Search results for: artificial kidney

Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

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In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: Karl-Erich Lindenschmidt, Apurba Das, Joel Trudell, Keanne Russell

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In this presentation, we explore options for mitigating ice-jam flooding along the Athabasca River in western Canada. Not only flood hazard, expressed in this case as the probability of flood depths and extents being exceeded, but also flood risk, in which annual expected damages are calculated. Flood risk is calculated, which allows a cost-benefit analysis to be made so that decisions on the best mitigation options are not based solely on flood hazard but also on the costs related to flood damages and the benefits of mitigation. The river ice model is used to simulate extreme ice-jam flood events with which scenarios are run to determine flood exposure and damages in flood-prone areas along the river. We will concentrate on three mitigation options – the placement of a dike, artificial breakage of the ice cover along the river, the installation of an ice-control structure, and the construction of a reservoir. However, any mitigation option is not totally failsafe. For example, dikes can still be overtopped and breached, and ice jams may still occur in areas of the river where ice covers have been artificially broken up. Hence, for all options, it is recommended that zoning of building developments away from greater flood hazard areas be upheld. Flood mitigation can have a negative effect of giving inhabitants a false sense of security that flooding may not happen again, leading to zoning policies being relaxed. (Text adapted from Lindenschmidt [2022] "Ice Destabilization Study - Phase 2", submitted to the Regional Municipality of Wood Buffalo, Alberta, Canada)

Keywords: ice jam, flood hazard, flood risk river ice modelling, flood risk

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Authors: Shadrack Mambo, Karim Djouani, Yskandar Hamam, Barend van Wyk, Patrick Siarry

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This paper discusses the current trends in medical image registration techniques and addresses the need to provide a solid theoretical foundation for research endeavours. Methodological analysis and synthesis of quality literature was done, providing a platform for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Research on medical image registration techniques assists clinical and medical practitioners in diagnosis of tumours and lesion in anatomical organs, thereby enhancing fast and accurate curative treatment of patients. Literature review aims to provide a solid theoretical foundation for research endeavours in image registration techniques. Developing a solid foundation for a research study is possible through a methodological analysis and synthesis of existing contributions. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in medical image registration techniques and also communicate to them, the contribution of this research in the field of image processing. The gaps identified in current techniques can be closed by use of artificial neural networks that form learning systems designed to minimise error function. The paper also suggests several areas of future research in the image registration.

Keywords: image registration techniques, medical images, neural networks, optimisaztion, transformation

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Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: Aarnav Singh, Jatin Moolchandani

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The proposed research work aims to build a query-based AI chatbot that can answer any question related to any topic. A chatbot is software that converses with users via text messages. In the proposed system, we aim to build a chatbot that generates a response based on the user’s query. For this, we use natural language processing to analyze the query and some set of texts to form a concise answer. The texts are obtained through web-scrapping and filtering all the credible sources from a web search. The objective of this project is to provide a chatbot that is able to provide simple and accurate answers without the user having to read through a large number of articles and websites. Creating an AI chatbot that can answer a variety of user questions on a variety of topics is the goal of the proposed research project. This chatbot uses natural language processing to comprehend user inquiries and provides succinct responses by examining a collection of writings that were scraped from the internet. The texts are carefully selected from reliable websites that are found via internet searches. This project aims to provide users with a chatbot that provides clear and precise responses, removing the need to go through several articles and web pages in great detail. In addition to exploring the reasons for their broad acceptance and their usefulness across many industries, this article offers an overview of the interest in chatbots throughout the world.

Keywords: Chatbot, Artificial Intelligence, natural language processing, web scrapping

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Authors: Paul Barasa Wanyama, Tom Wanyama

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Governments and development partners in low-income countries often prioritize building Water Sanitation and Hygiene (WaSH) infrastructure of healthcare facilities to improve maternal healthcare outcomes. However, the operation, maintenance, and utilization of this infrastructure are almost never considered. Many healthcare facilities in these countries use untreated water that is not monitored for quality or quantity. Consequently, it is common to run out of water while a patient is on their way to or in the operating theater. Further, the handwashing stations in healthcare facilities regularly run out of water or soap for months, and the latrines are typically not clean, in part due to the lack of water. In this paper, we present a system that uses Internet of Things (IoT), big data, cloud computing, and AI to initiate WaSH security in healthcare facilities, with a specific focus on maternal health. We have implemented smart sensors and actuators to monitor and control WaSH systems from afar to ensure their objectives are achieved. We have also developed a cloud-based system to analyze WaSH data in real time and communicate relevant information back to the healthcare facilities and their stakeholders (e.g., medical personnel, NGOs, ministry of health officials, facilities managers, community leaders, pregnant women, and new mothers and their families) to avert or mitigate problems before they occur.

Keywords: WaSH, internet of things, artificial intelligence, maternal health, rural communities, healthcare facilities

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Authors: Avijit Dey, Antony Gomes, Subir Chandra Dasgupta

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Tea (Camellia sinensis) is the most popular beverages in the world and is ranked second after the water. Tea has been considered as a health promoting beverage since ancient times due to its health-promoting activity. Recently, immunomodulatory, anti-arthritic, antioxidant, anticancer and cardioprotective activity of tea has been established. Very few studies have demonstrated the effect of high dose of black tea on health. The aim of the present study was to evaluate the role of low & high dose of Black Tea Extract (BTE) on the different physiological parameters of mother and pups during prenatal and postnatal developmental period in the experimental rodent. BTE was orally administered in LD (50mg BTE/kg/day) and HD (100mg BTE/kg/day) except control groups of rats (n=6/group) throughout the prenatal (day 0-21) and postnatal (day 21-42) periods. During prenatal period (0, 7th, 14th, 20th days) body weight, urinary calcium, magnesium, urea and creatinine was measured. In postnatal period physical (0, 10th, 21th days) parameters of pups like body weight, cranial length, cranial diameter, neck width, tail length, craniosacral length of pups were analyzed. Liver and lungs from pups and kidney spleen, etc. from mothers were collected on day 42 for histopathological studies. The comparative urine strip and morphology of RBC was also analyzed by SEM from mothers of different groups on day 42. The level of cytokines like IL-1alpha, IL-1beta, IL-6, IL-10, TNF-alpha were analysed by enzyme-linked immunosorbent assay (ELISA) on day 0, day 20 and day 42. The body weight of LD and HD mothers were also significantly (P<0.05) less than control mothers at 20th day of pregnancy and there was also significant changes in urinary calcium, urea, creatinine. The bio morphometric analysis of pups showed significant alteration (P<0.05) in HD groups relative to control. Some histological alterations were also observed in pups and mothers. Comparative urine strip analysis and morphology of RBC showed significant changes in treated groups. LD and HD treated mothers showed an increase in proinflammatory cytokines like IL-1beta, TNF-alpha and decrease in anti-inflammatory cytokine-like IL-10 on day 20 compared to PC mothers. This study clearly indicated that high dose of BTE possesses detrimental effect on pregnant mother and the pup. Further studies are in progress to elucidate the molecular mechanism of actions. This project work has been sponsored by National Tea Research Foundation vide Project Sanction No.: 17 (305)/2013/4423 dated 11th March, 2014. All experimental protocols described in the study were approved by animal ethics committee.

Keywords: black tea extract, pregnancy, prenatal and postnatal development, inflammation

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Samineh Barmaki, Ville Jokinen, Esko Kankuri

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We report a microfluidic chip that can be used to modify the gaseous microenvironment of a cell-culture in ambient atmospheric conditions. The aim of the study is to show the cellular response to nitric oxide (NO) under hypoxic (oxygen < 5%) condition. Simultaneously targeting to hypoxic and nitric oxide will provide an opportunity for NO‑based therapeutics. Studies on cellular responses to lowered oxygen concentration or to gaseous mediators are usually carried out under a specific macro environment, such as hypoxia chambers, or with specific NO donor molecules that may have additional toxic effects. In our study, the chip consists of a microfluidic layer and a cell culture well, separated by a thin gas permeable polydimethylsiloxane (PDMS) membrane. The main design goal is to separate the gas oxygen scavenger and NO donor solutions, which are often toxic, from the cell media. Two different types of gas exchangers, titled 'pool' and 'meander' were tested. We find that the pool design allows us to reach a higher level of oxygen depletion than meander (24.32 ± 19.82 %vs -3.21 ± 8.81). Our microchip design can make the cells culture more simple and makes it easy to adapt existing cell culture protocols. Our first application is utilizing the chip to create hypoxic conditions on targeted areas of cell culture. In this study, oxygen scavenger sodium sulfite generates hypoxia and its effect on human embryonic kidney cells (HEK-293). The PDMS membrane was coated with fibronectin before initiating cell cultures, and the cells were grown for 48h on the chips before initiating the gas control experiments. The hypoxia experiments were performed by pumping of O₂-depleted H₂O into the microfluidic channel with a flow-rate of 0.5 ml/h. Image-iT® reagent as an oxygen level responser was mixed with HEK-293 cells. The fluorescent signal appears on cells stained with Image-iT® hypoxia reagent (after 6h of pumping oxygen-depleted H₂O through the microfluidic channel in pool area). The exposure to different levels of O₂ can be controlled by varying the thickness of the PDMS membrane. Recently, we improved the design of the microfluidic chip, which can control the microenvironment of two different gases at the same time. The hypoxic response was also improved from the new design of microchip. The cells were grown on the thin PDMS membrane for 30 hours, and with a flowrate of 0.1 ml/h; the oxygen scavenger was pumped into the microfluidic channel. We also show that by pumping sodium nitroprusside (SNP) as a nitric oxide donor activated under light and can generate nitric oxide on top of PDMS membrane. We are aiming to show cellular microenvironment response of HEK-293 cells to both nitric oxide (by pumping SNP) and hypoxia (by pumping oxygen scavenger solution) in separated channels in one microfluidic chip.

Keywords: hypoxia, nitric oxide, microenvironment, microfluidic chip, sodium nitroprusside, SNP

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Authors: Jyoti Singh, Ranju Bansal

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Fighting pain and inflammation is a common problem faced by physicians while dealing with a wide variety of diseases. Since ancient time nonsteroidal anti-inflammatory agents (NSAIDs) and opioids have been the cornerstone of treatment therapy, however, the usefulness of both these classes is limited due to severe side effects. NSAIDs, which are mainly used to treat mild to moderate inflammatory pain, induce gastric irritation and nephrotoxicity whereas opioids show an array of adverse reactions such as respiratory depression, sedation, and constipation. Moreover, repeated administration of these drugs induces tolerance to the analgesic effects and physical dependence. Further discovery of selective COX-2 inhibitors (coxibs) suggested safety without any ulcerogenic side effects; however, long-term use of these drugs resulted in kidney and hepatic toxicity along with an increased risk of secondary cardiovascular effects. The basic approaches towards inflammation and pain treatment are constantly changing, and researchers are continuously trying to develop safer and effective anti-inflammatory drug candidates for the treatment of different inflammatory conditions such as osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, psoriasis and multiple sclerosis. Synthetic 3(2H)-pyridazinones constitute an important scaffold for drug discovery. Structure-activity relationship studies on pyridazinones have shown that attachment of a lactam at N-2 of the pyridazinone ring through a methylene spacer results in significantly increased anti-inflammatory and analgesic properties of the derivatives. Further introduction of the heterocyclic ring at lactam nitrogen results in improvement of biological activities. Keeping in mind these SAR studies, a new series of compounds were synthesized as shown in scheme 1 and investigated for anti-inflammatory, analgesic, anti-platelet activities and docking studies. The structures of newly synthesized compounds have been established by various spectroscopic techniques. All the synthesized pyridazinone derivatives exhibited potent anti-inflammatory and analgesic activity. Homoveratryl substituted derivative was found to possess highest anti-inflammatory and analgesic activity displaying 73.60 % inhibition of edema at 40 mg/kg with no ulcerogenic activity when compared to standard drugs indomethacin. Moreover, 2-substituted-4-benzo[d][1,3]dioxole-6-phenylpyridazin-3(2H)-ones derivatives did not produce significant changes in bleeding time and emerged as safe agents. Molecular docking studies also illustrated good binding interactions at the active site of the cyclooxygenase-2 (hCox-2) enzyme.

Keywords: anti-inflammatory, analgesic, pyridazin-3(2H)-one, selective COX-2 inhibitors

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Authors: Gaurav Kumar Sinha

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The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

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Authors: Mehmet Erdi Korkmaz, Mustafa Günay

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Nowadays, grinding is frequently replaced with hard turning for reducing set up time and higher accuracy. This paper focused on mathematical modeling of average surface roughness (Ra) in hard turning of AISI L2 grade (DIN 1.2210) cold work tool steel with ceramic tools. The steel was hardened to 60±1 HRC after the heat treatment process. Cutting speed, feed rate, depth of cut and tool nose radius was chosen as the cutting conditions. The uncoated ceramic cutting tools were used in the machining experiments. The machining experiments were performed according to Taguchi L27 orthogonal array on CNC lathe. Ra values were calculated by averaging three roughness values obtained from three different points of machined surface. The influences of cutting conditions on surface roughness were evaluated as statistical and experimental. The analysis of variance (ANOVA) with 95% confidence level was applied for statistical analysis of experimental results. Finally, mathematical models were developed using the artificial neural networks (ANN). ANOVA results show that feed rate is the dominant factor affecting surface roughness, followed by tool nose radius and cutting speed.

Keywords: ANN, hard turning, DIN 1.2210, surface roughness, Taguchi method

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Authors: Zhaohan Xie, Yining Yu, Mingliang Chen

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As research has shown a convergent tendency for aversion to AI recommendation, it is imperative to find a way to promote AI usage and better harness the technology. In the context of e-commerce, this study has found evidence that people show less avoidance of algorithms when recommending search products compared to experience products. This is due to people’s different attribution of mind to AI versus humans, as suggested by mind perception theory. While people hold the belief that an algorithm owns sufficient capability to think and calculate, which makes it competent to evaluate search product attributes that can be obtained before actual use, they doubt its capability to sense and feel, which is essential for evaluating experience product attributes that must be assessed after experience in person. The result of the behavioral investigation (Study 1, N=112) validated that consumers show low purchase intention to experience products recommended by AI. Further consumer neuroscience study (Study 2, N=26) using Event-related potential (ERP) showed that consumers have a higher level of cognitive conflict when faced with AI recommended experience product as reflected by larger N2 component, while the effect disappears for search product. This research has implications for the effective employment of AI recommenders, and it extends the literature on e-commerce and marketing communication.

Keywords: algorithm recommendation, consumer behavior, e-commerce, event-related potential, experience product, search product

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Authors: Qingxin Chen, Liben Jiang, Andrew Smith, Karim Hadjri

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Since 2007, the Chinese food industry has undergone serious food contamination in the baby dairy industry, especially milk powder contamination. One of the milk powder products was found to contain melamine and a significant number (294,000) of babies were affected by kidney stones. Due to growing concerns among consumers about food safety and protection, and high pressure from central government, companies must take radical action to ensure food quality protection through the use of an appropriate quality management system. Previously, though researchers have investigated the health and safety aspects of food industries and products, quality issues concerning food products in China have been largely over-looked. Issues associated with baby dairy products and their quality issues have not been discussed in depth. This paper investigates the impact of quality management systems on the Chinese baby dairy product industry. A literature review was carried out to analyse the use of quality management systems within the Chinese milk power market. Moreover, quality concepts, relevant standards, laws, regulations and special issues (such as Melamine, Flavacin M1 contamination) have been analysed in detail. A qualitative research approach is employed, whereby preliminary analysis was conducted by interview, and data analysis based on interview responses from four selected Chinese baby dairy product companies was carried out. Through the analysis of literature review and data findings, it has been revealed that for quality management system that has been designed by many practitioners, many theories, models, conceptualisation, and systems are present. These standards and procedures should be followed in order to provide quality products to consumers, but the implementation is lacking in the Chinese baby dairy industry. Quality management systems have been applied by the selected companies but the implementation still needs improvement. For instance, the companies have to take measures to improve their processes and procedures with relevant standards. The government need to make more interventions and take a greater supervisory role in the production process. In general, this research presents implications for the regulatory bodies, Chinese Government and dairy food companies. There are food safety laws prevalent in China but they have not been widely practiced by companies. Regulatory bodies must take a greater role in ensuring compliance with laws and regulations. The Chinese government must also play a special role in urging companies to implement relevant quality control processes. The baby dairy companies not only have to accept the interventions from the regulatory bodies and government, they also need to ensure that production, storage, distribution and other processes will follow the relevant rules and standards.

Keywords: baby dairy product, food quality, milk powder contamination, quality management system

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Authors: Morsli Mourad, Tahiri Mohamed, Samdi Azzeddine

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Casablanca is the biggest city in Morocco. It concentrates more than 60% of the economic and industrial activity of the kingdom. Its building and public works (BTP) sector is the leading source of inert waste scattered in open areas. This inert waste is a major challenge for the city of Casablanca, as it is not properly managed, thus causing a significant nuisance for the environment and the health of the population. Hence the vision of our project is to recycle and valorize concrete waste. In this work, we present concrete results in the exploitation of this abundant and permanent deposit. Typical wastes are concrete, clay and concrete bricks, ceramic tiles, marble panels, gypsum, scrap metal, wood . The work performed included: geolocation with a combination of artificial intelligence and Google Earth, estimation of the amount of waste per site, sorting, crushing, grinding, and physicochemical characterization of the samples. Then, we proceeded to the exploitation of the types of substrates to be developed: light cement, coating, and glue for ceramics... The said products were tested and characterized by X-ray fluorescence, specific surface, resistance to bending and crushing, etc. We will present in detail the main results of our research work and also describe the specific properties of each material developed.

Keywords: déchets de démolition et des chantiers de construction, logiciels de combinaison SIG, valorisation de déchets inertes, enduits, ciment leger, casablanca

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Malik Muhammad Akhtar, Tanzeel Khan

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Various natural and anthropogenic factors are affecting recharge processes in urban areas due to intense urban expansion; land-use/landcover change (LULC) and climate considerably influence the ecosystem functions. In Quetta, a terrible transformation of LULC has occurred due to an increase in human population and rapid urbanization over the past years; according to the Pakistan Bureau of Statistics, the increase of population from 252,577 in 1972 to 2,275,699 in 2017 shows an abrupt rise which in turn has affected the aquifer recharge capability, vegetation, and precipitation at Quetta. This study focuses on the influence of population growth and LULC on groundwater table level by employing multi-temporal, multispectral satellite data during the selected years, i.e. 2014, 2017, and 2020. The results of land classification showed that barren land had shown a considerable decrease, whereas the urban area has increased over time from 152.4sq/km in 2014 to 195.5sq/km in 2017 to 283.3sq/km in 2020, whereas surface-water area coverage has increased since 2014 because of construction of few dams around the valley. Rapid urbanization stresses limited hydrology resources, and this needs to be addressed to conserve/sustain the resources through educating the local community, awareness regarding water use and climate change, and supporting artificial recharge of the aquifers.

Keywords: climate changes, urbanization, GIS, land use, Quetta, watershed

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Authors: Zakir Hossain, Md. Saddam Hossain

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A nutritionally balanced diet and selection of appropriate species are important criteria in aquaculture. The present study was conducted to evaluate the effects of polyunsaturated fatty acids (PUFAs) and beta glucan containing diet on growth performance, feed utilization, maturation, immunity, early embryonic and larval development of endangered Pabdah catfish, Ompok pabda. In this study, squid extracted lipids and mushroom powder were used as the source of PUFAs and beta glucan, respectively, and formulated two isonitrogenous diets such as basal or control (CON) diet and treated (PBG) diet with maintaining 30% protein levels. During the study period, similar physicochemical conditions of water such as temperature, pH, and dissolved oxygen (DO) were 26.5±2 °C, 7.4±0.2, and 6.7±0.5 ppm, respectively in each cistern. The results showed that final mean body weight, final mean length gain, food conversion ratio (FCR), specific growth rate (SGR), food conversion efficiency (%), hepatosomatic index (HSI), kidney index (KI), and viscerosomatic index (VSI) were significantly (P<0.01 and P<0.05) higher in fish fed the PBG diet than that of fish fed the CON diet. The length-weight relationship and relative condition factor (K) of O. pabda were significantly (P<0.05) affected by the PBG diet. The gonadosomatic index (GSI), sperm viability, blood serum calcium ion concentrations (Ca²⁺), and vitellogenin level were significantly (P<0.05) higher in fish fed the PBG diet than that of fish fed the CON diet; which was used to the indication of fish maturation. During the spawning season, lipid granules and normal morphological structure were observed in the treated fish liver, whereas fewer lipid granules of liver were observed in the control group. Based on the immunity and stress resistance-related parameters such as hematological indices, antioxidant activity, lysozyme level, respiratory burst activity, blood reactive oxygen species (ROS), complement activity (ACH50 assay), specific IgM, brain AChE, plasma PGOT, and PGPT enzyme activity were significantly (P<0.01 and P<0.05) higher in fish fed the PBG diet than that of fish fed the CON diet. The fecundity, fertilization rate (92.23±2.69%), hatching rate (87.43±2.17 %) and survival (76.62±0.82%) of offspring were significantly higher (P˂0.05) in the PBG diet than in the control. Consequently, early embryonic and larval development was better in PBG treated group than in the control. Therefore, the present study showed that the polyunsaturated fatty acids (PUFAs) and beta glucan enriched experimental diet were more effective and achieved better growth, feed utilization, maturation, immunity, and spawning performances of O. pabda.

Keywords: polyunsaturated fatty acids, beta glucan, maturity, immunity, catfish

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Hung-Chu Chen, Han Qi, Bauke de Vries

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Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared.

Keywords: energy demand model, geographically weighted regression, normalized difference built-up index, normalized difference vegetation index, spatial statistics

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Authors: Uchenna Nkiruka Umeononihu, Adenike Kuku, Oludele Odekanyin, Olubunmi Babalola, Femi Agboola, Rapheal Okonji

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In this study, a lectin from the bulb of Dioscorea bulbifera was purified, characterised, and its acute and sub-acute toxicity was investigated with a view to evaluate its toxic effects in mice. The protein from the bulb was extracted by homogenising 50 g of the bulb in 500 ml of phosphate buffered saline (0.025 M) of pH 7.2, stirred for 3 hr, and centrifuged at the speed of 3000 rpm. Blood group and sugar specificity assays of the crude extract were determined. The lectin was purified in a two-step procedure- gel filtration on Sephadex G-75 and affinity chromatography on Sepharose 4-B arabinose. The degree of purity of the purified lectin was ascertained by SDS-polyacrylamide gel electrophoresis. Detection of covalently bound carbohydrate was carried out with Periodic Acid-Schiffs (PAS) reagent staining technique. Effects of temperature, pH, and EDTA on the lectin were carried out using standard methods. This was followed by acute toxicity studies via oral and subcutaneous routes using mice. The animals were monitored for mortality and signs of toxicity. The sub-acute toxicity studies were carried out using rats. Different concentrations of the lectin were administered twice daily for 5 days via the subcutaneous route. The animals were sacrificed on the sixth day; blood samples and liver tissues were collected. Biochemical assays (determination of total protein, direct bilirubin, Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), catalase (CAT), and superoxide dismutase (SOD)) were carried out on the serum and liver homogenates. The collected organs (heart, liver, kidney, and spleen) were subjected to histopathological analysis. The results showed that lectin from the bulbs of Dioscorea bulbifera agglutinated non-specifically the erythrocytes of the human ABO system as well as rabbit erythrocytes. The haemagglutinating activity was strongly inhibited by arabinose and dulcitol with minimum inhibitory concentrations of 0.781 and 6.25, respectively. The lectin was purified to homogeneity with native and subunit molecular weights of 56,273 and 29,373 Daltons, respectively. The lectin was thermostable up to 30 0C and lost 25 %, 33.3 %, and 100 % of its heamagglutinating activity at 40°C, 50°C, and 60°C, respectively. The lectin was maximally active at pH 4 and 5 but lost its total activity at pH eight, while EDTA (10 mM) had no effect on its haemagglutinating activity. PAS reagent staining showed that the lectin was not a glycoprotein. The sub-acute studies on rats showed elevated levels of ALT, AST, serum bilirubin, total protein in serum and liver homogenates suggesting damage to liver and spleen. The study concluded that the aerial bulb of D. bulbifera lectin was non-specific in its heamagglutinating activity and dimeric in its structure. The lectin shared some physicochemical characteristics with lectins from other Dioscorecea species and was moderately toxic to the liver and spleen of treated animals.

Keywords: Dioscorea bulbifera, heamagglutinin, lectin, toxicity

Procedia PDF Downloads 132

Authors: Idowu R. Akomolafe, Naven Chetty

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Background: The widespread medical application of ionizing radiation has raised public concern about radiation exposure and, thus, associated cancer risk. The production of reactive oxygen species and free radicals as a result of radiation exposure can cause severe damage to deoxyribonucleic acid (DNA) of cells, thus leading to biological effect. Radiotherapy is an excellent modality in the treatment of cancerous cells, comes with a few challenges. A significant challenge is the exposure of healthy cells surrounding the tumour to radiation. The last few decades have witnessed lots of attention shifted to plants, herbs, and natural product as an alternative to synthetic compound for radioprotection. Thus, the study investigated the radioprotective efficacy of Costus afer against whole-body radiation-induced haematological, histopathological disorder in mice. Materials and Method: Fifty-four mice were randomly divided into nine groups. Animals were pretreated with the extract of Costus afer by oral gavage for six days before irradiation. Control: 6 mice received feed and water only; 6 mice received feed, water, and 3Gy; 6 mice received feed, water, and 6Gy; experimental: 6 mice received 250 mg/kg extract; 6 mice received 500 mg/kg extract; 6 mice received 250 mg/kg extract and 3Gy; 6 mice received 500 mg/kg extract and 3Gy; 6 mice received 250 mg/kg extract and 6Gy; 6 mice received 500 mg/kg extract and 6Gy in addition to feeding and water. The irradiation was done at the Radiotherapy and Oncology Department of Grey's Hospital using linear accelerator (LINAC). Thirty-six mice were sacrificed by cervical dislocation 48 hours after irradiation, and blood was collected for haematology tests. Also, the liver and kidney of the sacrificed mice were surgically removed for histopathology tests. The remaining eighteen (18) mice were used for mortality and survival studies. Data were analysed by one-way ANOVA, followed by Tukey's multiple comparison test. Results: Prior administration of Costus afer extract decreased the symptoms of radiation sickness and caused a significant delay in the mortality as demonstrated in the experimental mice. The first mortality was recorded on day-5 post irradiation, and this happened to the group E- that is, mice that received 6Gy but no extract. There was significant protection in the experimental mice, as demonstrated in the blood counts against hematopoietic and gastrointestinal damage when compared with the control. The protection was seen in the increase in blood counts of experimental animals and the number of survivor. The protection offered by Costus afer may be due to its ability to scavenge free radicals and restore gastrointestinal and bone marrow damage produced by radiation. Conclusions: The study has demonstrated that exposure of mice to radiation could cause modifications in the haematological and histopathological parameters of irradiated mice. However, the changes were relieved by the methanol extract of Costus afer, probably through its free radical scavenging and antioxidant properties.

Keywords: costus afer, hematological, mortality, radioprotection, radiotherapy

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

Procedia PDF Downloads 122

Authors: G. S. Thyagaraju, U. P. Kulkarni

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Context-aware computing refers to a general class of computing systems that can sense their physical environment, and adapt their behaviour accordingly. Context aware computing makes systems aware of situations of interest, enhances services to users, automates systems and personalizes applications. Context-aware services have been introduced into mobile devices, such as PDA and mobile phones. In this paper we are presenting a novel approaches used to realize the context aware mobile. The context aware mobile phone (CAMP) proposed in this paper senses the users situation automatically and provides user context required services. The proposed system is developed by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rough sets theory based decision table. Bayesian Network to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the decision table based rules for service recommendation. To exemplify and demonstrate the effectiveness of the proposed methods, the context aware mobile phone is tested for college campus scenario including different locations like library, class room, meeting room, administrative building and college canteen.

Keywords: context aware mobile, fuzzy logic, decision table, Bayesian probability

Procedia PDF Downloads 368