Search results for: intelligent late

Authors: Hadi Ardiny, Amir Mohammad Beigzadeh

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Through the utilization of a combination of various sensors and data fusion methods, the detection of potential nuclear threats can be significantly enhanced by extracting more information from different data. In this research, an experimental and modeling approach was employed to track a radioactive source by combining a surveillance camera and a radiation detector (NaI). To run this experiment, three mobile robots were utilized, with one of them equipped with a radioactive source. An algorithm was developed in identifying the contaminated robot through correlation between camera images and camera data. The computer vision method extracts the movements of all robots in the XY plane coordinate system, and the detector system records the gamma-ray count. The position of the robots and the corresponding count of the moving source were modeled using the MCNPX simulation code while considering the experimental geometry. The results demonstrated a high level of accuracy in finding and locating the target in both the simulation model and experimental measurement. The modeling techniques prove to be valuable in designing different scenarios and intelligent systems before initiating any experiments.

Keywords: nuclear threats, radiation detector, MCNPX simulation, modeling techniques, intelligent systems

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Authors: Javed Mohammed

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As urbanization process has been and will be happening in an unprecedented scale worldwide, strong requirements from academic research and practical fields for smart management and intelligent planning of cities are pressing to handle increasing demands of infrastructure and potential risks of inhabitants agglomeration in disaster management. Geo-spatial data and Geographic Information System (GIS) are essential components for building smart cities in a basic way that maps the physical world into virtual environment as a referencing framework. On higher level, GIS has been becoming very important in smart cities on different sectors. In the digital city era, digital maps and geospatial databases have long been integrated in workflows in land management, urban planning and transportation in government. People have anticipated GIS to be more powerful not only as an archival and data management tool but also as spatial models for supporting decision-making in intelligent cities. The purpose of this project is to offer observations and analysis based on a detailed discussion of Geographic Information Systems( GIS) driven Framework towards the development of Smart and Sustainable Cities through high penetration of Renewable Energy Technologies.

Keywords: digital maps, geo-spatial, geographic information system, smart cities, renewable energy, urban planning

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Authors: Mahdi Motahari, Mojtaba Farzaneh, Ebrahim Sepidbar

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Tracking of moving and flying targets is one of the most important issues in image processing topic. Estimating of trajectory of desired object in short-term and long-term scale is more important than tracking of moving and flying targets. In this paper, a new way of identifying and estimating of future trajectory of a moving ball in long-term scale is estimated by using synthesis and interaction of image processing algorithms including noise removal and image segmentation, Kalman filter algorithm in order to estimating of trajectory of ball in football game in short-term scale and intelligent adaptive neuro-fuzzy algorithm based on time series of traverse distance. The proposed system attain more than 96% identify accuracy by using aforesaid methods and relaying on aforesaid algorithms and data base video in format of synthesis and interaction. Although the present method has high precision, it is time consuming. By comparing this method with other methods we realize the accuracy and efficiency of that.

Keywords: tracking, signal processing, moving targets and flying, artificial intelligent systems, estimating of trajectory, Kalman filter

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Authors: Ilona Buchem, Carolin Gellner

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Over the last few years, the call for the support of senior learners in the development of their digital literacy has become prevalent, mainly due to the progression towards ageing societies paired with advances in digitalisation in all spheres of life, including e-health and electronic patient record (EPA). While major research efforts in supporting senior learners in developing digital literacy have been invested so far in e-learning focusing on knowledge acquisition and cognitive tasks, little research exists in learning models which target virtual mentoring and coaching with the help of pedagogical agents and address the social dimensions of learning. Research from studies with students in the context of formal education has already provided methods for designing intelligent virtual agents in support of personalised learning. However, this research has mostly focused on cognitive skills and has not yet been applied to the context of mentoring/coaching of senior learners, who have different characteristics and learn in different contexts. In this paper, we describe how insights from previous research can be used to develop an intelligent virtual learning coach (agent) for senior learners with a focus on building the social relationship between the agent and the learner and the key task of the agent to socialize learners to the larger context of digital literacy with a focus on electronic health records. Following current approaches to mentoring and coaching, the agent is designed not to enhance and monitor the cognitive performance of the learner but to serve as a trusted friend and advisor, whose role is to provide one-to-one guidance and support sharing of experiences among learners (peers). Based on literature review and synopsis of research on virtual agents and current coaching/mentoring models under consideration of the specific characteristics and requirements of senior learners, we describe the design framework which was applied to design an intelligent virtual learning coach as part of the e-learning system for digital literacy of senior learners in the ePA-Coach project founded by the German Ministry of Education and Research. This paper also presents the results from the evaluation study, which compared the use of the first prototype of the virtual learning coach designed according to the design framework with a voice narration in a multimedia learning environment with senior learners. The focus of the study was to validate the agent design in the context of the persona effect (Lester et al., 1997). Since the persona effect is related to the hypothesis that animated agents are perceived as more socially engaging, the study evaluated possible impacts of agent coaching in comparison with voice coaching on motivation, engagement, experience, and digital literacy.

Keywords: virtual learning coach, virtual mentor, pedagogical agent, senior learners, digital literacy, electronic health records

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Authors: Igor A. Krichtafovitch

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The evolutionary processes are not linear. Long periods of quiet and slow development turn to rather rapid emergences of new species and even phyla. During Cambrian explosion, 22 new phyla were added to the previously existed 3 phyla. Contrary to the common credence the natural selection or a survival of the fittest cannot be accounted for the dominant evolution vector which is steady and accelerated advent of more complex and more intelligent living organisms. Neither Darwinism nor alternative concepts including panspermia and intelligent design propose a satisfactory solution for these phenomena. The proposed hypothesis offers a logical and plausible explanation of the evolutionary processes in general. It is based on two postulates: a) the Biosphere is a single living organism, all parts of which are interconnected, and b) the Biosphere acts as a giant biological supercomputer, storing and processing the information in digital and analog forms. Such supercomputer surpasses all human-made computers by many orders of magnitude. Living organisms are the product of intelligent creative action of the biosphere supercomputer. The biological evolution is driven by growing amount of information stored in the living organisms and increasing complexity of the biosphere as a single organism. Main evolutionary vector is not a survival of the fittest but an accelerated growth of the computational complexity of the living organisms. The following postulates may summarize the proposed hypothesis: biological evolution as a natural life origin and development is a reality. Evolution is a coordinated and controlled process. One of evolution’s main development vectors is a growing computational complexity of the living organisms and the biosphere’s intelligence. The intelligent matter which conducts and controls global evolution is a gigantic bio-computer combining all living organisms on Earth. The information is acting like a software stored in and controlled by the biosphere. Random mutations trigger this software, as is stipulated by Darwinian Evolution Theories, and it is further stimulated by the growing demand for the Biosphere’s global memory storage and computational complexity. Greater memory volume requires a greater number and more intellectually advanced organisms for storing and handling it. More intricate organisms require the greater computational complexity of biosphere in order to keep control over the living world. This is an endless recursive endeavor with accelerated evolutionary dynamic. New species emerge when two conditions are met: a) crucial environmental changes occur and/or global memory storage volume comes to its limit and b) biosphere computational complexity reaches critical mass capable of producing more advanced creatures. The hypothesis presented here is a naturalistic concept of life creation and evolution. The hypothesis logically resolves many puzzling problems with the current state evolution theory such as speciation, as a result of GM purposeful design, evolution development vector, as a need for growing global intelligence, punctuated equilibrium, happening when two above conditions a) and b) are met, the Cambrian explosion, mass extinctions, happening when more intelligent species should replace outdated creatures.

Keywords: supercomputer, biological evolution, Darwinism, speciation

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Authors: France Phiri, Arnold Kanengoni, Dawood Hattas, Khanyisile Mbatha

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A study was carried out to determine seasonal changes in fiber composition and condensed tannin (CT) concentrations in Rhus lancea and Celtis africana and their effects on feed intake and blood metabolites in mixed-feeders. Rhus lancea and C. africana were analysed for dry matter (DM), acid detergent lignin (ADL), acid detergent fiber (ADF), neutral detergent fiber (NDF) and CT concentrations over four seasons; early wet (EWS), late wet (LWS), early dry (EDS) and late dry (LDS). Twelve indigenous male goats were kept in metabolic crates for periods of 21 days per season and fed one of two diet combinations; the test diet comprised R. lancea and C. africana (denoted as BROWSE) and the lucerne diet comprised lucerne (Medicago sativa and concentrates (CON). Feed intake, body weight and blood metabolites were determined in all goats over each study period. Goats fed BROWSE in the EDS, LDS and LWS lost weight while goats fed CON gained weight (P < 0.05). Goats fed CON had higher urea, alkaline phosphatase and gamma-glutamyl transferase concentrations than those fed BROWSE (P < 0.05). Creatinine and cholesterol concentrations in all goats across LWS, EDS and LDS were lower than the normal range, while total protein and globulin concentrations were higher. The goats fed BROWSE had higher creatinine concentrations (P < 0.05) than those fed CON. Cholesterol concentrations were higher (P < 0.05) in goats fed BROWSE than in those on CON fed. It was concluded that goats fed BROWSE lost weight, indicating insufficient nutrients for maintenance requirements.

Keywords: fiber, maintenance, condense tannins, blood metabolites

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Authors: Ajay Agrawal, Pritha Basnet, Achala Thakur, Pappu Rizal, Rubina Rai

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Context: Rapid expulsion of fetus in intrauterine fetal death (IUFD) is usually requested without any medical grounds for it. So; an efficient, safe method for induction of labor (IOL) is required. Objective: To determine if pre-treatment with mifepristone followed by IOL with misoprostol in late IUFD is more efficacious. Methods: We conducted a randomized controlled trial in 100 patients. Group-A women received single oral dose of 200 mg mifepristone, followed by induction with vaginal misoprostol after 24-hour. Group-B women were induced only with vaginal misoprostol. In each group 5 dose of misoprostol was used 4 hourly. If first cycle was unsuccessful, after break of 12 hour, second course of misoprostol was started. The primary outcome was a measure of induction to delivery time and vaginal delivery within 24 hours. Secondary outcome was to measure need of oxytocin and complications. Results: Maternal age, parity and period of gestation were comparable between groups. Number of misoprostol dose needed in group A was significantly less than group B. Mann Whitney U test showed, women in group A had significantly earlier onset of labor, however total induction to delivery interval was not significant. In group-A, 85.7% delivered within 24 hours of first dose of misoprostol while in group-B 70% delivered within 24 hour (p=0.07). More women in Group B required oxytocin. Conclusion: Pretreatment with mifepristone before IOL following late IUFD is an effective and safe regimen. It appears to shorten the duration of induction to onset of labor.

Keywords: induction of labor, intrauterine fetal death, mifepristone, misoprostol

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Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Shengbiao Wu, Weihua Cao, Min Wu, Can Liu

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This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision.

Keywords: microwave filter, scattering parameter, coupling matrix, intelligent tuning

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Taha Bensiradj, Samira Moussaoui

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Hybrid Sensors and Vehicular Networks (HSVN), represent a hybrid network, which uses several generations of Ad-Hoc networks. It is used especially in Intelligent Transport Systems (ITS). The HSVN allows making collaboration between the Wireless Sensors Network (WSN) deployed on the border of the road and the Vehicular Network (VANET). This collaboration is defined by messages exchanged between the two networks for the purpose to inform the drivers about the state of the road, provide road safety information and more information about traffic on the road. Moreover, this collaboration created by HSVN, also allows the use of a network and the advantage of improving another network. For example, the dissemination of information between the sensors quickly decreases its energy, and therefore, we can use vehicles that do not have energy constraint to disseminate the information between sensors. On the other hand, to solve the disconnection problem in VANET, the sensors can be used as gateways that allow sending the messages received by one vehicle to another. However, because of the short communication range of the sensor and its low capacity of storage and processing of data, it is difficult to ensure the exchange of road messages between it and the vehicle, which can be moving at high speed at the time of exchange. This represents the time where the vehicle is in communication range with the sensor. This work is the proposition of a communication protocol between the sensors and the vehicle used in HSVN. The latter has as the purpose to ensure the exchange of road messages in the available time of exchange.

Keywords: HSVN, ITS, VANET, WSN

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Authors: Bilal Wadood, Suleman Khan, Sajjad Ahmed

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The Late Cretaceous strata (Mughal Kot Formation) exposed in Central Indus Basin, Pakistan is evaluated for establishing sequence stratigraphic framework and potential of hydrocarbon accumulation. The petrographic studies and SEM analysis were carried out to infer the hydrocarbon potential of the rock unit. The petrographic details disclosed 4 microfacies including Pelagic Mudstone, OrbitoidalWackestone, Quartz Arenite, and Quartz Wacke. The lowermost part of the rock unit consists of OrbitoidalWackestone which shows deposition in the middle shelf environment. The Quartz Arenite and Quartz Wacke suggest deposition on the deep slope settings while the Pelagic Mudstone microfacies point toward deposition in the distal deep marine settings. Based on the facies stacking patterns and cyclicity in the chronostratigraphic context, the strata is divided into two 3rd order cycles. One complete sequence i.e Transgressive system tract (TST), Highstand system tract (HST) and Lowstand system tract (LST) are again replaced by another Transgressive system tract and Highstant system tract with no markers of sequence boundary. The LST sands are sandwiched between TST and HST shales but no potential porosity/permeability values have been determined. Microfacies and SEM studies revealed very fewer chances for hydrocarbon accumulation and overall reservoir potential is characterized as low.

Keywords: cycle, deposition, microfacies, reservoir

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Authors: Akulov M. A., Orlova O. R., Zaharov V. O., Tomskij A. A.

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Introduction: One of the common postoperative complications of posterior cranial fossa (PCF) and cerebello-pontine angle tumor treatment is a facial nerve palsy, which leads to multiple and resistant to treatment impairments of mimic muscles structure and functions. After 4-6 months after facial nerve palsy with insufficient therapeutic intervention patients develop a postparalythic syndrome, which includes such symptoms as mimic muscle insufficiency, mimic muscle contractures, synkinesis and spontaneous muscular twitching. A novel method of treatment is the use of a recent local neuromuscular blocking agent– botulinum toxin A (BTA). Experience of BTA treatment enables an assumption that it can be successfully used in late facial nerve palsy complications to significantly increase quality of life of patients. Study aim. To evaluate the efficacy of botulinum toxin A (BTA) (Xeomin) treatment in patients with late facial nerve palsy complications. Patients and Methods: 31 patients aged 27-59 years 6 months after facial nerve palsy development were evaluated. All patients received conventional treatment, including massage, movement therapy etc. Facial nerve palsy developed after acoustic nerve tumor resection in 23 (74,2%) patients, petroclival meningioma resection – in 8 (25,8%) patients. The first group included 17 (54,8%) patients, receiving BT-therapy; the second group – 14 (45,2%) patients continuing conventional treatment. BT-injections were performed in synkinesis or contracture points 1-2 U on injured site and 2-4 U on healthy side (for symmetry). Facial nerve function was evaluated on 2 and 4 months of therapy according to House-Brackman scale. Pain syndrome alleviation was assessed on VAS. Results: At baseline all patients in the first and second groups demonstrated аpostparalytic syndrome. We observed a significant improvement in patients receiving BTA after only one month of treatment. Mean VAS score at baseline was 80,4±18,7 and 77,9±18,2 in the first and second group, respectively. In the first group after one month of treatment we observed a significant decrease of pain syndrome – mean VAS score was 44,7±10,2 (р<0,01), whereas in the second group VAS score was as high as 61,8±9,4 points (p>0,05). By the 3d month of treatment pain syndrome intensity continued to decrease in both groups, but, the first group demonstrated significantly better results; mean score was 8,2±3,1 and 31,8±4,6 in the first and second group, respectively (р<0,01). Total House-Brackman score at baseline was 3,67±0,16 in the first group and 3,74±0,19 in the second group. Treatment resulted in a significant symptom improvement in the first group, with no improvement in the second group. After 4 months of treatment House-Brockman score in the first group was 3,1-fold lower, than in the second group (р<0,05). Conclusion: Botulinum toxin injections decrease postparalytic syndrome symptoms in patients with facial nerve palsy.

Keywords: botulinum toxin, facial nerve palsy, postparalytic syndrome, synkinesis

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Authors: Meltem Eryılmaz

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With the emergence of the COVID-19 infection outbreak, the socio-cultural, political, economic, educational systems dynamics of the world have gone through a major change, especially in the educational field, specifically dentistry preclinical education, where the students must have a certain amount of real-time experience in endodontics and other various procedures. The totality of the digital and physical elements that make our five sense organs feel as if we really exist in a virtual world is called virtual reality. Virtual reality, which is very popular today, has started to be used in education. With the inclusion of developing technology in education and training environments, virtual learning platforms have been designed to enrich students' learning experiences. The field of health is also affected by these current developments, and the number of virtual reality applications developed for students studying dentistry is increasing day by day. The most widely used tools of this technology are virtual reality glasses. With virtual reality glasses, you can look any way you want in a world designed in 3D and navigate as you wish. With this project, solutions that will respond to different types of dental practices of students who study dentistry with virtual reality applications are produced. With this application, students who cannot find the opportunity to work with patients in distance education or who want to improve themselves at home have unlimited trial opportunities. Unity 2021, Visual Studio 2019, Cardboard SDK are used in the study.

Keywords: dentistry, intelligent tutoring system, virtual reality, online learning, COVID-19

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Authors: Maria Magdy Danial Riad

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The objective of this study was to investigate the effect of different protein sources with different degradability ratios during late gestation of ewes on colostrum composition and its IgG concentration, body weight change of dams, and birth weight of their lambs. Objectives: 35 multiparous native crossbred ewes (BW= 59±2.5kg) were randomly allocated to five dietary treatments (7 ewes / treatment) for 2 months prior to lambing. Methods: Experimental diets were isonitrogenous (12.27% CP) and isocaloric (2.22 Mcal ME/kg DM). In diet I (the control), solvent extract soybeans (SESM 33% RUP of CP), II feed grade urea (FGU 31% RUP), III slow release urea (SRU 31% RUP). As sources of undegradable protein, extruded expeller SBM-EESM 40 (37% RUP) and extruded expeller SBM-EESM 60 (41% RUP) were used in groups IV and V, respectively. Results showed no significant effect on feed intake, crude protein (CP), metabolizable energy (ME), and body condition score (BCS). Ewes fed the 37% RUP diet gained more (p<0.05) weight compared with ewes fed the 31% RUP diet (5.62 vs. 2.5kg). Ewes in EESM 60 had the highest levels of fat, protein, total solid, solid not fat, and immunoglobulin and the lowest in urea N content (P< 0.05) in colostrum during the first 24hrs after lambing. Conclusions: Protein source and RUP levels in ewes’ diets had no significant effect (P< 0.05) on lambs’ birth weight and ewes' blood biochemical parameters. Increasing the RUP content of diet during late gestation resulted in an increase in colostrum constituents and its IgG level but had no effect on ewes’ performance and their lambs’ outcome.

Keywords: colostrum, ewes, lambs output, pregnancy, undegradable protein

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Jelili Kunle Adedeji, Toeb Akorede Akinbola

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The problem of vehicle mishap as a result of miscalculation, recklessness, or malfunction of some part in a vehicle is acknowledged to be a global issue. In most of the cases, it results into death or life injuries, all over the world; the issue becomes a nightmare to the stakeholders on how to curb mishaps on our roads due to these endemic factors. Hence this research typically examined the design of a device, specifically for learners that can lead to a society of intelligent vehicles (traffic) without withdrawing the driving authority from them, unlike pre-existing systems. Though ILASS shears a lot of principle with existing advance drivers assistance systems, yet there are two fundamental differences between ILASS system and existing systems. Firstly ILASS is meant to accept continuous input from the throttle at all time such that the devices will not constraint the driving process unnecessarily and ensure a change of speed at any point in time. Secondly, it made use of a variable threshold distance between the host vehicle and front vehicle which can be set by the host driver under the constraint of road maintenance agency, who communicates the minimum possible threshold for a different lane to the host vehicle. The results obtained from the simulation of the ILASS system concluded that ILASS is a good solution to road accidents, particularly road accident which occurs as a result of driving at high speed.

Keywords: front-vehicle, host-speed, threshold-distance, ILASS

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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Authors: Karine Pimenta Teixeira, Jessica Flores, Isadora PerdigãO Rocha, Leticia De Sá Carneiro, Mahsa Kamali, Ali Ghahremaninezhad

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The advent of nanotechnology has enabled innovative solutions towards improving the behavior of infrastructure materials. Nanomaterials have the potential to revolutionize the construction industry by improving the performance and durability of construction materials, as well as imparting new functionalities to these materials. Due to variability in the environmental temperature during mixing and curing of cementitious materials in practice, it is important to understand how curing temperature influences the behavior of cementitious materials. In addition, high temperature curing is relevant in applications such as oil well cement and precast industry. Knowledge of the influence of temperature on the performance of cementitious materials modified with nanoparticles is important in the nanoengineering of cementitious materials in applications such as oil well cement and precast industry. This presentation aims to investigate the influence of temperature on the hydration, mechanical properties and durability of cementitious materials modified with TiO2 nanoparticles. It was found that temperature improved the early hydration. The cement pastes cured at high temperatures showed an increase in the compressive strength at early age but the strength gain decreased at late ages. The electrical resistivity of the cement pastes cured at high temperatures was shown to decrease more noticeably at late ages compared to that of the room temperature cured cement paste. SEM examination indicated that hydration product was more uniformly distributed in the microstructure of the cement paste cured at room temperature compared to the cement pastes cured at high temperature.

Keywords: cement paste, nanoparticles, temperature, hydration

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Authors: Ewa Krasnodębska

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The article presents the costs and income maize cultivation for grain four selected varieties with different numbers of FAO in 2013-2014. Results of the experiments are derived from a field experiment conducted in indywidulnym farm specializing in the production plant located in the eastern part of Mazowieckie voivodship. The experiment examined the profitability of four varieties of maize cultivation: medium early: P8400 (FAO 240) and P8589 (FAO 250), and an average of late: PR38N86 (FAO 270) and P9027 (FAO 260). In order to evaluate the profitability of grain maize production was calculated income from 1 ha of crops in zł and profitability index taking into account the direct payments up to 1 ha. Analyzing the value of crop production can be concluded that the value of the total production of each variety was very much varied and very much depend on the sales price and yield of maize obtained from 1 ha of cultivation. The largest average seed yield of two years at a moisture content of 15% was achieved in a variety PR38N86, which amounted to 12.1 t / ha and the lowest in the variety P8400 - 9.8 t / ha. Income from 1 ha of crops including EU subsidies ranged from 4916.4 zł / ha in 2013 for variety and only 528.7 PR38N86 zł / ha for a variety of P8400 in 2014. Profitability index reached the highest average late PR38N86 variety of FAO 290 over the entire two-year period under study, and the lowest rate of profitability achieved P8400 medium early variety of FAO 240. The profitability of production ranged from 8964.0 zł / ha in 2013 for a variety of PR38N86 to 5616.0 zł / ha for a variety of P8400 in 2014. Cultivation of maize for grain production is attractive and does not require large amounts of work, but its economic rationale is based primarily on the resulting yield and the price of buying.

Keywords: corn, grain, income, profitability

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: R. Kanthavel, R. Yogesh Kumar, T. Narendrakumar, B. Santhosh, S. Surya Prakash

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This paper primarily focuses on developing an affordable personal assistant and the implementation of it in the field of Artificial Intelligence (AI) to create a virtual assistant/friend. The problem in existing home automation techniques is that it requires the usage of exact command words present in the database to execute the corresponding task. Our proposed work is ATOM a.k.a ‘Artificial intelligence Talking Omnipresent Machine’. Our inspiration came from an unlikely source- the movie ‘Iron Man’ in which a character called J.A.R.V.I.S has omnipresence, and device controlling capability. This device can control household devices in real time and send the live information to the user. This device does not require the user to utter the exact commands specified in the database as it can capture the keywords from the uttered commands, correlates the obtained keywords and perform the specified task. This ability to compare and correlate the keywords gives the user the liberty to give commands which are not necessarily the exact words provided in the database. The proposed work has a higher flexibility (due to its keyword extracting ability from the user input) comparing to the existing work Intelligent Home automation System (IHAS), is more accurate, and is much more affordable as it makes use of WI-FI module and raspberry pi 2 instead of ZigBee and a computer respectively.

Keywords: home automation, speech recognition, voice control, personal assistant, artificial intelligence

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Houda Khaled, Frederic Boulvain, Fredj Chaabani

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This paper focuses on the sedimentological and mineralogical studies of Aptian series outcrops in the Serdj and Bellouta Mountain situated in north-central Tunisia. In the Serdj Mountain, the Aptian series is about 590 meters thick and it is defined by tow formations corresponding respectively to the Sidi Hamada formation (Barremian-Gragasian) and the Serdj formation (Middle Gragasian-Late Clansaysian). This later is consisting of five limestones sequences separated by marly levels limestones associated to some siltstones bed. The Bellouta section is especially composed of carbonate rocks and it is attributed to the Middle Gragasian - Late Clansaysian. These sections are studied in detail regarding lithology, micropaleontology, microfacies, magnetic susceptibility and mineralogical composition in order to provide new insights into the paleoenvironmental evolution and paleoclimatological implications during Aptian. The following facies associations representing different ramp palaeoenvironments have been identified: mudstone-wackestone outer ramp facies; skeletal grainstone-packstone mid-ramp facies, packstone-grainstone inner-ramp facies which include a variety of organisms such as ooliths, rudists ostracods associated to athor bioclats. The coastal facies is especially defined by a mudstone -wackestone texture coastal rich with miliolidea and orbitolines. The magnetic susceptibility (Xin) of all samples was compared with the lithological and microfacies variation. The MS curves show that the high values are correlated with the distal facies and the low values are registred in the coastal environment. The X-ray diffractometer analysis show the presence of kaolinite and illite.

Keywords: Aptian, Serdj formation, mineralogy, petrography

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Authors: Aydin M. Torkabadi, Ehsan Pourjavad

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A decade after the introduction of Lean in Saskatchewan’s public healthcare system, its effectiveness remains a controversial subject among health researchers, workers, managers, and politicians. Therefore, developing a framework to quantitatively assess the Lean achievements is significant. This study investigates the success of initiatives across Saskatchewan health regions by recognizing the Lean healthcare criteria, measuring the success levels, comparing the regions, and identifying the areas for improvements. This study proposes an integrated intelligent computing approach by applying Fuzzy Inference System (FIS) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FIS is used as an efficient approach to assess the Lean healthcare criteria, and TOPSIS is applied for ranking the values in regards to the level of leanness. Due to the innate uncertainty in decision maker judgments on criteria, principals of the fuzzy theory are applied. Finally, FIS-TOPSIS was established as an efficient technique in determining the lean merit in healthcare systems.

Keywords: lean healthcare, intelligent computing, fuzzy inference system, healthcare evaluation, technique for order of preference by similarity to ideal solution, multi-criteria decision making, MCDM

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Devendra Gupta, Sushilk K. Agarwal, Amit Kesari, P. K. Singh

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Objectives: Pediatric patients often may experience persistent respiratory failure that requires tracheostomy placement in Pediatric ICU. We have designed a technique of tracheostomy in pediatric patients with S-shaped incision on the tracheal wall with higher success rate and lower complication rate. Technique: Following general anesthesia and positioning of the patient, the trachea was exposed in midline by a vertical skin incision. In order to make S-shaped tracheostoma, second tracheal ring was identified. The conventional vertical incision was made in second tracheal ring and then extended at both its ends laterally in the inter-cartilaginous space parallel to the tracheal cartilage in the opposite direction to make the incision S-shaped. The trachea was dilated with tracheal dilator and appropriate size of tracheostomy tube was then placed into the trachea. Results: S-shaped tracheostomy was performed in 20 children with mean age of 6.25 years (age range is 2-7) requiring tracheostomy placement. The tracheostomy tubes were successfully placed in all the patients in single attempt. There was no incidence of significant intra-operative bleeding, subcutaneous emphysema, vocal cord palsy or pneumothorax. Two patients developed pneumonia and expired within a year. However, there was no incidence of tracheo-esophageal fistula, suprastomal collapse or difficulty in decannulation on one year of follow up related to our technique. One patient developed late trachietis managed conservatively. Conclusion: S-shaped tracheoplasty was associated with high success rate, reduced risk of the early and late complications in pediatric patients requiring tracheostomy.

Keywords: peatrics, tracheostomy, ICU, tracheostoma

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Authors: Xiao Wang, Qinge Zhang

Abstract:

Background: This randomized, open-label study examined the therapeutic effects of computerized cognitive training (CCT) combined with selective serotonin reuptake inhibitors (SSRIs) on cognitive impairment among patients with late-life depression (LLD). Method: Study data were collected from May 5, 2021, to April 21, 2023. Outpatients who met diagnostic criteria for major depressive disorder according to the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria (i.e., a total score on the 17-item Hamilton Depression Rating Scale (HAMD-17) ≥ 18 and a total score on the Montreal Cognitive Assessment scale (MOCA) <26) were randomly assigned to receive up to 12 weeks of CCT and SSRIs treatment (n=57) or SSRIs and Control treatment (n=61). The primary outcome was the change in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) scores from baseline to week 12 between the two groups. The secondary outcomes included changes in the HAMD-17 score, Hamilton Anxiety Scale (HAMA) score and Neuropsychiatric Inventory (NPI) score. Mixed model repeated measures (MMRM) analysis was performed on modified intention-to-treat (mITT) and completer populations. Results: The full analysis set (FAS) included 118 patients (CCT and SSRIs group, n=57; SSRIs and Control group, n =61). Over the 12-week study period, the reduction in the ADAS-cog total score was significant (P < 0.001) in both groups, while MMRM analysis revealed a significantly greater reduction in cognitive function (ADAS-cog total scores) from baseline to posttreatment in the CCT and SSRIs group than in the SSRI and Control group [(F (1,115) =13.65, least-squares mean difference [95% CI]: −2.77 [−3.73, −1.81], p<0.001)]. There were significantly greater improvements in depression symptoms (measured by the HAMD-17) in the CCT and SSRIs group than in the control group [MMRM, estimated mean difference (SE) between groups −3.59 [−5.02, −2.15], p < 0.001]. The least-squares mean changes in the HAMA scores and NPI scores between baseline and week 8 were greater in the CCT and SSRIs group than in the control group (all P < 0.05). There was no significant difference between groups on response rates and remission rates by using the last-observation-carried-forward (LOCF) method (all P > 0.05). The most frequent adverse events (AEs) in both groups were dry mouth, somnolence, and constipation. There was no significant difference in the incidence of adverse events between the two groups. Conclusions: CCT combined with SSRIs was efficacious and well tolerated in LLD patients with cognitive impairment.

Keywords: late-life depression, cognitive function, computerized cognitive training, SSRIs

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