Search results for: Artificial Bee Colony algorithm

Authors: Seyedabdolali Mortazavi Kamalabadi, Faegheh Taheran

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By giving an overview of previous arguments and findings concerned with the concept of competitive advantage, first, we define the overall concept of competitive advantage and discuss nuances of understanding such an important and strategic idea. Finally, by considering the major concerns of marketing academia, including globalization, AI-based technologies, consumer well-being, and internal coopetition between a firm’s units, fruitful avenues to be explored by future studies are presented in the form of research propositions. In the end, relevant gaps mentioned by numerous studies that are worth investigating are demonstrated.

Keywords: artificial intelligence, competitive advantage, consumer well-being, coopetition, globalization, literature review, temporary competitive advantage

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Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

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The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Khaled Ahmed Kadouh, Kamal Ali Albashiri

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This paper presents a designed algorithm involves improvement of transferring data over Simple Object Access Protocol (SOAP). The aim of this work is to establish whether using SOAP in exchanging XML messages has any added advantages or not. The results showed that XML messages without SOAP take longer time and consume more memory, especially with binary data.

Keywords: JAX-WS, SMTP, SOAP, web service, XML

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: Ki-Cheong Yoo, Yushik Han, Heejeung Sohn, Jinwoo Kim

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A ground collapse can be caused by natural and artificial factors. Ground collapses that have occurred frequently in Korea were observed and classified into different types by the main contributing factor. In this study, ground collapse induced by groundwater level disturbance in an excavation site was analyzed. Also, ground loosening region around the excavation site was detected and analyzed using non-destructive testing, such as GPR (Ground Penetrating Radar) survey and Electrical Resistivity. The result of the surveys showed that the ground was loosened widely over the surrounding area of the excavation due to groundwater discharge.

Keywords: electrical resistivity, ground collapse, groundwater level, GPR (ground penetrating radar)

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Authors: Tsang K. L. V., Lewis L. A., Griffith S., Tucker P.

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Background:  Many children with high incidence disabilities, such as autism spectrum disorder (ASD), struggle to participate in the community in a socially acceptable manner. There are limitations for clinical settings to provide natural, real-life scenarios for them to practice the life skills needed to meet their real-life challenges. Virtual reality (VR) offers potential solutions to resolve the existing limitations faced by clinicians to create simulated natural environments for their clients to generalize the facilitated skills. Research design: The research aimed to develop a prototype of an interactive VR system to provide realistic and immersive environments for clients to practice skills. The descriptive qualitative methodology is employed to design and develop the Artificial Interactive Reality Unified System (AIRUS) prototype, which provided insights on how to use advanced VR technology to create simulated real-life social scenarios and enable users to interact with the objects and people inside the virtual environment using natural eye-gazes, hand and body movements. The eye tracking (e.g., selective or joint attention), hand- or body-tracking (e.g., repetitive stimming or fidgeting), and facial tracking (e.g., emotion recognition) functions allowed behavioral data to be captured and managed in the AIRUS architecture. Impact of project: Instead of using external controllers or sensors, hand tracking software enabled the users to interact naturally with the simulated environment using daily life behavior such as handshaking and waving to control and interact with the virtual objects and people. The AIRUS protocol offers opportunities for breakthroughs in future VR-based psychosocial assessment and intervention in occupational therapy. Implications for future projects: AI technology can allow more efficient data capturing and interpretation of object identification and human facial emotion recognition at any given moment. The data points captured can be used to pinpoint our users’ focus and where their interests lie. AI can further help advance the data interpretation system.

Keywords: occupational therapy, psychosocial assessment and intervention, simulated interactive environment, virtual reality

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Authors: Daifeng Hao, Guang Feng, Jingfeng Zhao, Tao Li, Xiaoye Tuo

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Objective: To retrospectively analyze the clinical characteristics of incision infection with sternal osteomyelitis sinus tract after cardiac surgery and the operation method and therapeutic effect of filling and repairing with activated PRP gel. Methods: From March 2011 to October 2022, 142 cases of incision infection after cardiac surgery with sternal osteomyelitis sinus were retrospectively analyzed, and the causes of poor wound healing after surgery, wound characteristics, perioperative wound management were summarized. Treatment during operation, collection and storage process of autologous PRP before debridement surgery, PRP filling repair and activation method after debridement surgery, effect of anticoagulant drugs on surgery, postoperative complications and average wound healing time, etc.. Results: Among the cases in this group, 53.3% underwent coronary artery bypass grafting, 36.8% underwent artificial heart valve replacement, 8.2% underwent aortic artificial vessel replacement, and 1.7% underwent allogeneic heart transplantation. The main causes of poor incision healing were suture reaction, fat liquefaction, osteoporosis, diabetes, and metal allergy in sequence. The wound is characterized by an infected sinus tract. Before the operation, 100-150ml of PRP with 4 times the physiological concentration was collected separately with a blood component separation device. After sinus debridement, PRP was perfused to fill the bony defect in the middle of the sternum, activated with thrombin freeze-dried powder and calcium gluconate injection to form a gel, and the outer skin and subcutaneous tissue were sutured freely. 62.9% of patients discontinued warfarin during the perioperative period, and 37.1% of patients maintained warfarin treatment. There was no significant difference in the incidence of postoperative wound hematoma. The average postoperative wound healing time was 12.9±4.7 days, and there was no obvious postoperative complication. Conclusions: Application of activated PRP gel to fill incision infection with sternal osteomyelitis sinus after cardiac surgery has a less surgical injury and satisfactory and stable curative effect. It can completely replace the previously used pectoralis major muscle flap transplantation operation scheme.

Keywords: platelet-rich plasma, negative-pressure wound therapy, sternal osteomyelitis, cardiac surgery

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Authors: Le Zhao, Alain Nogaret

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We have built universal Central Pattern Generator (CPG) hardware by interconnecting Hodgkin-Huxley neurons with reciprocally inhibitory synapses. We investigate the dynamics of neuron oscillations as a function of the time delay between current steps applied to individual neurons. We demonstrate stimulus dependent switching between spiking polyrhythms and map the phase portraits of the neuron oscillations to reveal the basins of attraction of the system. We experimentally study the dependence of the attraction basins on the network parameters: the neuron response time and the strength of inhibitory connections.

Keywords: central pattern generator, winnerless competition principle, artificial neural networks, synapses

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Authors: Ahmed S. Fayed, Umima M. Mansour

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Three polyvinyl chloride membrane sensors were developed for the electrochemical evaluation of ferrous, manganese and zinc ions. The sensors were used for assaying metal ions in cervical mucus (CM) of Egyptian river buffalo-cows (Bubalus bubalis) as their levels vary dependent on cyclical hormone variation during different phases of estrus cycle. The presented sensors are based on using ionophores, β-cyclodextrin (β-CD), hydroxypropyl β-cyclodextrin (HP-β-CD) and sulfocalix-4-arene (SCAL) for sensors 1, 2 and 3 for Fe²⁺, Mn²⁺ and Zn²⁺, respectively. Dioctyl phthalate (DOP) was used as the plasticizer in a polymeric matrix of polyvinylchloride (PVC). For increasing the selectivity and sensitivity of the sensors, each sensor was enriched with a suitable complexing agent, which enhanced the sensor’s response. For sensor 1, β-CD was mixed with bathophenanthroline; for sensor 2, porphyrin was incorporated with HP-β-CD; while for sensor 3, oxine was the used complexing agent with SCAL. Linear responses of 10^-7-10^-2 M with cationic slopes of 53.46, 45.01 and 50.96 over pH range 4-8 were obtained using coated graphite sensors for ferrous, manganese and zinc ionic solutions, respectively. The three sensors were validated, according to the IUPAC guidelines. The obtained results by the presented potentiometric procedures were statistically analyzed and compared with those obtained by atomic absorption spectrophotometric method (AAS). No significant differences for either accuracy or precision were observed between the two techniques. Successful application for the determination of the three studied cations in CM, for the purpose to determine the proper time for artificial insemination (AI) was achieved. The results were compared with those obtained upon analyzing the samples by AAS. Proper detection of estrus and correct time of AI was necessary to maximize the production of buffaloes. In this experiment, 30 multi-parous buffalo-cows were in second to third lactation and weighting 415-530 kg, and were synchronized with OVSynch protocol. Samples were taken in three times around ovulation, on day 8 of OVSynch protocol, on day 9 (20 h before AI) and on day 10 (1 h before AI). Beside analysis of trace elements (Fe²⁺, Mn²⁺ and Zn²⁺) in CM using the three sensors, the samples were analyzed for the three cations and also Cu²⁺ by AAS in the CM samples and blood samples. The results obtained were correlated with hormonal analysis of serum samples and ultrasonography for the purpose of determining of the optimum time of AI. The results showed significant differences and powerful correlation with Zn²⁺ composition of CM during heat phase and the ovulation time, indicating that the parameter could be used as a tool to decide optimal time of AI in buffalo-cows.

Keywords: PVC Sensors, buffalo-cows, cyclodextrins, atomic absorption spectrophotometry, artificial insemination, OVSynch protocol

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Authors: Prasad Kalagura, B. Veeresh kumar

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We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit.

Keywords: embedded ARM7 processor, mouse pointer control, voice recognition

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper presents a Guidance and Control (G&C) strategy to address spacecraft maneuvering problem for future Rendezvous and Docking (RVD) missions. The proposed strategy allows safe and propellant efficient trajectories for space servicing missions including tasks such as approaching, inspecting and capturing. This work provides the validation test results of the G&C laws using a Hardware-In-the-Loop (HIL) setup with two robotic mockups representing the chaser and the target spacecraft. Through this paper, the challenges of the relative motion control in space are first summarized, and in particular, the constraints imposed by the mission, spacecraft and, onboard processing capabilities. Second, the proposed algorithm is introduced by presenting the formulation of constrained Model Predictive Control (MPC) to optimize the fuel consumption and explicitly handle the physical and geometric constraints in the system, e.g. thruster or Line-Of-Sight (LOS) constraints. Additionally, the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description and accordingly explained. The resulting convex optimization problem allows real-time implementation capability based on a detailed discussion on the computational time requirements and the obtained results with respect to the onboard computer and future trends of space processors capabilities. Finally, the performance of the algorithm is presented in the scope of a potential future mission and of the available equipment. The results also cover a comparison between the proposed algorithms with Linear–quadratic regulator (LQR) based control law to highlight the clear advantages of the MPC formulation.

Keywords: autonomous vehicles, embedded optimization, real-time experiment, rendezvous and docking, space robotics

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Seyedamir Makinejadsanij

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One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Fatemeh Babaeian, Nemai Chandra Karmakar

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Chipless RFID system is a wireless system for tracking and identification which use passive tags for encoding data. The advantage of using chipless RFID tag is having a planar tag which is printable on different low-cost materials like paper and plastic. The printed tag can be attached to different items in the labelling level. Since the price of chipless RFID tag can be as low as a fraction of a cent, this technology has the potential to compete with the conventional optical barcode labels. However, due to the passive structure of the tag, data processing of the reflection signal is a crucial challenge. The captured reflected signal from a tag attached to an item consists of different components which are the reflection from the reader antenna, the reflection from the item, the tag structural mode RCS component and the antenna mode RCS of the tag. All these components are summed up in both time and frequency domains. The effect of reflection from the item and the structural mode RCS component can distort/saturate the frequency domain signal and cause difficulties in extracting the desired component which is the antenna mode RCS. Therefore, it is required to study the reflection of the tag in both time and frequency domains to have a better understanding of the nature of the captured chipless RFID signal. The other benefits of this study can be to find an optimised encoding technique in tag design level and to find the best processing algorithm the chipless RFID signal in decoding level. In this paper, the reflection from a typical backscattered chipless RFID tag with six resonances is analysed, and different components of the signal are separated in both time and frequency domains. Moreover, the time domain signal corresponding to each resonator of the tag is studied. The data for this processing was captured from simulation in CST Microwave Studio 2017. The outcome of this study is understanding different components of a measured signal in a chipless RFID system and a discovering a research gap which is a need to find an optimum detection algorithm for tag ID extraction.

Keywords: antenna mode RCS, chipless RFID tag, resonance, structural mode RCS

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: S. Mahovic Poljacek, T. Tomasegovic, T. Cigula, D. Donevski, R. Szentgyörgyvölgyi, S. Jakovljevic

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In this paper, a degradation of the photopolymeric material (PhPM), used as printing plate in the flexography reproduction technique, caused by accelerated aging has been observed. Since the basis process for production of printing plates from the PhPM is a radical cross-linking process caused by exposing to UV wavelengths, the assumption was that improper storage or irregular handling of the PhPM plate can change the surface and structure characteristics of the plates. Results have shown that the aging process causes degradation in the structure and changes in the surface of the PhPM printing plate.

Keywords: aging process, artificial treatment, flexography, photopolymeric material (PhPM)

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: N. Amraoui, D. Hammoutene

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We have performed a theoretical study using dispersion-corrected Density Functional Methods to evaluate a variety of artificial nucleobases as candidates for metal-mediated Hoogsteen-type base pairs. We focus on A-M-T Hoogsteen-type base pair with M=Co(II), Ru(I), Ni(I). All calculations are performed using (ADF 09) program. Metal-mediated Hoogsteen-type base pairs are studied as drug candidates, their geometry optimizations are performed at ZORA/TZ2P/BLYP-D level. The molecular geometries and different energies as total energies, coordination energies, Pauli interactions, orbital interactions and electrostatic energies are determined.

Keywords: chemistry, biology, density functional method, orbital interactions

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Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models, and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. It is important to design systems based on structural analysis, research, and evaluation of efficiency indicators. One of the important efficiency criteria is the reliability of the system, which depends on the components of the structure. Quantifying the reliability of large-scale systems is a computationally complex process, and it is advisable to perform it with the help of a computer. Logical-probabilistic modeling is one of the effective means of describing the structure of a complex system and quantitatively evaluating its reliability, which was the basis of our application. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of “weights” of elements of system. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research, and designing of optimal structure systems are carried out.

Keywords: complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability of systems, “weights” of elements

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Authors: Farahnaz Motamedi Sedeh, Homayoon Mahravani, Parvin Shawrang, Mehdi Behgar

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Most countries use inactivated binary ethylenimine (BEI) vaccines to control and prevent Foot-and-Mouth Disease (FMD). However, this vaccine induces a short-term humoral immune response in animals. This study investigated the cellular and humoral immune responses in homologous and prime-boost (PB) groups in the BALB/c mouse model. FMDV strain O/IRN/1/2007 was propagated in the BHK-21 cell line and inactivated by three methods, including a chemical with BEI to produce a conventional vaccine (CV), a gamma irradiation vaccine (GIV), and an electron irradiated vaccine (EIV). Three vaccines were formulated with the adjuvant aluminum hydroxide gel. In addition, a DNA vaccine was prepared by amplifying the virus VP1 gene pcDNA3.1 plasmid. In addition, the plasmid encoding the granulocyte-macrophage colony-stimulating factor gene (GM-CSF) was used as a molecular adjuvant. Eleven groups of five mice each were selected, and the vaccines were administered as homologous and heterologous strategy prime-boost (PB) in three doses two weeks apart. After the evaluation of neutralizing antibodies, interleukin (IL)-2, IL-4, IL-10, interferon-gamma (INF-γ), and MTT assays were compared in the different groups. The pcDNA3.1+VP1 cassette was prepared and confirmed as a DNA vaccine. The virus was inactivated by gamma rays and electron beams at 50 and 55 kGy as GIV and EIV, respectively. Splenic lymphocyte proliferation in the inactivated vaccinated homologous groups was significantly lower (P≤0.05) compared with the heterologous prime-boosts (PB1, PB2, PB3) and DNA + GM-CSF groups (P≤0.05). The highest SNT titer was observed in the inactivated vaccine groups. IFN-γ and IL-2 were higher in the vaccinated groups. It was found that although there was a protective humoral immune response in the groups with inactivated vaccine, there was adequate cellular immunity in the group with the DNA vaccine. However, the strongest cellular and humoral immunity was observed in the PB groups. The primary injection was accompanied by DNA vaccine + GM-CSF and boosted injection with GIV or CV.

Keywords: foot and mouth disease, irradiated vaccine, immune responses, DNA vaccine, prime boost strategy

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Authors: Qunying Yuan, Manjula Bomma, Adrian Rhoden, Zhigang Xiao

Abstract:

Selenium is an essential micronutrient for all mammals and plays an important role in maintaining human physiological functions. The potential applications of selenium as food supplements, cancer-prevention, antimicrobial and anti-inflammatory agents have been investigated in biomedicine and food sciences. Nanoscale of selenium is of particular interest due to its better biocompatibility, higher bioavailability, lower toxicity, more homogeneous distribution, and presumptive controlled release of substances. The objective of this study is to explore whether selenium nanoparticle (SeNP) has the potential to be used as a food preservative to reduce food spoilage. SeNPs were synthesized through ascorbic acid reduction of sodium selenite using the bovine serum albumin (BSA) as capping and stabilizing agent. The chemically synthesized SeNPs had a spherical conformation and a size of 22.8 ± 4.7 nm. FTIR analysis confirmed that the nanoparticles were covered with BSA. We further tested the antimicrobial activity of these SeNPs against common food-borne bacteria. Colony forming unit assay showed that SeNPs exhibited good inhibition on the growth of Listeria Monocytogens (ATCC15313), Staphylococcus epidermidis (ATCC 700583) starting at 0.5µg/mL, but only a moderate inhibitory effect on the growth of Staphylococcus aureus (ATCC12600) and Vibrio alginolyticus (ATCC 33787) at a concentration higher than 10µg/mL and 2.5µg/mL, respectively. There was a mild effect against the growth Salmonella enterica (ATCC19585) when the concentration reached 15µg/mL. No inhibition was observed in the growth of Enterococcus faecalis (ATCC 19433). Surprisingly, SeNPs appeared to promote the growth of Vibrio parahaemolyticus (ATCC43996) and Salmonella enterica (ATCC49284) at 30 µg/mL and above. Our preliminary data suggested that the chemically synthesized SeNPs may be able to inhibit some food-borne bacteria, and SeNP as a food preservative should be used with caution. We will explore the mechanisms of the inhibitory action of chemically synthesized SeNPs on bacterial growth and whether the SeNPs are able to inhibit the development of biofilm and antibiotic resistance.

Keywords: antimicrobial, food-borne bacteria, nanoparticles, selenium

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Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov

Abstract:

Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models.

Keywords: neural networks, sponsored search, web advertisement, click prediction, click-through rate

Procedia PDF Downloads 572

Authors: Zeyad Abdelmageid, Xianbin Wang

Abstract:

Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

Procedia PDF Downloads 116

Authors: K. Sathishkumar, V. Thiagarasu

Abstract:

Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

Procedia PDF Downloads 323

Authors: Magdy Roman, Mostafa Shoeib, Mostafa Rostom

Abstract:

Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods.

Keywords: path planning, collision avoidance, convergence, computer vision, mobile robots

Procedia PDF Downloads 192

Authors: Mayar Almasri, Rosa Helmi, Rayana Enany

Abstract:

This report describes the features of AutoCAD software released by Adobe. It explains how the program makes it easier for engineers and designers and reduces their time and effort spent using AutoCAD. Moreover, it highlights how AutoCAD works, how some of the commands used in it, such as Shortcut, make it easy to use, and features that make it accurate in measurements. The results of the report show that most users of this program are designers and engineers, but few people know about it and find it easy to use. They prefer to use it because it is easy to use, and the shortcut commands shorten a lot of time for them. The feature got a high rate and some suggestions for improving AutoCAD in Aperture, but it was a small percentage, and the highest percentage was that they didn't need to improve the program, and it was good.

Keywords: artificial intelligence, design, planning, commands, autodesk, dimensions

Procedia PDF Downloads 128

Authors: Mohammed Alrefaie, Yaser Miaji

Abstract:

Greenhouses provide a convenient means to grow plants in the best environment. They achieve this by trapping heat from the sunlight and using artificial means to enhance the environment of the greenhouse. This includes controlling factors such as air flow, light intensity and amount of water among others that can have a big impact on plant growth. The aim of the greenhouse is to give maximum yield from plants possible. This report details the development and testing of greenhouse environment control system that can regulate light intensity, airflow and power supply inside the greenhouse. The details of the module development to control these three factors along with results of testing are presented.

Keywords: greenhouse, control system, light intensity, comprehensive environment

Procedia PDF Downloads 480

Authors: Haya Salah, Srinivas Sharan

Abstract:

Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

Procedia PDF Downloads 120