household level, and also it has highlighted on the type of corruption in food distribution that is generated by the PDS system. The data used is of secondary nature from NSSO 68 round conducted in 2012. Bivariate and multivariate techniques have been used to understand the working and consumption of food for this paper.

Keywords: calories intake, entitle food quantity, poverty aliviation through PDS, target error

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: N. Hatraf, L. Merabti, Z. Neffah, W. Taane

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The excessive consumption of fossil energies (electrical energy) during summer caused by the technological development involves more and more climate warming. In order to reduce the worst impact of gas emissions produced from classical air conditioning, heat driven solar absorption chiller is pretty promising; it consists on using solar as motive energy which is clean and environmentally friendly to provide cold. Solar absorption machine is composed by four components using Lithium Bromide /water as a refrigerating couple. LiBr- water is the most promising in chiller applications due to high safety, high volatility ratio, high affinity, high stability and its high latent heat. The lithium bromide solution is constitute by the salt lithium bromide which absorbs water under certain conditions of pressure and temperature however if the concentration of the solution is high in the absorption chillers; which exceed 70%, the solution will crystallize. The main aim of this article is to study the phenomena of the crystallization and to evaluate how the dependence between the electric conductivity and the concentration which should be controlled.

Keywords: absorption, crystallization, experimental results, lithium bromide solution

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Authors: S. M. Mahdi Niktabara, K. Seshagiri Raob, Amit Kumar Shrivastavac, Jiří Ščučkaa

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The presence of the discontinuity in the form of joints is one of the most significant factors causing instability in the rock mass. On the other hand, dynamic loads, including earthquake and blasting induce cyclic shear loads along the joints in rock masses; therefore, failure of rock mass exacerbates along the joints due to changing shear resistance. Joints are under constant normal load (CNL) and constant normal stiffness (CNS) conditions. Normal stiffness increases on the joints with increasing depth, and it can affect shear resistance. For correct assessment of joint shear resistance under varying normal stiffness and number of cycles, advanced laboratory shear machine is essential for the shear test. Conventional direct shear equipment has limitations such as boundary conditions, working under monotonic movements only, or cyclic shear loads with constant frequency and amplitude of shear loads. Hence, a large-scale servo-controlled direct shear testing machine was designed and fabricated to perform shear test under the both CNL and CNS conditions with varying normal stiffness at different frequencies and amplitudes of shear loads. In this study, laboratory cyclic shear tests were conducted on non-planar joints under varying normal stiffness. In addition, the effects of different frequencies and amplitudes of shear loads were investigated. The test results indicate that shear resistance increases with increasing normal stiffness at the first cycle, but the influence of normal stiffness significantly decreases with an increase in the number of shear cycles. The frequency of shear load influences on shear resistance, i.e. shear resistance increases with increasing frequency. However, at low shear amplitude the number of cycles does not affect shear resistance on the joints, but it decreases with higher amplitude.

Keywords: cyclic shear load, frequency of load, amplitude of displacement, normal stiffness

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Authors: John Abubakar

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Access to decent housing is a global challenge. An estimated one billion people currently live in slum settlements globally. About 80 percent of these slum dwellers are in Asia and Africa. Nigeria accounts for a significant percentage of African slum dwellers because of its size. Addressing the challenge of slum settlement in Nigeria can have far reaching positive implications in Africa. A major slum settlement in Nigeria is Mararaba slum in Karu local government of Nasarawa state. The importance of this slum settlement hinges on its proximity to Abuja, Nigeria’s capital city. This study is an attempt at identifying the impact of self-help housing and government mass housing scheme in addressing the problem of housing access in Mararaba area of Karu local government, Nasarawa state. The research method used is the content analysis of existing literature. After the review of existing literature, the paper argues that self-help house is more impactful in addressing housing access in Mararaba area of Karu local government. Therefore, self-help housing should be recognized and incorporated into the housing policy of Nasarawa state. Both self-help housing and government mass housing programs are reviewed comparatively, and their strengths and weaknesses analyses.

Keywords: slum settlement, informal settlement, progressive improvement, holistic planning

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Authors: Jayden Leonard, Nguyen Que Huong

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In this work, we study the Trion state in a spherical quantum dot of a direct band gap semiconductor with a shell of organic material. The electronic structure of the Trion due to degenerate valence band will be considered. The coupling between the wannier exciton inside the dot and the Frenkel exciton in the shell will make the Trion state become hybrid. The competition between “semiconductor” and “organic” phases of the Trion and the transitions between them depend on Parameters of the system such as the materials, the size of the dot and the thickness of the shell, etc… and could be manipulated using those parameters.

Keywords: trion, exciton, quantum dot, heterostructure

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Authors: Fahad Suleiman

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The aim of this paper is to study students’ view on mathematics learning in Katsina State Senior Secondary Schools of Nigeria, such as their conceptions of mathematics, attitudes toward mathematics learning, etc. A questionnaire was administered to a random sample of 1,225 senior secondary two (SS II) students of Katsina State in Nigeria. The data collected showed a clear picture of the hurdles that affect the teaching and learning of mathematics in our schools. Problems such as logistics and operational which include shortage of mathematics teachers, non–availability of a mathematics laboratory, etc. were identified. It also depicted the substantial trends of changing views and attitudes toward mathematics across secondary schools. Students’ responses to the conception of mathematics were consistent and they demonstrated some specific characteristics of their views in learning mathematics. This survey has provided useful information regarding students’ needs and aspirations in mathematics learning for curriculum planners and frontline teachers for future curriculum reform and implementation.

Keywords: attitudes, mathematics, students, teacher

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Authors: Kailash C. Madan

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We study four models of a three server queueing system with Bernoulli schedule optional server vacations. Customers arriving at the system one by one in a Poisson process are provided identical exponential service by three parallel servers according to a first-come, first served queue discipline. In model A, all three servers may be allowed a vacation at one time, in Model B at the most two of the three servers may be allowed a vacation at one time, in model C at the most one server is allowed a vacation, and in model D no server is allowed a vacation. We study steady the state behavior of the four models and obtain steady state probability generating functions for the queue size at a random point of time for all states of the system. In model D, a known result for a three server queueing system without server vacations is derived.

Keywords: a three server queue, Bernoulli schedule server vacations, queue size distribution at a random epoch, steady state

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

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The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: cloud forensics, data protection Laws, GDPR, IoT forensics, machine Learning

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Authors: R. Dimitri Halley

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The research within Jungian Psychology presented here is on the wave state of Self. What has been discovered via shared dreaming, independently correlating dreams across dreamers, is beyond the Self stage into the deepest layer or the wave state Self: the very quantum ocean, the Self archetype is embedded in. A quantum wave or rhyming of meaning constituting synergy across several dreamers was discovered in dreams and in extensively shared dream work with small groups at a post therapy stage. Within the format of shared dreaming, we find synergy patterns beyond what Jung called the Self archetype. Jung led us up to the phase of Individuation and delivered the baton to Von Franz to work out the next synchronistic stage, here proposed as the finding of the quantum patterns making up the wave state of Self. These enfolded synchronistic patterns have been found in group format of shared dreaming of individuals approximating individuation, and the unfolding of it is carried by belief and faith. The reason for this format and operating system is because beyond therapy and of living reality, we find no science – no thinking or even awareness in the therapeutic sense – but rather a state of mental processing resembling more like that of spiritual attitude. Thinking as such is linear and cannot contain the deepest layer of Self, the quantum core of the human being. It is self reflection which is the container for the process at the wave state of Self. Observation locks us in an outside-in reactive flow from a first-person perspective and hence toward the surface we see to believe, whereas here, the direction of focus shifts to inside out/intrinsic. The operating system or language at the wave level of Self is thus belief and synchronicity. Belief has up to now been almost the sole province of organized religions but was viewed by Jung as an inherent property in the process of Individuation. The shared dreaming stage of the synchronistic patterns forms a larger story constituting a deep connectivity unfolding around individual Selves. Dreams of independent dreamers form larger patterns that come together as puzzles forming a larger story, and in this sense, this group work level builds on Jung as a post individuation collective stage. Shared dream correlations will be presented, illustrating a larger story in terms of trails of shared synchronicity.

Keywords: belief, shared dreaming, synchronistic patterns, wave state of self

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Authors: Juan José Mesas, Luis Sainz

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The motivation for the development of this paper lies in the need for energy networks to reduce losses, improve performance, optimize their operation and try to benefit from the interconnection capacity with other networks enabled for other energy carriers. These interconnections generate interdependencies between some energy networks and others, which requires suitable models and methods for their analysis. Traditionally, the modeling and study of energy networks have been carried out independently for each energy carrier. Thus, there are well-established models and methods for the steady-state analysis of electrical networks, gas networks, and thermal networks separately. What is intended is to extend and combine them adequately to be able to face in an integrated way the steady-state analysis of networks with multiple energy carriers. Firstly, the added value of multi-energy networks, their operation, and the basic principles that characterize them are explained. In addition, two current aspects of great relevance are exposed: the storage technologies and the coupling elements used to interconnect one energy network with another. Secondly, the characteristic equations of the different energy networks necessary to carry out the steady-state analysis are detailed. The electrical network, the natural gas network, and the thermal network of heat and cold are considered in this paper. After the presentation of the equations, a particular case of the steady-state analysis of a specific multi-energy network is studied. This network is represented graphically, the interconnections between the different energy carriers are described, their technical data are exposed and the equations that have previously been presented theoretically are formulated and developed. Finally, the two iterative numerical resolution methods considered in this paper are presented, as well as the resolution procedure and the results obtained. The pros and cons of the application of both methods are explained. It is verified that the results obtained for the electrical network (voltages in modulus and angle), the natural gas network (pressures), and the thermal network (mass flows and temperatures) are correct since they comply with the distribution, operation, consumption and technical characteristics of the multi-energy network under study.

Keywords: coupling elements, energy carriers, multi-energy networks, steady-state analysis

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Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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Authors: Abraham Undu, Stephen Akuma

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Websites are becoming a major component of an organization’s success in our ever globalizing competitive world. The website symbolizes an organization, interacting or projecting an organization’s principles, culture, values, vision, and perspectives. It is an interface connecting organizations and their clients. The university, as an academic institution, makes use of a website to communicate and offer computing services to its stakeholders (students, staff, host community, university management etc). Unfortunately, website designers often give more consideration to the technology, organizational structure and business objectives of the university than to the usability of the site. Website designers end up designing university websites which do not meet the needs of the primary users. This empirical study investigated the Benue State University website from the point view of students. This research was realized by using a standardized website usability questionnaire based on the five factors of usability defined by WAMMI (Website Analysis and Measurement Inventory): attractiveness, controllability, efficiency, learnability and helpfulness. The result of the investigation showed that the university website (https://portal.bsum.edu.ng/) has neutral usability level because of the usability issues associated with the website. The research recommended feasible solutions to improve the usability of the website from the users’ perspective and also provided a modified usability model that will be used for better evaluation of the Benue State University website.

Keywords: Benue State University, modified usability model, usability, usability factors

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Authors: Mladen Milicevic

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In this paper, subjective film sound will be addressed as it gets represented in narrative cinema. First, 'meta-diegetic' sound will be briefly explained followed by transition to “oneiric” sound. The representation of oneiric sound refers to a situation where film characters are experiencing some sort of an altered state of consciousness. Looking at an antlered state of consciousness in terms of human brain processes will point out to the cinematic ways of expression, which 'mimic' those processes. Using several examples for different films will illustrate these points.

Keywords: oneiric, ASC, film, sound

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Authors: O. S. Ebrahim, K. O. Shawky, M. O. S. Ebrahim, P. K. Jain

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Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). It was illustrated that changing the connection of the stator windings from delta to star at no load could achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.

Keywords: ANN, ESM, IM, star/delta switch, supervisory control, FT, reliability, power quality

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Authors: M. Barański

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This paper presents a several diagnostic methods designed to electrical machines especially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives. Those methods are preferred by the author in periodic diagnostic of electrical machines. The special attention should be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methods were created in Institute of Electrical Drives and Machines Komel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to-traction drive, turn insulation, vibrations

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Authors: Amina Boukelkoul, Rahil Boukelkoul, Leila Maachia

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The cumulative vapors emitted from the service stations may represent a hazard to the environment and the population. Besides fuel spill and their penetration into deep soil layers are the main contributors to soil and ground-water contamination in the vicinity of the petrol stations. The amount of the effluents from the service stations depends on strategy of maintenance and the policy adopted by the management to reduce the pollution. One key of the proposed approach is the idea of managing the effluents from the service stations which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating a probabilistic percentage of the amount of emitted pollutants is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the amount according to various options of operation.

Keywords: environment, markov modeling, pollution, service station

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Radoslaw Nagay

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The Paulina Project aims to address the challenges faced by immobilized individuals, such as those with multiple sclerosis, muscle dystrophy, or spinal cord injuries, by developing a flexible hardware and software solution. This paper presents the research and development efforts of our team, which commenced in 2019 and is now in its final stage. Recognizing the diverse needs and limitations of individuals with limited mobility, we conducted in-depth testing with a group of 30 participants. The insights gained from these tests led to the complete redesign of the system. Our presentation covers the initial project ideas, observations from in-situ tests, and the newly developed system that is currently under construction. Moreover, in response to the financial constraints faced by many disabled individuals, we propose an affordable business model for the future commercialization of our invention. Through the Paulina Project, we strive to empower immobilized individuals, providing them with greater independence and improved quality of life.

Keywords: UI, human-machine interface, social inclusion, multiple sclerosis, muscular dystrophy, spinal cord injury, quadriplegic

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Wanqing You, Kai Qian, Ying Qian

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The concepts of Cloud Computing provide users with infrastructure, platform and software as service, which make those services more accessible for people via Internet. To better analysis the performance of Cloud Computing provisioning policies as well as resources allocation strategies, a toolkit named CloudSim proposed. With CloudSim, the Cloud Computing environment can be easily constructed by modelling and simulating cloud computing components, such as datacenter, host, and virtual machine. A good scheduling strategy is the key to achieve the load balancing among different machines as well as to improve the utilization of basic resources. Recently, the existing scheduling algorithms may work well in some presumptive cases in a single machine; however they are unable to make the best decision for the unforeseen future. In real world scenario, there would be numbers of tasks as well as several virtual machines working in parallel. Based on the concepts of multi-queue, this paper presents a new scheduling algorithm to schedule tasks with CloudSim by taking into account several parameters, the machines’ capacity, the priority of tasks and the history log.

Keywords: hierarchical queue, load balancing, CloudSim, information technology

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Authors: Jawad Ul Haq, Evan Mazur, Ahmed Qureshi, Mohamed Al-Hussein

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The uses of wood in furniture, building, bridges and aluminum in transportation and construction, make aluminum and forest economy a prominent matter in North America. Machines available to date to cut the aforementioned materials are mostly industry oriented with complex structure and operations which require special training and skill. Furthermore, requirements such as pneumatics, 3-phase supply are associated with cost, maintenance, and safety hazards. Power saws are very useful tools used to cut and shape materials; however, they can cause serious hand injuries. Operator’s hands in table saw are vulnerable as they are used to guide pieces into the saw. Apart from hands, saw operator is also prone to material being kicked back out of the saw or sustain eye or respiratory injuries due to rapidly flying sawdust and other debris. In this paper, design of an automatic saw cutting machine has been proposed to ensure safety, portability, usage at domestic level and capability to cut both aluminum and wood. This paper demonstrates detailed Mechanical design in SOLIDWORKS and Control Systems using Programmable Logic Controller (PLC), based on the aforementioned design objectives.

Keywords: programmable logic controller, saw cutting, control, automation

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: P. Raajeswari, S. M. Devatha, S. Yuvajanani, U. Rashika

Abstract:

Edible food packaging are the need of the hour to save life on land and under water by eliminating waste cycle and replacing Single Use Plastics at grass root level as it can be eaten or composted as such. Cassava (Manihot esculenta) selected for making edible films are rich source of starch, and also it exhibit good sheeting propertiesdue to the high amylose: amylopectin content. Cassava starch was extracted by manual method at a laboratory scale and yielded 65 per cent. Edible films were developed by adding food grade plasticizers and water. Glycerol showed good plasticizing property as compared to sorbitol and polylactic acid in both manual (petri dish) and machine (film making machine) production. The thickness of the film is 0.25±0.03 mm. Essential oil and components from peels like pomegranate, orange, pumpkin, onion, and banana brat, and herbs like tulsi and country borage was extracted through the standardized aqueous and alkaline method. In the standardized film, the essential oil and components from selected peel and herbs were added to the casting solution separately and casted the film. It was added to improve the anti-oxidant, anti-microbial and optical properties. By inclusion of extracts, it reduced the bubble formation while casting. FTIR, Water Vapor and Oxygen Transmission Rate (WVTR and OTR), tensile strength, microbial load, shelf life, and degradability of the films were done to analyse the mechanical property of the standardized films. FTIR showed the presence of essential oil. WVTR and OTR of the film was improved after inclusion of essential oil and extracts from 1.312 to 0.811 cm₃/m₂ and 15.12 to 17.81 g/ m₂.d. Inclusion of essential oil from herbs showed better WVTR and OTR than the inclusion of peel extract and standard. Tensile strength and Elongation at break has not changed by essential oil and extracts at 0.86 ± 0.12 mpa and 14 ± 2 at 85 N force. By inclusion of extracts, an optical property of the film enhanced, and it increases the appearance of the packaging material. The films were completely degraded on 84thdays and partially soluble in water. Inclusion of essential oil does not have impact on degradability and solubility. The microbial loads of the active films were decreased from 15 cfu/gm to 7 cfu/gm. The films can be stored at frozen state for 24 days and 48 days at atmospheric temperature when packed with South Indian snacks and savories.

Keywords: active films, cassava starch, plasticizer, characterization

Procedia PDF Downloads 72

Authors: Prathuang Usaborisut, Dithaporn Thungsotanon

Abstract:

Soil compaction induced by a tractor towing trailer becomes a major problem associated to sugarcane productivity. Soil beneath the tractor’s tire is not only under compressing stress but also shearing stress. Therefore, in order to help to understand such effects on soil, this research aimed to determine stress state in soil and predict compaction of soil under a tractive tire. The octahedral stress ratios under the tires were higher than one and much higher under higher draft forces. Moreover, the ratio was increasing with increase of number of tire’s passage. Soil compaction model was developed using data acquired from triaxial tests. The model was then used to predict soil bulk density under tractive tire. The maximum error was about 4% at 15 cm depth under lower draft force and tended to increase with depth and draft force. At depth of 30 cm and under higher draft force, the maximum error was about 16%.

Keywords: draft force, soil compaction model, stress state, tractive tire

Procedia PDF Downloads 347

Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 74

Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

Abstract:

This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

Procedia PDF Downloads 170