Search results for: computational methods

Authors: Pratham Madnur, Prathamkumar Shetty, Sneha Varur, Gouri Parashetti

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In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications.

Keywords: OpenCV, YOLOv8, cricket, custom dataset, computer vision, sports

Procedia PDF Downloads 89

Authors: Kubra Korkmaz Onat

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Decision-making significantly influences outcomes and shapes future actions, making it a crucial aspect of both personal and professional life. This study examines various decision-making approaches, focusing on their procedures and applications. The rational decision-making model is highlighted for its systematic approach and reliance on data analysis and logical reasoning. Additionally, the study explores consensus, weighted scoring, voting, and brainstorming analysis methods. Key findings indicate that each method has unique strengths and is best suited for specific contexts. The article concludes by offering practical guidance for how to choose the appropriate decision-making approach based on the circumstances.

Keywords: decision-making, decision-making process, decision-making methods, group decision-making

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Authors: Juan José Mesas, Luis Sainz

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

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

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Authors: Zahra Ahmadi, Fazlollah Adibnia

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Scheduling of tasks and the optimal allocation of resources in the cloud are based on the dynamic nature of tasks and the heterogeneity of resources. Applications that are based on the scientific workflow are among the most widely used applications in this field, which are characterized by high processing power and storage capacity. In order to increase their efficiency, it is necessary to plan the tasks properly and select the best virtual machine in the cloud. The goals of the system are effective factors in scheduling tasks and resource selection, which depend on various criteria such as time, cost, current workload and processing power. Multi-criteria decision-making methods are a good choice in this field. In this research, a new method of work planning and resource allocation in a heterogeneous environment based on the modified AHP algorithm is proposed. In this method, the scheduling of input tasks is based on two criteria of execution time and size. Resource allocation is also a combination of the AHP algorithm and the first-input method of the first client. Resource prioritization is done with the criteria of main memory size, processor speed and bandwidth. What is considered in this system to modify the AHP algorithm Linear Max-Min and Linear Max normalization methods are the best choice for the mentioned algorithm, which have a great impact on the ranking. The simulation results show a decrease in the average response time, return time and execution time of input tasks in the proposed method compared to similar methods (basic methods).

Keywords: hierarchical analytical process, work prioritization, normalization, heterogeneous resource allocation, scientific workflow

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Authors: Yas Barzegar, Atrin Barzegar

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Drinking water quality assessment is a major issue today; technology and practices are continuously improving; Artificial Intelligence (AI) methods prove their efficiency in this domain. The current research seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system (FIS) is applied with different defuzzification methods. The Proposed Model includes three fuzzy intermediate models and one fuzzy final model. Each fuzzy model consists of three input parameters and 27 fuzzy rules. The model is developed for water quality assessment with a dataset considering nine parameters (Alkalinity, Hardness, pH, Ca, Mg, Fluoride, Sulphate, Nitrates, and Iron). Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; it is an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The FIS method can provide an effective solution to complex systems; this method can be modified easily to improve performance.

Keywords: water quality, fuzzy logic, smart cities, water attribute, fuzzy inference system, membership function

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Anna Kizinska

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The research aims at discussing Polish and British incongruent terms that refer to company law. The Polish terms under analysis appear in the Polish Code of Commercial Partnerships and Companies and constitute legal terms or factual terms. The English equivalents of each Polish term under research appear in two Polish Code of Commercial Partnerships and Companies translations into English. The theoretical part of the paper includes the presentation of the definitions of a system-bound term and incongruity of terms. The aim of the analysis is to check if the classification of translation methods used in civil law terms translation comprehends the translation methods applied while translating company law terms into English. The translation procedures are defined according to Newmark. The stages of the research include 1) presentation of a definition of a Polish term, 2) enumerating the so-far published English equivalents of a given Polish term and comparing their definitions (as long as they appear in English law dictionaries ) with the definition of a given Polish term under analysis, 3) checking whether an English equivalent appears or not in, among others, the sources of the British law (legislation.gov.uk database) , 4) identifying the translation method that was applied while forming a given English equivalent.

Keywords: translation, legal terms, equivalence, company law, incongruency

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Authors: Murat Colak, Berkay Cakir Saridogan

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Since the dawn of modern corporations, almost every single employee has been working in the same loop, which contains three basic steps: going to work, providing the needs for the work, and getting back home. Only a small amount of people were able to break that standard and live outside the box. As the 2019 pandemic hit the Earth and most companies shut down their physical offices, that loop had to change for everyone. This means that the old management styles had to be significantly re-arranged to the "work from home" type of business methods. The methods include online conferences and meetings, time and task tracking using algorithms, globalization of the work, and, most importantly, remote working. After the global epidemic started, even the tech giants were concerned. Now, it can be seen those technology companies have an incredible step-up in their shares compared to the other companies because they know how to manage such situations even better than every other industry. This study aims to take the old traditional management styles in big companies and compare them with the post-covid methods (2019-2022). As a result of this comparison made using the annual reports and shared statistics, this study aims to explain why the winners of this crisis are the technology companies.

Keywords: Covid-19, technology companies, business models, remote work

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Authors: Siti Salbiah Samsudin, Hamzah Arof, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris

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Standard X-ray spine images produced by conventional screen-film technique have a limited field of view. This limitation may obstruct a complete inspection of the spine unless images of different parts of the spine are placed next to each other contiguously to form a complete structure. Another solution to producing a whole spine image is by assembling the digitized x-ray images of its parts automatically using image stitching. This paper presents a new Medical Image Stitching (MIS) method that utilizes Minimum Average Correlation Energy (MACE) filters to identify and merge pairs of x-ray medical images. The effectiveness of the proposed method is demonstrated in two sets of experiments involving two databases which contain a total of 40 pairs of overlapping and non-overlapping spine images. The experimental results are compared to those produced by the Normalized Cross Correlation (NCC) and Phase Only Correlation (POC) methods for comparison. It is found that the proposed method outperforms those of the NCC and POC methods in identifying both the overlapping and non-overlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about two to five times shorter than those of the POC and NCC methods.

Keywords: image stitching, MACE filter, panorama image, scoliosis

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Authors: Tuğçe Kiper Elibol, İbrahim Uslan, Mehmet Ali Guler, Murat Buyuk, Uğur Yolum

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According to the FAA report, 142603 bird strikes were reported for a period of 24 years, between 1990 – 2013. Bird strike with aerospace structures not only threaten the flight security but also cause financial loss and puts life in danger. The statistics show that most of the bird strikes are happening with the nose and the leading edge of the wings. Also, a substantial amount of bird strikes is absorbed by the jet engines and causes damage on blades and engine body. Crash proof designs are required to overcome the possibility of catastrophic failure of the airplane. Using computational methods for bird strike analysis during the product development phase has considerable importance in terms of cost saving. Clearly, using simulation techniques to reduce the number of reference tests can dramatically affect the total cost of an aircraft, where for bird strike often full-scale tests are considered. Therefore, development of validated numerical models is required that can replace preliminary tests and accelerate the design cycle. In this study, to verify the simulation parameters for a bird strike analysis, several different numerical options are studied for an impact case against a primitive structure. Then, a representative bird mode is generated with the verified parameters and collided against the leading edge of a training aircraft wing, where each structural member of the wing was explicitly modeled. A nonlinear explicit dynamics finite element code, LS-DYNA was used for the bird impact simulations. SPH methodology was used to model the behavior of the bird. Dynamic behavior of the wing superstructure was observed and will be used for further design optimization purposes.

Keywords: bird impact, bird strike, finite element modeling, smoothed particle hydrodynamics

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Kholod J. Alotaibi, Andrew M. Gravell

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Usability is an important software quality that is often neglected at the design stage. Although methods exist to incorporate elements of usability engineering, there is a need for more balanced usability focused methods that can enhance the experience of software usability for users. In this regard, the potential for Usage-Centered Design is explored with respect to requirements gathering and is shown to lead to high software usability besides other benefits. It achieves this through its focus on usage, defining essential use cases, by conducting task modeling, encouraging user collaboration, refining requirements, and so on. The requirements gathering process in UgCD is described in detail.

Keywords: requirements gathering, usability, usage-centred design, computer science

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Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif

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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.

Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence

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Authors: Olcay Çam, Ayşegül Bilge, Merve Uğuryol, Hacer Demirkol

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Complementary and alternative medicine are important for human health. It has stood out that from past to present people have resorted to particularly Turkish bath houses, cupping therapy, mud bath, hirudotheraphy and healing waters for the purpose of recovering from diseases and refresh their souls. Now, methods such as herbal treatments, massage, aromatherapy, prayer, meditation, yoga and thermal springs have been recently observed to be the most frequently used complementary therapies in Turkey. These methods are not known by people exactly. As a result, complementary therapies are applied along with the modern therapies in Turkey, we are considered to be effective in maintaining and improving individuals’ health.

Keywords: complementary therapy, health, health services, modern therapies

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Ulugbek Azimov, Nearchos Stylianidis, Nobuyuki Kawahara, Eiji Tomita

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A chemical kinetics and computational fluid-dynamics (CFD) analysis was performed to evaluate the combustion of syngas derived from biomass and coke-oven solid feedstock in a micro-pilot ignited supercharged dual-fuel engine under lean conditions. For this analysis, a new reduced syngas chemical kinetics mechanism was constructed and validated by comparing the ignition delay and laminar flame speed data with those obtained from experiments and other detail chemical kinetics mechanisms available in the literature. The reaction sensitivity analysis was conducted for ignition delay at elevated pressures in order to identify important chemical reactions that govern the combustion process. The chemical kinetics of NOx formation was analyzed for H2/CO/CO2/CH4 syngas mixtures by using counter flow burner and premixed laminar flame speed reactor models. The new mechanism showed a very good agreement with experimental measurements and accurately reproduced the effect of pressure, temperature and equivalence ratio on NOx formation. In order to identify the species important for NOx formation, a sensitivity analysis was conducted for pressures 4 bar, 10 bar and 16 bar and preheat temperature 300 K. The results show that the NOx formation is driven mostly by hydrogen based species while other species, such as N2, CO2 and CH4, have also important effects on combustion. Finally, the new mechanism was used in a multidimensional CFD simulation to predict the combustion of syngas in a micro-pilot-ignited supercharged dual-fuel engine and results were compared with experiments. The mechanism showed the closest prediction of the in-cylinder pressure and the rate of heat release (ROHR).

Keywords: syngas, chemical kinetics mechanism, internal combustion engine, NOx formation

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Authors: Ana-Katarina Nikich

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Background: The kinesiotaping method is often used in physiotherapy and rehabilitation. The purpose of this study was to evaluate the effectiveness of taping in the rehabilitation process of patients. Materials and methods: The study involved 90 male and female patients (the average age was 40-50 years) with various conditions requiring rehabilitation, such as injuries of the musculoskeletal system, sports injuries and other ailments. All patients were divided into two groups: experimental (n=40) and control (n=50). Both groups received 20 days of standard rehabilitation. In the experimental group, kinesiotaping methods were used, taking into account the individual characteristics of each patient. The control group performed regular exercises and physical therapy, but without using kinesiotape. During the study, physical parameters were monitored, interviews were conducted and the conditions of patients from both groups were compared. Results and discussion: The use of the kinesiotaping method in the rehabilitation process led to a significant improvement in physical parameters and pain reduction in patients. Significant improvement (p <0.005) was observed in all evaluated parameters among the patients of the experimental group. The control group also showed sufficient improvement (p <0.005), but the percentage of the experimental group was higher. As a result of the observation, the patients of the experimental group showed faster and more complete rehabilitation compared to the control group. The use of the kinesiotaping method allows to reduce the load on the damaged areas, improve blood circulation and lymphatic drainage, as well as increase stability and coordination of movements. Conclusions: Kinesiotaping as one of the modern therapeutic methods has shown its effectiveness in the rehabilitation process, contributing to the optimal recovery of patients with various conditions requiring rehabilitation. The use of tapes should be included in a comprehensive rehabilitation program to achieve the best results and reduce recovery time.

Keywords: kinesiotaping, rehabilitation, therapy, pain

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Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

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Authors: Enes Yasa, Guven Fidan

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Building Simulation tools need to better evaluate convective heat exchanges between external air and wall surfaces. Previous analysis demonstrated the significant effects of convective heat transfer coefficient values on the room energy balance. Some authors have pointed out that large discrepancies observed between widely used building thermal models can be attributed to the different correlations used to calculate or impose the value of the convective heat transfer coefficients. Moreover, numerous researchers have made sensitivity calculations and proved that the choice of Convective Heat Transfer Coefficient values can lead to differences from 20% to 40% of energy demands. The thermal losses to the ambient from a building surface or a roof mounted solar collector represent an important portion of the overall energy balance and depend heavily on the wind induced convection. In an effort to help designers make better use of the available correlations in the literature for the external convection coefficients due to the wind, a critical discussion and a suitable tabulation is presented, on the basis of algebraic form of the coefficients and their dependence upon characteristic length and wind direction, in addition to wind speed. Many research works have been conducted since early eighties focused on the convection heat transfer problems inside buildings. In this context, a Computational Fluid Dynamics (CFD) program has been used to predict external convective heat transfer coefficients at external building surfaces. For the building facades model, effects of wind speed and temperature differences between the surfaces and the external air have been analyzed, showing different heat transfer conditions and coefficients. In order to provide further information on external convective heat transfer coefficients, a numerical work is presented in this paper, using a Computational Fluid Dynamics (CFD) commercial package (CFX) to predict convective heat transfer coefficients at external building surface.

Keywords: CFD in buildings, external convective heat transfer coefficients, building facades, thermal modelling

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Authors: Ammar Khader Almasri

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The software processes play a vital role for delivering a high quality software system that meets the user’s needs. There are many software development models which are used by most system developers, which can be categorized into two categories (traditional and new methodologies). Mobile applications like other desktop applications need appropriate and well-working software development process. Nevertheless, mobile applications have different features which limit their performance and efficiency like application size, mobile hardware features. Moreover, this research aims to help developers in using a standardized model for developing mobile applications.

Keywords: software development process, agile methods , moblile application development, traditional methods

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Authors: Shyaka Eugene

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The efficient and safe demolition of structures is a critical challenge in civil engineering and construction. This study focuses on the development of optimal demolition strategies by investigating the crack propagation behavior in beams induced by soundless cracking agents. It is commonly used in controlled demolition and has gained prominence due to its non-explosive and environmentally friendly nature. This research employs a comprehensive experimental and computational approach to analyze the crack initiation, propagation, and eventual failure in beams subjected to soundless cracking agents. Experimental testing involves the application of various cracking agents under controlled conditions to understand their effects on the structural integrity of beams. High-resolution imaging and strain measurements are used to capture the crack propagation process. In parallel, numerical simulations are conducted using advanced finite element analysis (FEA) techniques to model crack propagation in beams, considering various parameters such as cracking agent composition, loading conditions, and beam properties. The FEA models are validated against experimental results, ensuring their accuracy in predicting crack propagation patterns. The findings of this study provide valuable insights into optimizing demolition strategies, allowing engineers and demolition experts to make informed decisions regarding the selection of cracking agents, their application techniques, and structural reinforcement methods. Ultimately, this research contributes to enhancing the safety, efficiency, and sustainability of demolition practices in the construction industry, reducing environmental impact and ensuring the protection of adjacent structures and the surrounding environment.

Keywords: expansion pressure, energy release rate, soundless chemical demolition agent, crack propagation

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Authors: Melkamu Tadesse Getachew

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Intelligent control systems have revolutionized biomedical engineering, advancing research and enhancing medical practice. This review paper examines the impact of intelligent control on various aspects of biomedical engineering. It analyzes how these systems enhance precision and accuracy in biomedical instrumentation, improving diagnostics, monitoring, and treatment. Integration challenges are addressed, and potential solutions are proposed. The paper also investigates the optimization of drug delivery systems through intelligent control. It explores how intelligent systems contribute to precise dosing, targeted drug release, and personalized medicine. Challenges related to controlled drug release and patient variability are discussed, along with potential avenues for overcoming them. The comparison of algorithms used in intelligent control systems in biomedical control is also reviewed. The implications of intelligent control in computational and systems biology are explored, showcasing how these systems enable enhanced analysis and prediction of complex biological processes. Challenges such as interpretability, human-machine interaction, and machine reliability are examined, along with potential solutions. Intelligent control in biomedical engineering also plays a crucial role in risk management during surgical operations. This section demonstrates how intelligent systems improve patient safety and surgical outcomes when integrated into surgical robots, augmented reality, and preoperative planning. The challenges associated with these implementations and potential solutions are discussed in detail. In summary, this review paper comprehensively explores the widespread impact of intelligent control on biomedical engineering, showing the future of human health issues promising. It discusses application areas, challenges, and potential solutions, highlighting the transformative potential of these systems in advancing research and improving medical practice.

Keywords: Intelligent control systems, biomedical instrumentation, drug delivery systems, robotic surgical instruments, Computational monitoring and modeling

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Jayashree Katti, Harsh Warkari, Prachi Yadav, Bhagyashri Chaudhari

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Approximately ten percent of individuals globally suffer from kidney stones, which can cause major side effects, including renal damage and blockage of the urinary tract. Traditional detection techniques depend on the manual evaluation of CT or X-ray images, which is not easy and may contain errors. With the aim to enhance kidney stone detection using medical imaging, this research explores various machine learning methods, such as Convolutional Neural Networks (CNN). By reviewing many machine learning algorithms, like ensemble techniques, Decision Tree, Random Forest, and Support Vector Machines (SVM), this study shows that machine learning tends to improve accuracy and reduce kidney stone detection time. According to the results of the earlier research, ensemble methods produced a classification accuracy of 97.95%, whereas the Decision Tree Classifier obtained an F1 score of 85.3%. Ensemble approaches gave a classification accuracy of 97.95%. Advanced techniques utilizing transfer learning, such as ALEXNET, achieved an accuracy rate of 96%.

Keywords: kidney stones, machine learning, medical imaging, CNN, transfer learning, decision tree, ensemble methods, random forest, SVM, ALEXNET

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Adam Abdullahi Mohammed

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Performance appraisal is an assessment made to ensure the level of a worker’s productivity in a given period of time. The appraisal system is divided into two categories that are traditional methods and modern methods, with emphasis based on the evaluation of work results. In the traditional approach of staff appraisal, which puts more emphasis on individual traits, supervisors are required to measure employees through interactions based on what they achieved with reference to job descriptions, as well as rating them based on questionnaires without staff interaction. These methods are not effective because staff may give biased information. The study will attempt to assess the effect of performance appraisal on employee productivity at Yobe State University, Damaturu. It is aimed at assessing the process, methods, and objectives of performance appraisal and its feedback to know how they affect the success of the appraisal, its results, and employee productivity. In this study, a quantitative research method is adopted in collecting and analyzing data, and a questionnaire will be used as data collecting instrument. As it is a case study, the target population is the staff of the department of Islamic Studies. The research will employ a census sampling technique where all the subjects in the target populations are given a chance to participate in the study. This sampling method was considered because the entire target population is considered researchable. The expected findings are that staff performance appraisal in the department of Islamic Studies has effects on employee productivity; this is to say if it is given due consideration and the needful being done will improve employee productivity.

Keywords: performance appraisal, employee productivity, Yobe state University, appraisal feedback

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Authors: Nasim Babazadeh

Abstract:

Growing demand for a quieter dwelling environment has turned the attention of construction companies to reducing the propagated noise of their project. In inner-city constructions, close distance between the construction site and surrounding buildings lessens the efficiency of passive noise control methods. Dwellers of the nearby areas may file complaints and lawsuits against the construction companies due to the emitted construction noise, thereby leading to the interruption of processes, compensation costs, or even suspension of the project. Therefore, construction noise should be predicted along with the project schedule. The advantage of managing the noise in the pre-construction phase is two-fold. Firstly, changes in the time plan and construction methods can be applied more flexibly. Thus, the costs related to rescheduling can be avoided. Secondly, noise-related legal problems are expected to be reduced. To implement noise mapping methods for the mentioned prediction, the required detailed information (such as the location of the noisy process, duration of the noisy work) can be exported from the 4D BIM model. The results obtained from the noise maps would be used to help the planners to define different work scenarios. The proposed approach has been applied for the foundation and earthwork of a site located in a residential area, and the obtained results are discussed.

Keywords: building information modeling, construction noise management, noise mapping, 4D BIM

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Authors: Eman A. Gani, Loay E. George, Faisel G. Mohammed, Kamal H. Sager

Abstract:

Digital retinal images are more appropriate for automatic screening of diabetic retinopathy systems. Unfortunately, a significant percentage of these images are poor quality that hinders further analysis due to many factors (such as patient movement, inadequate or non-uniform illumination, acquisition angle and retinal pigmentation). The retinal images of poor quality need to be enhanced before the extraction of features and abnormalities. So, the segmentation of retinal image is essential for this purpose, the segmentation is employed to smooth and strengthen image by separating the background and damaged areas from the overall image thus resulting in retinal image enhancement and less processing time. In this paper, methods for segmenting colored retinal image are proposed to improve the quality of retinal image diagnosis. The methods generate two segmentation masks; i.e., background segmentation mask for extracting the background area and poor quality mask for removing the noisy areas from the retinal image. The standard retinal image databases DIARETDB0, DIARETDB1, STARE, DRIVE and some images obtained from ophthalmologists have been used to test the validation of the proposed segmentation technique. Experimental results indicate the introduced methods are effective and can lead to high segmentation accuracy.

Keywords: retinal images, fundus images, diabetic retinopathy, background segmentation, damaged areas segmentation

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Authors: Alastair Hales, Xi Jiang

Abstract:

Numerical methods are used to evaluate the operation of confined face-to-face piezoelectric fan arrays as pitch, P, between the blades is varied. Both in-phase and counter-phase oscillation are considered. A piezoelectric fan consists of a fan blade, which is clamped at one end, and an extremely low powered actuator. This drives the blade tip’s oscillation at its first natural frequency. Sufficient blade tip speed, created by the high oscillation frequency and amplitude, is required to induce vortices and downstream volume flow in the surrounding air. A single piezoelectric fan may provide the ideal solution for low powered hot spot cooling in an electronic device, but is unable to induce sufficient downstream airflow to replace a conventional air mover, such as a convection fan, in power electronics. Piezoelectric fan arrays, which are assemblies including multiple fan blades usually in face-to-face orientation, must be developed to widen the field of feasible applications for the technology. The potential energy saving is significant, with a 50% power demand reduction compared to convection fans even in an unoptimised state. A numerical model of a typical piezoelectric fan blade is derived and validated against experimental data. Numerical error is found to be 5.4% and 9.8% using two data comparison methods. The model is used to explore the variation of pitch as a function of amplitude, A, for a confined two-blade piezoelectric fan array in face-to-face orientation, with the blades oscillating both in-phase and counter-phase. It has been reported that in-phase oscillation is optimal for generating maximum downstream velocity and flow rate in unconfined conditions, due at least in part to the beneficial coupling between the adjacent blades that leads to an increased oscillation amplitude. The present model demonstrates that confinement has a significant detrimental effect on in-phase oscillation. Even at low pitch, counter-phase oscillation produces enhanced downstream air velocities and flow rates. Downstream air velocity from counter-phase oscillation can be maximally enhanced, relative to that generated from a single blade, by 17.7% at P = 8A. Flow rate enhancement at the same pitch is found to be 18.6%. By comparison, in-phase oscillation at the same pitch outputs 23.9% and 24.8% reductions in peak downstream air velocity and flow rate, relative to that generated from a single blade. This optimal pitch, equivalent to those reported in the literature, suggests that counter-phase oscillation is less affected by confinement. The optimal pitch for generating bulk airflow from counter-phase oscillation is large, P > 16A, due to the small but significant downstream velocity across the span between adjacent blades. However, by considering design in a confined space, counterphase pitch should be minimised to maximise the bulk airflow generated from a certain cross-sectional area within a channel flow application. Quantitative values are found to deviate to a small degree as other geometric and operational parameters are varied, but the established relationships are maintained.

Keywords: piezoelectric fans, low energy cooling, power electronics, computational fluid dynamics

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

Abstract:

The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

Procedia PDF Downloads 363