Search results for: brain machine interface (BMI)

Authors: Alhusein Almahjoub, Dongyu Qiu

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This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

Procedia PDF Downloads 133

Authors: Md Yeasin, Ranjit Kumar Paul

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In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity.

Keywords: agriculture, casual inference, machine learning, recommendation system

Procedia PDF Downloads 70

Authors: Z. Ezzouine, A. Nakheli

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This paper presents a high position electromagnetic sensor system (HPESS) that is applicable for moving object detection. The authors have developed a high-performance position sensor prototype dedicated to students’ laboratory. The challenge was to obtain a highly accurate and real-time sensor that is able to calculate position, length or displacement. An electromagnetic solution based on a two coil induction principal was adopted. The HPESS converts mechanical motion to electric energy with direct contact. The output signal can then be fed to an electronic circuit. The voltage output change from the sensor is captured by data acquisition system using LabVIEW software. The displacement of the moving object is determined. The measured data are transmitted to a PC in real-time via a DAQ (NI USB -6281). This paper also describes the data acquisition analysis and the conditioning card developed specially for sensor signal monitoring. The data is then recorded and viewed using a user interface written using National Instrument LabVIEW software. On-line displays of time and voltage of the sensor signal provide a user-friendly data acquisition interface. The sensor provides an uncomplicated, accurate, reliable, inexpensive transducer for highly sophisticated control systems.

Keywords: electromagnetic sensor, accurately, data acquisition, position measurement

Procedia PDF Downloads 273

Authors: Mark McGuire, Eric Shannon, John Parmigiani

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Timber harvesting increasingly involves mechanized equipment. This has increased the efficiency of harvesting, but has also introduced worker-safety concerns. One such concern arises from the use of harvesters. During operation, harvesters subject saw chain to large dynamic mechanical stresses. These stresses can, under certain conditions, cause the saw chain to fracture. The high speed of harvester saw chain can cause the resulting open chain loop to fracture a second time due to the dynamic loads placed upon it as it travels through space. If a second fracture occurs, it can result in a projectile consisting of one-to-several chain links. This projectile is referred to as a chain shot. It has speeds similar to a bullet but typically has greater mass and is a significant safety concern. Numerous examples exist of chain shots penetrating bullet-proof barriers and causing severe injury and death. Improved harvester-cab barriers can help prevent injury however a comprehensive scientific understanding of chain shot is required to consistently reduce or prevent it. Obtaining this understanding requires a test machine with the capability to cause chain shot to occur under carefully controlled conditions and accurately measure the response. Worldwide few such test machine exist. Those that do focus on validating the ability of barriers to withstand a chain shot impact rather than obtaining a scientific understanding of the chain shot event itself. The purpose of this paper is to describe the design, fabrication, and use of a test machine capable of a comprehensive scientific investigation of chain shot. The capabilities of this machine are to test all commercially-available saw chains and bars at chain tensions and speeds meeting and exceeding those typically encountered in harvester use and accurately measure the corresponding key technical parameters. The test machine was constructed inside of a standard shipping container. This provides space for both an operator station and a test chamber. In order to contain the chain shot under any possible test conditions, the test chamber was lined with a base layer of AR500 steel followed by an overlay of HDPE. To accommodate varying bar orientations and fracture-initiation sites, the entire saw chain drive unit and bar mounting system is modular and capable of being located anywhere in the test chamber. The drive unit consists of a high-speed electric motor with a flywheel. Standard Ponsse harvester head components are used to bar mounting and chain tensioning. Chain lubrication is provided by a separate peristaltic pump. Chain fracture is initiated through ISO standard 11837. Measure parameters include shaft speed, motor vibration, bearing temperatures, motor temperature, motor current draw, hydraulic fluid pressure, chain force at fracture, and high-speed camera images. Results show that the machine is capable of consistently causing chain shot. Measurement output shows fracture location and the force associated with fracture as a function of saw chain speed and tension. Use of this machine will result in a scientific understanding of chain shot and consequently improved products and greater harvester operator safety.

Keywords: chain shot, safety, testing, timber harvesters

Procedia PDF Downloads 139

Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Alok Srivastava, Vidit Gupta, Aparna Singh, Chandra Sekher Yerramalli

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Carbon-fiber epoxy composites show extremely high modulus and strength in the uniaxial direction. However, they are prone to fail under low load in transverse direction due to the weak nature of the interface between the carbon-fiber and epoxy. In the current study, we have coated graphene nano-platelets (GNPs) on the carbon-fibers in an attempt to strengthen the interface/interphase between the fiber and the matrix. Vacuum Assisted Resin Transfer Moulding (VARTM) has been used to make the laminates of eight cross-woven fabrics. Tensile, flexural and fracture toughness tests have been performed on pristine carbon-fiber composite (P-CF), GNP coated carbon-fiber composite (GNP-CF) and functionalized-GNP coated carbon-fiber composite (F-GNP-CF). The tensile strength and flexural strength values are pretty similar for P-CF and GNP-CF. The micro-structural examination of the GNP coated carbon-fibers, as well as the fracture surfaces, have been carried out using scanning electron microscopy (SEM). The micrographs reveal the deposition of GNPs onto the carbon fibers in transverse and longitudinal direction. Fracture surfaces show the debonding and pull outs of the carbon fibers in P-CF and GNP-CF samples.

Keywords: carbon fiber, graphene nanoplatelets, strength, VARTM, Vacuum Assisted Resin Transfer Moulding

Procedia PDF Downloads 134

Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Helen Zhang

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Accurate segmentation of the airways from CT scans is crucial for early diagnosis of lung cancer. However, the existing airway segmentation algorithms often rely on thin-slice CT scans, which can be inconvenient and costly. This paper presents a set of machine learning-based 3D super-resolution algorithms along heterogeneous dimensions to improve the resolution of thicker CT scans to reduce the reliance on thin-slice scans. To evaluate the efficacy of the super-resolution algorithms, quantitative assessments using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) were performed. The impact of super-resolution on airway segmentation accuracy is also studied. The proposed approach has the potential to make airway segmentation more accessible and affordable, thereby facilitating early diagnosis and treatment of lung cancer.

Keywords: 3D super-resolution, airway segmentation, thin-slice CT scans, machine learning

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Authors: Hanan. Al Chaghouri, Mohammad Azad Malik, P. John Thomas, Paul O’Brien

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The process of assembling metal nanoparticles at the interface of two liquids has received a great deal of attention over the past few years due to a wide range of important applications and their unusual properties as compared to bulk materials. We present a low cost, simple and cheap synthesis of metal nanoparticles, core/shell structures and semiconductors followed by assembly of these particles between immiscible liquids. The aim of this talk is divided to three parts: Firstly, to describe the achievement of a closed loop recycling for producing cadmium sulfide as powders and/or nanostructured thin films for solar cells or other optoelectronic devices applications by using a different chain length of commercially available secondary amines of dithiocarbamato complexes. The approach can be extended to other metal sulfides such as those of Zn, Pb, Cu, or Fe and many transition metals and oxides. Secondly, to synthesis significantly cheaper magnetic particles suited for the mass market. Ni/NiO nanoparticles with ferromagnetic properties at room temperature were among the smallest and strongest magnets (5 nm) were made in solution. The applications of this work can be to produce viable storage devices and the other possibility is to disperse these nanocrystals in solution and use it to make ferrofluids which have a number of mature applications. The third part is about preparing and assembling of submicron silver, cobalt and nickel particles by using polyol methods and liquid/liquid interface, respectively. Coinage metals like gold, copper and silver are suitable for plasmonic thin film solar cells because of their low resistivity and strong interactions with visible light waves. Silver is the best choice for solar cell application since it has low absorption losses and high radiative efficiency compared to gold and copper. Assembled cobalt and nickel as films are promising for spintronic, magnetic and magneto-electronic and biomedics.

Keywords: metal nanoparticles, core/shell structures and semiconductors, ferromagnetic properties, closed loop recycling, liquid/liquid interface

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Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui

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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.

Keywords: EEG, ICA, SVM, wavelet

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Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

Procedia PDF Downloads 118

Authors: Mathula Thangarajh

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Duchenne muscular dystrophy (DMD) is a severe form of X-linked muscular dystrophy caused by mutations in the dystrophin gene resulting in progressive skeletal muscle weakness. Boys with DMD also have significant cognitive disabilities. The intelligence quotient of boys with DMD, compared to peers, is approximately one standard deviation below average. Detailed neuropsychological testing has demonstrated that boys with DMD have a global developmental impairment, with verbal memory and visuospatial skills most significantly affected. Furthermore, the total brain volume and gray matter volume are lower in children with DMD compared to age-matched controls. These results are suggestive of a significant structural and functional compromise to the developing brain as a result of absent dystrophin protein expression. There is also some genetic evidence to suggest that mutations in the 3’ end of the DMD gene are associated with more severe neurocognitive problems. Our working hypothesis is that (i) boys with DMD do not make gains in neurodevelopmental skills compared to typically developing children and (ii) women carriers of DMD mutations may have subclinical cognitive deficits. We also hypothesize that there may be an intergenerational vulnerability of cognition, with boys of DMD-carrier mothers being more affected cognitively than boys of non-DMD-carrier mothers. The objectives of this study are: 1. Assess the neurodevelopment in boys with DMD at 4-time points and perform baseline neuroradiological assessment, 2. Assess cognition in biological mothers of DMD participants at baseline, 3. Assess possible correlation between DMD mutation and cognitive measures. This study also explores functional brain abnormalities in people with DMD by exploring how regional and global connectivity of the brain underlies executive function deficits in DMD. Such research can contribute to a better holistic understanding of the cognition alterations due to DMD and could potentially allow clinicians to create better-tailored treatment plans for the DMD population. There are four study visits for each participant (baseline, 2-4 weeks, 1 year, 18 months). At each visit, the participant completes the NIH Toolbox Cognition Battery, a validated psychometric measure that is recommended by NIH Common Data Elements for use in DMD. Visits 1, 3, and 4 also involve the administration of the BRIEF-2, ABAS-3, PROMIS/NeuroQoL, PedsQL Neuromuscular module 3.0, Draw a Clock Test, and an optional fMRI scan with the N-back matching task. We expect to enroll 52 children with DMD, 52 mothers of children with DMD, and 30 healthy control boys. This study began in 2020 during the height of the COVID-19 pandemic. Due to this, there were subsequent delays in recruitment because of travel restrictions. However, we have persevered and continued to recruit new participants for the study. We partnered with the Muscular Dystrophy Association (MDA) and helped advertise the study to interested families. Since then, we have had families from across the country contact us about their interest in the study. We plan to continue to enroll a diverse population of DMD participants to contribute toward a better understanding of Duchenne Muscular Dystrophy.

Keywords: neurology, Duchenne muscular dystrophy, muscular dystrophy, cognition, neurodevelopment, x-linked disorder, DMD, DMD gene

Procedia PDF Downloads 86

Authors: Fan Li, Chun-Hsien Chen, Li Pheng Khoo

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Operators of vessel traffic service (VTS) center provides three different types of services; namely information service, navigational assistance and traffic organization to vessels. To provide these services, operators monitor vessel traffic through computer interface and provide navigational advice based on the information integrated from multiple sources, including automatic identification system (AIS), radar system, and closed circuit television (CCTV) system. Therefore, this information is crucial in VTS operation. However, what information the VTS operator actually need to efficiently and properly offer services is unclear. The aim of this study is to investigate into information requirements for VTS operation. To achieve this aim, field observation was carried out to elicit the information requirements for VTS operation. The study revealed that the most frequent and important tasks were handling arrival vessel report, potential conflict control and abeam vessel report. Current location and vessel name were used in all tasks. Hazard cargo information was particularly required when operators handle arrival vessel report. The speed, the course, and the distance of two or several vessels were only used in potential conflict control. The information requirements identified in this study can be utilized in designing a human-computer interface that takes into consideration what and when information should be displayed, and might be further used to build the foundation of a decision support system for VTS.

Keywords: vessel traffic service, information requirements, hierarchy task analysis, field observation

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Authors: Abir Hmida, Nihel Chekir, Ammar Ben Brahim

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In the present paper, we investigate the feasibility of a thermal solar driven cold room in Gabes, southern region of Tunisia. The cold room of 109 m³ is refrigerated using an ammonia absorption machine. It is destined to preserve dates during the hot months of the year. A detailed study of the cold room leads previously to the estimation of the cooling load of the proposed storage room in the operating conditions of the region. The next step consists of the estimation of the required heat in the generator of the absorption machine to ensure the desired cold temperature. A thermodynamic analysis was accomplished and complete description of the system is determined. We propose, here, to provide the needed heat thermally from the sun by using vacuum tube collectors. We found that at least 21m² of solar collectors are necessary to accomplish the work of the solar cold room.

Keywords: absorption, ammonia, cold room, solar collector, vacuum tube

Procedia PDF Downloads 153

Authors: Nuno Biga

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Artificial Intelligence (AI) allows machines to interpret information and learn from context analysis, giving them the ability to make predictions adjusted to each specific situation. In addition to learning by performing deterministic and probabilistic calculations, the 'artificial brain' also learns through information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) that provides users with useful suggestions, namely to pursue the following operations: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time the bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed in a pilot project. Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of this information is materialised in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" that players can use during the Game. Each participant in the Virtual Assisted-BIGAMES permanently asks himself about the decisions he should make during the game in order to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, and as the participants gain a better understanding of the game, they will more easily dispense with the VA's recommendations and rely more on their own experience, capability, and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator (Serious Game Controller) is responsible for supporting the players with further analysis and the recommended action may be (or not) aligned with the previous recommendations of the VA. All the information should be jointly analysed and assessed by each player, who are expected to add “Emotional Intelligence”, a component absent from the machine learning process.

Keywords: artificial intelligence (AI), gamification, key performance indicators (KPI), machine learning, management simulators, serious games, virtual assistant

Procedia PDF Downloads 86

Authors: Yuko Ishiwaka, Tomohiro Yoshida, Tadateru Itoh

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Human makes preparation called readiness potential unconsciously (RP) before awareness of their own decision. For example, when recognizing a button and pressing the button, the RP peaks are observed 200 ms before the initiation of the movement. It has been known that the preparatory movements are acquired before actual movements, but it has not been still well understood how humans can obtain the RP during their growth. On the proposition of why the brain must respond earlier, we assume that humans have to adopt the dangerous environment to survive and then obtain the behavior to cover the various time lags distributed in the body. Without RP, humans cannot take action quickly to avoid dangerous situations. In taking action, the brain makes decisions, and signals are transmitted through the Spinal Cord to the muscles to the body moves according to the laws of physics. Our research focuses on the time lag of the neuron signal transmitting from a brain to muscle via a spinal cord. This time lag is one of the essential factors for readiness potential. We propose a firing pattern controller model of a motor nerve system considered innervation and jitter, which produces time lag. In our simulation, we adopt innervation and jitter in our proposed muscle-skeleton model, because these two factors can create infinitesimal time lag. Q10 Hodgkin Huxley model to calculate action potentials is also adopted because the refractory period produces a more significant time lag for continuous firing. Keeping constant power of muscle requires cooperation firing of motor neurons because a refractory period stifles the continuous firing of a neuron. One more factor in producing time lag is slow or fast-twitch. The Expanded Hill Type model is adopted to calculate power and time lag. We will simulate our model of muscle skeleton model by controlling the firing pattern and discuss the relationship between the time lag of physics and neurons. For our discussion, we analyze the time lag with our simulation for knee bending. The law of inertia caused the most influential time lag. The next most crucial time lag was the time to generate the action potential induced by innervation and jitter. In our simulation, the time lag at the beginning of the knee movement is 202ms to 203.5ms. It means that readiness potential should be prepared more than 200ms before decision making.

Keywords: firing patterns, innervation, jitter, motor nerve system, readiness potential

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Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Shatha Ghareeb, Rawaa Al-Jumeily, Thar Baker

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In Abu Dhabi, there are many different education curriculums where sector of private schools and quality assurance is supervising many private schools in Abu Dhabi for many nationalities. As there are many different education curriculums in Abu Dhabi to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. In fact, each curriculum has a different number of years, assessment techniques, reassessment rules, and exam boards. Currently, students that transfer curriculums are not being placed in the right year group due to different start and end dates of each academic year and their date of birth for each year group is different for each curriculum and as a result, we find students that are either younger or older for that year group which therefore creates gaps in their learning and performance. In addition, there is not a way of storing student data throughout their academic journey so that schools can track the student learning process. In this paper, we propose to develop a computational framework applicable in multicultural countries such as UAE in which multi-education systems are implemented. The ultimate goal is to use cloud and fog computing technology integrated with Artificial Intelligence techniques of Machine Learning to aid in a smooth transition when assigning students to their year groups, and provide leveling and differentiation information of students who relocate from a particular education curriculum to another, whilst also having the ability to store and access student data from anywhere throughout their academic journey.

Keywords: admissions, algorithms, cloud computing, differentiation, fog computing, levelling, machine learning

Procedia PDF Downloads 124

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Nitin Dwivedi, Jigna Shah

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Aim and objective of study: This article indicates a comparison among various generations of dendrimers, a dendrimer is a bioactive material has repetitively branched molecule and used for delivery of various therapeutic active agents. This debut report compares the effect various generations of PPI dendrimers for brain targeting and management of neurodegenerative disorders potential on single platform. This report involves the study of the various mechanism of synthesis ligand anchored various generations PPI dendrimers deliver the drug directly to the CNS, prove their effectiveness in the management of the various neurodegenerative disease. Material and Methods: The Memantine an anti-Alzheimer drug loaded in different generations (3.0G, 4.0G, and 5.0G) of PPI dendrimers which were synthesized were synthesized. The various studies investigate the effect of PPI dendrimers generation on different characteristic parameters i.e. synthesis procedure, drug loading, release behavior, hemolysis profile at different concentration, MRI study for determine the route drug from olfactory transfer, animal model study in vitro, as well as in vivo performance. The outcomes of the investigation indicate drug delivery benefit as well as superior biocompatibility of 4.0G PPI dendrimer over 3.0G and 5.0G dendrimer, respectively. Results and Conclusion: The above study indicate the superiority of in drug delivery system with maximum drug utilization and minimize the drug dose for neurodegenerative disorder over 5.0G PPI dendrimers. So, 4.0G PPI dendrimers are the safe formulations for the symptomatic treatment of the neurodegenerative disorder. The fifth-generation poly(propyleneimine) (PPI) dendrimers, inherent toxicity due to the presence of many peripheral cationic groups is the major issue that limits their applicability.

Keywords: Alzheimer disease, generation, memantine, PPI

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Authors: Mehrzad Abdi Khalife, Iraj Mahdavi

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This paper presents the agile model for project management process. For project management process, the Project Management Body of Knowledge (PMBOK) guideline has been selected as platform. Combination of computational science and artificial intelligent methodology has been added to the guideline to transfer the standard to agile project management process. The model is the combination of practical standard, computational science and artificial intelligent. In this model, we present communication model and protocols to keep process agile. Here, we illustrate the collaboration man and machine in project management area with artificial intelligent approach.

Keywords: artificial intelligent, conceptual model, man-machine collaboration, project management, standard

Procedia PDF Downloads 322

Authors: Florin Leon, Silvia Curteanu

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Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

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Authors: Anjali N. Shete, G. D. Mahajan, Nanda Somwanshi

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Context: Smartphones are the new generation of mobile phones; they have emerged over the last few years. Technology has developed so much that it has become part of our life and mobile phones are one of them. These smartphones are equipped with the capabilities to display photos, play games, watch videos and navigation, etc. The advances have a huge impact on many walks of life. The adoption of new technology has been challenging for the elderly. But, the elder population is also moving towards digitally connected lives. As age advances, there is a decline in the motor and cognitive functions of the brain, and hence the reaction time is affected. The study was undertaken to assess the usefulness of smartphones in improving cognitive functions. Aims and Objectives: The aim of the study was to observe the effects of smartphone addiction on reaction time in elderly population Material and Methods: This is an experimental study. 100 elderly subjects were enrolled in this study randomly from urban areas. They all were using smartphones for several hours a day. They were divided into two groups according to the scores of the mobile phone addiction scale (MPAS). Simple reaction time was estimated by the Ruler drop method. The reaction time was then calculated for each subject in both groups. The data were analyzed using mean, standard deviation, and Pearson correlation test. Results: The mean reaction time in Group A is 0.27+ 0.040 and in Group B is 0.20 + 0.032. The values show a statistically significant change in reaction time. Conclusion: Group A with a high MPAS score has a low reaction time compared to Group B with a low MPAS score. Hence, it can be concluded that the use of smartphones in the elderly is useful, delaying the neurological decline, and smarten the brain.

Keywords: smartphones, MPAS, reaction time, elderly population

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Authors: Franklin Chu Buh, Irene Ule Ngole Sumbele, Andrew I. R. Maas, Mathieu Motah, Jogi V. Pattisapu, Eric Youm, Basil Kum Meh, Firas H. Kobeissy, Kevin W. Wang, Peter J. A. Hutchinson, Germain Sotoing Taiwe

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Introduction: Studying TBI characteristics and their relation to outcomes can identify initiatives to improve TBI prevention and care. The objective of this study was to define the features and outcomes of TBI patients seen over a 1-year period in a level-I trauma center in Cameroon. Methods: Data on demographics, causes, injury mechanisms, clinical aspects, and discharge status were prospectively collected over a period of 12 months. The Glasgow Outcome Scale-Extended (GOSE) and the Quality of Life Questionnaire after Brain Injury (QoLIBRI) were used to evaluate outcomes 6-months after TBI. Categorical variables were described as frequencies and percentages. Comparisons between 2 categorical variables were done using Pearson's Chi-square test or Fisher's exact test. Results: A total of 160 TBI patients participated in the study. The age group 15-45 years (78%; 125) was most represented. Males were more affected (90%; 144). Low educational level was recorded in 122 (76%) cases. Road traffic incidents (RTI) were the main cause of TBI (85%), with professional bike riders being frequently involved (27%, 43/160). Assaults (7.5%) and falls (2.5%) represent the second and third most common causes of TBI in Cameroon, respectively. Only 15 patients were transported to the hospital by ambulance, and 14 of these were from a referring hospital. CT-imaging was performed in 78% (125/160) of cases intracranial traumatic abnormality was identified in 77/125 (64%) cases. Financial constraints were the main reason for not performing a CT scan on 35 patients. A total of 46 (33%) patients were discharged against medical advice (DAMA) due to financial constraints. Mortality was 14% (22/160) but disproportionately high in patients with severe TBI (46%). DAMA had poor outcomes with QoLIBRI. Only 4 patients received post-injury physiotherapy services. Conclusion: TBI in Cameroon mainly results from RTIs and commonly affects young adult males, and low educational or socioeconomic status and commercial bike riding appear to be predisposing factors. Lack of pre-hospital care, financial constraints limiting both CT-scanning and medical care, and lack of acute physiotherapy services likely influenced care and outcomes adversely.

Keywords: characteristics, traumatic brain injury, outcome, disparities in care, prospective study

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Authors: Jagdish Prasad Sahoo

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The increase in active earth pressure during the event of an earthquake results sliding, overturning and tilting of earth retaining structures. In order to improve upon the stability of structures, the soil mass is often reinforced with various types of reinforcements such as metal strips, geotextiles, and geogrids etc. The stresses generated in the soil mass are transferred to the reinforcements through the interface friction between the earth and the reinforcement, which in turn reduces the lateral earth pressure on the retaining walls. Hence, the evaluation of earth pressure in the presence of seismic forces with an inclusion of reinforcements is important for the design retaining walls in the seismically active zones. In the present analysis, the effect of reinforcing horizontal layers of reinforcements in the form of sheets (Geotextiles and Geogrids) in sand used as backfill, on reducing the active earth pressure due to earthquake body forces has been studied. For carrying out the analysis, pseudo-static approach has been adopted by employing upper bound theorem of limit analysis in combination with finite elements and linear optimization. The computations have been performed with and out reinforcements for different internal friction angle of sand varying from 30 ° to 45 °. The effectiveness of the reinforcement in reducing the active earth pressure on the retaining walls is examined in terms of active earth pressure coefficient for presenting the solutions in a non-dimensional form. The active earth pressure coefficient is expressed as functions of internal friction angle of sand, interface friction angle between sand and reinforcement, soil-wall interface roughness conditions, and coefficient of horizontal seismic acceleration. It has been found that (i) there always exists a certain optimum depth of the reinforcement layers corresponding to which the value of active earth pressure coefficient becomes always the minimum, and (ii) the active earth pressure coefficient decreases significantly with an increase in length of reinforcements only up to a certain length beyond which a further increase in length hardly causes any reduction in the values active earth pressure. The optimum depth of the reinforcement layers and the required length of reinforcements corresponding to the optimum depth of reinforcements have been established. The numerical results developed in this analysis are expected to be useful for purpose of design of retaining walls.

Keywords: active, finite elements, limit analysis, presudo-static, reinforcement

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Authors: Artur Matuck, Guilherme F. Nobre

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Machines can be either tool, media, or social agents. Advances in technology have been delivering machines capable of autonomous expression, both through communication and art. This paper deals with models (theoretical approach) and experiments (applied approach) related to artificial agents. On one hand it traces how social sciences' scholars have worked with topics such as text automatization, man-machine writing cooperation, and communication. On the other hand it covers how computer sciences' scholars have built communicative and artistic machines, including the programming of creativity. The aim is to present a brief survey on artificially intelligent communicators and artificially creative writers, and provide the basis to understand the meta-authorship and also to new and further man-machine co-authorship.

Keywords: artificial communication, artificial creativity, artificial writers, meta-authorship, robotic art

Procedia PDF Downloads 275

Authors: Kourosh Modarresi

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The abundance of media channels and devices has given users a variety of options to extract, discover, and explore information in the digital world. Since, often, there is a long and complicated path that a typical user may venture before taking any (significant) action (such as purchasing goods and services), it is critical to know how each node (media channel) in the path of user has contributed to the final action. In this work, the significance of each media channel is computed using statistical analysis and machine learning techniques. More specifically, “Regularized Singular Value Decomposition”, and “Sparse Principal Component” has been used to compute the significance of each channel toward the final action. The results of this work are a considerable improvement compared to the present approaches.

Keywords: multimedia attribution, sparse principal component, regularization, singular value decomposition, feature significance, machine learning, linear systems, variable shrinkage

Procedia PDF Downloads 291

Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Aaditya U. Jhamb

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Due to the current energy crisis that many countries are battling, energy-efficient buildings are the subject of extensive research in the modern technological era because of growing worries about energy consumption and its effects on the environment. The paper explores 8 factors that help determine energy efficiency for a building: (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution), with Tsanas and Xifara providing a dataset. The data set employed 768 different residential building models to anticipate heating and cooling loads with a low mean squared error. By optimizing these characteristics, machine learning algorithms may assess and properly forecast a building's heating and cooling loads, lowering energy usage while increasing the quality of people's lives. As a result, the paper studied the magnitude of the correlation between these input factors and the two output variables using various statistical methods of analysis after determining which input variable was most closely associated with the output loads. The most conclusive model was the Decision Tree Regressor, which had a mean squared error of 0.258, whilst the least definitive model was the Isotonic Regressor, which had a mean squared error of 21.68. This paper also investigated the KNN Regressor and the Linear Regression, which had to mean squared errors of 3.349 and 18.141, respectively. In conclusion, the model, given the 8 input variables, was able to predict the heating and cooling loads of a residential building accurately and precisely.

Keywords: energy efficient buildings, heating load, cooling load, machine learning models

Procedia PDF Downloads 79