Search results for: physics guided machine learning

Authors: Wenzheng Fu, Yue Fu, Zhijiang Dong, Yujian Fu

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The integration of artificial intelligence (AI) and machine learning (ML) into project management is transforming how engineering projects are executed, monitored, and controlled. This paper provides a comprehensive survey of AI and ML applications in project management, systematically categorizing their use in key areas such as project data analytics, monitoring, tracking, scheduling, and reporting. As project management becomes increasingly data-driven, AI and ML offer powerful tools for improving decision-making, optimizing resource allocation, and predicting risks, leading to enhanced project outcomes. The review highlights recent research that demonstrates the ability of AI and ML to automate routine tasks, provide predictive insights, and support dynamic decision-making, which in turn increases project efficiency and reduces the likelihood of costly delays. This paper also examines the emerging trends and future opportunities in AI-driven project management, such as the growing emphasis on transparency, ethical governance, and data privacy concerns. The research suggests that AI and ML will continue to shape the future of project management by driving further automation and offering intelligent solutions for real-time project control. Additionally, the review underscores the need for ongoing innovation and the development of governance frameworks to ensure responsible AI deployment in project management. The significance of this review lies in its comprehensive analysis of AI and ML’s current contributions to project management, providing valuable insights for both researchers and practitioners. By offering a structured overview of AI applications across various project phases, this paper serves as a guide for the adoption of intelligent systems, helping organizations achieve greater efficiency, adaptability, and resilience in an increasingly complex project management landscape.

Keywords: artificial intelligence, decision support systems, machine learning, project management, resource optimization, risk prediction

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Authors: S. Vijayakumar, S. B. Rathna Kumar, Navnath D Jagadale

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Learning and teaching are the challenges ahead in the education of the students with hearing impairment using sign language (SHISL). Either the students or teachers face difficulties in the process of learning/teaching. Communication is one of the main barriers while teaching SHISL. Further, the courses of study or the subjects are limited to SHISL at least in countries like India. Students with hearing impairment mainly opt for sign language as a communication mode. Subjects like physics, chemistry, advanced mathematics etc. are not available in the curriculum for the SHISL since their content and ideas are complex. In India, exemption for language papers is being given for the students with hearing impairment. It may give opportunity to them to secure secondary/ higher secondary qualifications. It is a known fact that students with hearing impairment are facing difficulty in their future carrier. They secure neither a higher study nor a good employment opportunity. Vocational training in various trades will land them in few jobs with few bucks in pocket. However, not all of them are blessed with higher positions in government or private sectors in competitive fields or where the technical knowledge is required. E learning with sign language instructions can be used for teaching languages and science subjects. Computer Based Instruction (CBI), Computer Based Training (CBT), and Computer Assisted Instruction (CAI) are now part-and-parcel of Modern Education. It will also include signed video clip corresponding to the topic. Learning language subjects will improve the understanding of concepts in different subjects. Learning other science subjects like their hearing counterparts will enable the SHISL to go higher in studies and increase their height to pluck a fruit of the tree of employment.

Keywords: students with hearing impairment using sign language, hearing impairment, language subjects, science subjects, e-learning

Procedia PDF Downloads 403

Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

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Authors: Matt Vellkorn, Mara Sarinski

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The field of advanced diagnostics is a very rapidly changing one. A new technology that has not been fully used yet are resistotrons. A resistotron is a physical device thatis used to detect the presence of low energy alpha particles. It has been used for many years in nuclear physics as an alpha particle detector. Since they are used in nuclear physics, they have to be accurate. They have to be able to differentiate between alpha particles and other types of radiation. The resistotrons are primarily used for safety. They are used in areas where people or animals can get exposed to radiation. A typical example is in the treatment of nuclear waste. As it is with any nuclear physics instrument, a resistotron has to be very accurate and reliable. In the past, the instrument was very expensive because they were made out of copper. Today, they are made out of brass. The main difference is that brass is much less expensive than copper.

Keywords: biosensors, resistotrons, biophysics, diagnostics

Procedia PDF Downloads 117

Authors: Prajwal Prakash Vasisht, Sharath Rajamurthy, Nishanth Dara

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In a supervised machine learning approach, metrics such as precision, accuracy, and coverage can be calculated using ground truth labels to help in model tuning, evaluation, and selection. In an unsupervised setting, however, where the data has no ground truth, there are few interpretable metrics that can guide us to do the same. Our approach creates a framework to adapt the Intersection over Union metric, referred to as Adapted IoU, usually used to evaluate supervised learning models, into the unsupervised domain, which solves the problem by factoring in subject matter expertise and intuition about the ideal output from the model. This metric essentially provides a scale that allows us to compare the performance across numerous unsupervised models or tune hyper-parameters and compare different versions of the same model.

Keywords: general metric, unsupervised learning, classification, intersection over union

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Authors: Valery Yakubovich, Shuping wu

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Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

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Authors: Neha Singh, Ananya Arora

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Facial expression measurement plays a crucial role in the identification of emotion. Facial expression plays a key role in psychophysiology, neural bases, and emotional disorder, to name a few. The Facial Action Coding System (FACS) has proven to be the most efficient and widely used of the various systems used to describe facial expressions. Coders can manually code facial expressions with FACS and, by viewing video-recorded facial behaviour at a specified frame rate and slow motion, can decompose into action units (AUs). Action units are the most minor visually discriminable facial movements. FACS explicitly differentiates between facial actions and inferences about what the actions mean. Action units are the fundamental unit of FACS methodology. It is regarded as the standard measure for facial behaviour and finds its application in various fields of study beyond emotion science. These include facial neuromuscular disorders, neuroscience, computer vision, computer graphics and animation, and face encoding for digital processing. This paper discusses the conceptual basis for FACS, a numerical listing of discrete facial movements identified by the system, the system's psychometric evaluation, and the software's recommended training requirements.

Keywords: facial action, action units, coding, machine learning

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Authors: Vida Razzaghifard

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E-learning, also known as online learning, has indicated an increased growth in recent years. One of the critical factors in the successful application of e-learning in higher education is students’ perceptions towards it. The main purpose of this paper is to investigate the perceptions of engineering students about e-learning in UK. For the purpose of the present study, 145 second year Engineering students were randomly selected from the total population of 1280 participants. The participants were asked to complete a questionnaire containing 16 items. The data collected from the questionnaire were analyzed through the Statistical Package for Social Science (SPSS) software. The findings of the study revealed that the majority of participants have negative perceptions on e-learning. Most of the students had trouble interacting effectively during online classes. Furthermore, the majority of participants had negative experiences with the learning platform they used during e-learning. Suggestions were made on what could be done to improve the students’ perceptions towards e-learning.

Keywords: E-learning, higher, education, engineering education, online learning

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Authors: C. W. Kan, H. F. Cheung, Y. S. Lee

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This study examines the results of colour fading of cotton fabric by plasma-induced ozone treatment, with an aim to provide learning materials for fashion designers when designing colour fading effects in fashion products. Cotton knitted fabrics were dyed with red reactive dye with a colour depth of 1.5% and were subjected to ozone generated by a commercially available plasma machine for colour fading. The plasma-induced ozone treatment was conducted with different parameters: (i) air concentration = 10%, 30%, 50% and 70%; (ii) water content in fabric = 35% and 45%, and (iii) treatment time = 10 minutes, 20 minutes and 30 minutes. Finally, the colour properties of the plasma–induced ozone treated fabric were measured by spectrophotometer under illuminant D₆₅ to obtain the CIE L*, CIE a* and CIE b* values.

Keywords: learning materials, colour fading, colour properties, fashion products

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

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Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

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Authors: Kashif Laeeq, Zubair A. Shaikh

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The paradigm of education is drastically changing from conventional to e-learning model. Due to ease of learning with various other benefits, several educational institutions are adopting the e-learning models. Some institutions are still willing to transform their educational system on to e-learning, but due to limited resources, they are still compromising on the old traditional system. The cloud computing could be one of the best solutions to overcome this problem by providing hardware, software, and infrastructure resources with cost efficient manner. The adoption of cloud computing in education will bring revolution in this paradigm. This paper introduces various positive features of e-learning and presents a way how cloud computing technology can be provisioned e-learning model. This paper also investigates the numerous challenges and opportunities that would be observed in cloud computing adoption in e-learning domain. The concept and knowledge present in this paper may create a new direction of research in the domain of cloud-based e-learning.

Keywords: cloud-based e-learning, e-learning, cloud computing application, smart learning

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Authors: Oladimeji Adetona Adeyeye, Emmanuel Rotimi Sadiku, Oluwaseun Olayinka Adeyeye

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In order to resuscitate soybean production and post-harvest processing especially, in term of threshing, there is need to develop an affordable threshing machine which will reduce drudgery associated with manual soybean threshing. Soybean thresher was fabricated and evaluated at Institute of Agricultural Research and Training IAR&T Apata Ibadan. The machine component includes; hopper, threshing unit, shaker, cleaning unit and the seed outlet, all working together to achieve the main objective of threshing and cleaning. TGX1835 - 10E variety was used for evaluation because of its high resistance to pests, rust and pustules. The final moisture content of the used sample was about 15%. The sample was weighed and introduced into the machine. The parameters evaluated includes moisture content, threshing efficiency, cleaning efficiency, machine capacity and speed. The threshing efficiency and capacity are 74% and 65.9kg/hr respectively. All materials used were sourced locally which makes the cost of production of the machine extremely cheaper than the imported soybean thresher.

Keywords: efficiency, machine capacity, speed, soybean, threshing

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Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Muneer Abbad

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In search of better, traditional learning universities have expanded their ways to deliver knowledge and integrate cost effective e-learning systems. Universities’ use of information and communication technologies has grown tremendously over the last decade. To ensure efficient use of the e-learning system, this project aimed to evaluate the good and bad practices, detect errors and determine areas for further improvements in usage. This project critically evaluated the students’ perception of the e-learning system and recommended changes to improve students’ e-learning usage, through conducting questionnaire given to the students that have experience with e-learning systems. Results of the study indicated that, in general, students have favourable perceptions toward using the e-learning system. They seemed to value the resources tool and its contribution to building their knowledge more than other e-learning tools. However, they seemed to perceive a limited value from the audio or video podcasts. This study has shown that technology acceptance is the most variable, factor that contributes to students’ perception and satisfaction of the e-learning system.

Keywords: e-learning, perception, Jordan, universities

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Nur Sofi Hidayah, Ratih Tri Purwatiningsih

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Learning is a process attempts person to obtain a new behavior changes as a whole, as a result of his own experience in the interaction with the environment. Learning involves our brain to think, while the ability of the brain to each student's performance is different. To obtain optimal learning results then need time to learn the exact hour that the brain's performance is not too heavy. Referring to the learning system in Finland which apply 45 minutes to learn and a 15-minute break is expected to be the brain work better, with the rest of the brain, the brain will be more focused and lessons can be absorbed well. It can be concluded that learning in this way students learn with brain always fresh and the best possible use of the time, but it can make students not saturated in a lesson.

Keywords: learning, working hours brain, time efficient learning, working hours in the brain receive stimulus.

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Authors: Zoe Lynch, Kirsty Zieschank

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There are several factors that compromise the utility and wide-spread use of existing emotional and behavioural distress screening instruments. Some of these factors include lengthy administration times, high costs, feasibility issues, and a lack of self-report options for children under 12 years of age. This animation-based resource was developed to overcome as many of these factors as possible. Developed for educators and medical and mental health professionals, this resource offers children a self-guided mechanism for reporting any current emotional and behavioural distress. An avatar assistant, selected by the child, accompanies them through each stage of the screening process, offering further instruction if prompted. Children enter their age and gender before viewing comparative animations conveying common childhood emotional and behavioural difficulties. The child then selects the most relatable animations, along with the frequency with which they experience the depicted emotions. From a perspective of intellectual development, an engaging, animated format means that outcomes will not be constrained by children’s reading, writing, cognitive, or verbal expression abilities. Having been user-tested with children aged 6 to 12, this resource shows promising results as a self-guided screening instrument.

Keywords: animation-based screening instrument, mental health, primary-aged children, self-guided

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Authors: Andy Wang

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Personalized learning refers to an educational approach that tailors instruction to meet the unique needs, interests, and abilities of each learner. This method of learning aims at providing students with a customized learning experience that is more engaging, interactive, and relevant to their personal lives. With generative AI technology, the author has developed a Personal Tutoring Bot (PTB) that supports personalized learning. The author is currently testing PTB in his EE 499 – Microelectronics Metrology course. Virtual Reality (VR) and Augmented Reality (AR) provide interactive and immersive learning environments that can engage student in online learning. This paper presents the rationale of integrating VR/AR tools in PTB and discusses challenges and solutions of incorporating VA/AR into the Personal Tutoring Bot (PTB).

Keywords: personalized learning, online education, hands-on practice, VR/AR tools

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Authors: Mzamani Aaron Mabasa

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The research study examines the reality that the Language Services Directorate can be considered a language repository hub. The study postulates that multilingual education guided by language policy implementation can improve student performance and pass rate. Various documents in the form of style guides, glossaries and tutorial letters may be used to enable students to understand complex words, sentences, phrases and paragraphs when technical vocabularies are used. This paper addresses the way in which quality assurance can transform South African official languages, including Sign Language, as mandated by the Language Policy for Higher Education. The paper further emphasizes that Language Services is unique in the sense that it involves all South African officials as tools for student support and facilitation of learning. This is in line with the Constitution of the Republic of South Africa (1996) and the Unisa Language Policy of 2023, which declares the status, parity and esteem of these official languages regarding usage in formal function domains, namely education, economy, social and politics. The aim of this paper is to ensure that quality assurance is ultimately accomplished in terms of teaching and learning standards. Eventually, all South African languages can be used for official domains to achieve functional multilingualism. This paper furthermore points out that content analysis as a research instrument as far as a qualitative approach is concerned may be used as a data collection technique.

Keywords: repository, multilingualism, policy, education

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Authors: F. Ruiz-Fuster

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Arnold’s redefinition of human motives can sustain a psychology of education which emphasizes the beauty of knowledge and the exercise of intellectual functions. Thus, education instead of focusing on skills and learning by doing would be centered on ‘the widest reaches of the human spirit’. One way to attain it is by developing children’s inherent interest. Arnold takes into account the fact that the desire to know is the inherent interest which leads students to explore and learn. She also emphasizes the need of exercising human functions as thinking, judging and reasoning. According to Arnold, the influence of psychological theories of motivation in education has derived in considering that all learning and school tasks should derive from children’s needs and impulses. The desire to know and the curiosity have not been considered as basic and active as any instinctive drive or basic need, so there has been an attempt to justify and understand how biological drives guide student’s learning. However, understanding motives and motivation not as a drive, an instinct or an impulse guided by our basic needs, but as a want that leads to action can help to understand, from a psychological perspective, how teachers can motivate students to learn, strengthening their desire and interest to reason and discover the whole new world of knowledge.

Keywords: academic motivation, interests, desire to know, educational psychology, intellectual functions

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Authors: Paula Paulino, Alzira Matias, Ana Margarida Veiga Simão

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Self-regulated learning (SRL) directs students in analyzing proposed tasks, setting goals and designing plans to achieve those goals. The literature has suggested a metacognitive strategy for goal attainment known as Mental Contrasting with Implementation Intentions (MCII). This strategy involves Mental Contrasting (MC), in which a significant goal and an obstacle are identified, and Implementation Intentions (II), in which an "if... then…" plan is conceived and operationalized to overcome that obstacle. The present study proposes to assess the MCII process and whether it promotes students’ commitment towards learning goals during school tasks in sciences subjects. In this investigation, we intended to study the MCII strategy in a systemic context of the classroom. Fifty-six students from middle school and secondary education attending a public school in Lisbon (Portugal) participated in the study. The MCII strategy was explicitly taught in a procedure that included metacognitive modeling, guided practice and autonomous practice of strategy. A mental contrast between a goal they wanted to achieve and a possible obstacle to achieving that desire was instructed, and then the formulation of plans in order to overcome the obstacle identified previously. The preliminary results suggest that the MCII metacognitive strategy, applied to the school context, leads to more sophisticated reflections, the promotion of learning goals and the elaboration of more complex and specific self-regulated plans. Further, students achieve better results on school tests and worksheets after strategy practice. This study presents important implications since the MCII has been related to improved outcomes and increased attendance. Additionally, MCII seems to be an innovative process that captures students’ efforts to learn and enhances self-efficacy beliefs during learning tasks.

Keywords: implementation intentions, learning goals, mental contrasting, metacognitive strategy, self-regulated learning

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: R. Janyaphadungpong, A. Pimkhaokham

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This prospective clinical study determined the insertion torque (IT) value and monitored the changes in implant stability quotient (ISQ) values during the 12 weeks healing period from implant placement without bone graft (control group) and with bone graft using the guided bone regeneration (GBR) technique (study group). The relationship between the IT and ISQ values of the implants was also assessed. The control and study groups each consisted of 6 patients with 8 implants per group. The ASTRA TECH Implant System™ EV 4.2 mm in diameter was placed in the posterior mandibular region. In the control group, implants were placed in bone without bone graft, whereas in the study group implants were placed simultaneously with the GBR technique at favorable bone defect. IT (Ncm) of each implant was recorded when fully inserted. ISQ values were obtained from the Osstell® ISQ at the time of implant placement, and at 2, 4, 8, and 12 weeks. No difference in IT was found between groups (P = 0.320). The ISQ values in the control group were significantly higher than in the study group at the time of implant placement and at 4 weeks. There was no significant association between IT and ISQ values either at baseline or after the 12 weeks. At 12 weeks of healing, the control and study groups displayed different trends. Mean ISQ values for the control group decreased over the first 2 weeks and then started to increase. ISQ value increases were statistically significant at 8 weeks and later, whereas mean ISQ values in the study group decreased over the first 4 weeks and then started to increase, with statistical significance after 12 weeks. At 12 weeks, all implants achieved osseointegration with mean ISQ values over the threshold value (ISQ>70). These results indicated that implants, in which guided bone regeneration technique was performed during implant placement for treating favorable bone defects, were as predictable as implants placed without bone graft. However, loading in implants placed with the GBR technique for correcting favorable bone defects should be performed after 12 weeks of healing to ensure implant stability and osseointegration.

Keywords: dental implant, favorable bone defect, guided bone regeneration technique, implant stability

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Soheila Sadeghi

Abstract:

In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Zenaide Carvalho da Silva, Leandro Rodrigues Ferreira, Andrey Ricardo Pimentel

Abstract:

Learning styles (LS) refer to the ways and forms that the student prefers to learn in the teaching and learning process. Each student has their own way of receiving and processing information throughout the learning process. Therefore, knowing their LS is important to better understand their individual learning preferences, and also, understand why the use of some teaching methods and techniques give better results with some students, while others it does not. We believe that knowledge of these styles enables the possibility of making propositions for teaching; thus, reorganizing teaching methods and techniques in order to allow learning that is adapted to the individual needs of the student. Adapting learning would be possible through the creation of online educational resources adapted to the style of the student. In this context, this article presents the structure of a learning object interface adaptation based on the LS. The structure created should enable the creation of the adapted learning object according to the student's LS and contributes to the increase of student’s motivation in the use of a learning object as an educational resource.

Keywords: adaptation, interface, learning object, learning style

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Authors: Marzieh Karimihaghighi, Carlos Castillo

Abstract:

This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

Abstract:

Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

Procedia PDF Downloads 146