Search results for: artificial recharge

Authors: I. S. R. Butswat, D. Zahraddeen

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This study identified and described some skill-based and necessity-driven entrepreneurship in animal agriculture (AA). AA is an integral segment of the world food industry, and provides a good and rapid source of income. The contribution of AA to the Sub-Saharan economy is quite significant, and there are still large opportunities that remain untapped in the sector. However, it is imperative to understand, simplify and package the various components of AA in order to pave way for rapid wealth creation, poverty eradication and women empowerment programmes in sub-Saharan Africa and other developing countries. The entrepreneurial areas of AA highlighted were animal breeding, livestock fattening, dairy production, poultry farming, meat production (beef, mutton, chevon, etc.), rabbit farming, wool/leather production, animal traction, animal feed industry, commercial pasture management, fish farming, sport animals, micro livestock production, private ownership of abattoirs, slaughter slabs, animal parks and zoos, among others. This study concludes that reproductive biotechnology such as oestrous synchronization, super-/multiple ovulation, artificial insemination and embryo transfer can be employed as a tool for improvement of genetic make-up of low-yielding animals in terms of milk, meat, egg, wool, leather production and other economic traits that will necessitate sustainable job and wealth creations.

Keywords: animal, agriculture, entreprenurship, wealth

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Authors: Leonie Laskowitz

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A self-confident appearance is a basic prerequisite for success in the world of work 4.0. Within a few seconds, people convey a first impression that usually lasts. Artificial intelligence is making it increasingly important how our virtual selves appear and communicate (nonverbally) in digital worlds such as the metaverse. In addition to the modified creation of an avatar, the field of photogrammetry is developing fast, creating exact likenesses of ourselves in virtual environments. Given the importance of self-representation in virtual space for future collaborations, it is important to investigate the impact of phenotype in virtual worlds and how an avatar type can profitably be used situationally. We analyzed the effect of self-similar versus desirable self-presentation as an avatar on one's self-awareness, considering various theoretical constructs in the area of self-awareness and stress stimuli. The avatars were arbitrarily created on the one hand and scanned on the other hand with the help of a lidar sensor, the state-of-the-art photogrammetry method. All subjects were exposed to the established Trier Social Stress Test. The results showed that especially insecure people prefer to create rather than be scanned when confronted with a stressful work situation. (1) If they are in a casual work environment and a relaxed situation, they prefer a 3D photorealistic avatar that reflects them in detail. (2) Confident people will give their avatar their true appearance in any situation, while insecure people would only do so for honesty and authenticity. (3) Thus, the choice of avatar type has considerable impact on self-confidence in different situations.

Keywords: avatar, virtual identity, self-presentation, metaverse, virtual reality, self-awareness

Procedia PDF Downloads 148

Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

Procedia PDF Downloads 254

Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Habibi Said Mustafa, Hiroko Ono

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Since 2006, Afghanistan is dealing with one of the most dramatic trend of urban movement in its history, cities and towns are expanding in size and number. Kabul is the capital of Afghanistan and as well as the fast-growing city in the Asia. The influx of the returnees from neighbor countries and other provinces of Afghanistan caused high rate of artificial growth which slums increased. As an unwanted consequence of this growth, today informal settlements have covered a vast portion of the city. Land Readjustment (LR) has proved to be an important tool for developing informal settlements and reorganizing urban areas but its implementation always varies from country to country and region to region within the countries. Consequently, to successfully develop the informal settlements in Kabul, we need to define an Afghan model of LR specifically for Afghanistan which needs to incorporate all those factors related to the socio-economic condition of the country. For this purpose, a part of the old city of Kabul has selected as a study area which is located near the Central Business District (CBD). After the further analysis and incorporating all needed factors, the result shows a positive potential for the implementation of an adaptable Land Readjustment model for Kabul city which is more sustainable and socio-economically friendly. It will enhance quality of life and provide better urban services for the residents. Moreover, it will set a vision and criteria by which sustainable developments shall proceed in other similar informal settlements of Kabul.

Keywords: adaptation, informal settlements, Kabul, land readjustment, preservation

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Moschos Vogiatzis, K. Perakis

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Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process.

Keywords: classification, land use/land cover, mapping, random forest

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Authors: Roya Ahmadi Ahangar, Hamid Madadyari

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The seeker optimization algorithm (SOA) is increasingly gaining popularity among the researchers society due to its effectiveness in solving some real-world optimization problems. This paper provides the load-frequency control method based on the SOA for removing oscillations in the power system. A three-zone power system includes a thermal zone, a hydraulic zone and a wind zone equipped with robust proportional-integral-differential (PID) controllers. The result of simulation indicates that load-frequency changes in the wind zone for the multi-zone system are damped in a short period of time. Meanwhile, in the oscillation period, the oscillations amplitude is not significant. The result of simulation emphasizes that the PID controller designed using the seeker optimization algorithm has a robust function and a better performance for oscillations damping compared to the traditional PID controller. The proposed controller’s performance has been compared to the performance of PID controller regulated with Particle Swarm Optimization (PSO) and. Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithms in order to show the superior capability of the proposed SOA in regulating the PID controller. The simulation results emphasize the better performance of the optimized PID controller based on SOA compared to the PID controller optimized with PSO, GA and ABC algorithms.

Keywords: load-frequency control, multi zone, robust PID controller, wind generation

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Authors: Nancy Ayman Kamal Mohamed Mehrz

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As the economies of other countries in the Mediterranean Basin, the tourism sector in our country has a high denominator in economics. Tourism businesses, which are building blocks of tourism, sector faces with a variety of problems during their activities. These problems faced make business efficiency and competition conditions of the businesses difficult. Most of the problems faced by the tourism businesses and the information of consumers about consumers’ rights were used in this study, which is conducted to determine the problems of tourism businesses in the Central Anatolia Region. It is aimed to contribute the awareness of staff and executives working at tourism sector and to attract attention of businesses active concurrently with tourism sector and legislators. E-tourism is among the issues that have recently been entered into the field of tourism. In order to achieve this type of tourism, Information and Communications Technology (or ICT) infrastructures as well as Co-governmental organizations and tourism resources are important. In this study, the opinions of managers and tourism officials about the e-tourism in Leman city were measured; it also surveyed the impact of level of digital literacy of managers and tourism officials on attracting tourists. This study was conducted. One of the environs of the Esfahan province. This study is a documentary – survey and the sources include library resources and also questionnaires. The results obtained indicate that if managers use ICT, it may help e-tourism to be developed in the region, and increasing managers’ beliefs on e-tourism and upgrading their level of digital literacy may affect e-tourism development.

Keywords: financial problems, the problems of tourism businesses, tourism businesses, internet, marketing, tourism, tourism management economic competitiveness, enhancing competitiveness

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: Basak Ozkendirci

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The discovery of chemical dyes, the development of regenerated fibers, and warp knitting technology have enormous effects on the fashion world. The trends created by the information obtained in the context of various studies today shape the fashion world. Trend analysts must follow scientific developments as well as sociological events, political developments and artwork to obtain healthy data on trends. Digital printing technologies have changed the dynamics of textile printing production and also the style of printed designs. Fashion designers already have started design 3D printed accessories and garments. The research fields like the internet of things, artificial intelligence, hologram technologies, mechatronics, energy storage systems, nanotechnology are seen as the technologies that will change the social life and economy of the future. It is clear that research carried out in these areas will affect the textiles of the future and whereat the trends of fashion. The article aims to create a future vision for trend researchers and designers by giving clues about the changes to be experienced in the fashion world. In the first part of the article, information about the scientific studies that are thought to shape the future is given, and the forecasting about how the inventions that can be obtained from these studies can be adapted at the textile are presented. In the second part of the article, examples of how the new generation of innovative textiles will affect the daily life experience of the user are given.

Keywords: biotextiles, fashion trends, nanotextiles, new materials, smart textiles, techno textiles

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Khalidou Ba, Abdelkrim Chahine, Mohamed Ebn Touhami

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A strong industrial interest is focused on the development of coatings for anticorrosion protection. In this context, phosphate composite materials are expanding strongly due to their chemical characteristics and their interesting physicochemical properties. Sol-gel coatings offer high homogeneity and purity that may lead to obtain coating presenting good adhesion to metal surface. The goal behind this work is to develop efficient coatings for corrosion protection of steel to extend its life. In this context, a sol gel process allowing to obtain thin film coatings on carbon steel with high resistance to corrosion has been developed. The optimization of several experimental parameters such as the hydrolysis time, the temperature, the coating technique, the molar ratio between precursors, the number of layers and the drying mode has been realized in order to obtain a coating showing the best anti-corrosion properties. The effect of these parameters on the microstructure and anticorrosion performance of the films sol gel coating has been investigated using different characterization methods (FTIR, XRD, Raman, XPS, SEM, Profilometer, Salt Spray Test, etc.). An optimized coating presenting good adhesion and very stable anticorrosion properties in salt spray test, which consists of a corrosive attack accelerated by an artificial salt spray consisting of a solution of 5% NaCl, pH neutral, under precise conditions of temperature (35 °C) and pressure has been obtained.

Keywords: sol gel, coating, corrosion, XPS

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Authors: Simon Arunga, Teddy Kwaga, Rita Kageni, Michael Gichangi, Nyawira Mwangi, Fred Kagwa, Rogers Mwavu, Amos Baryashaba, Luis F. Nakayama, Katharine Morley, Michael Morley, Leo A. Celi, Jessica Haberer, Celestino Obua

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Purpose: The main aim for creating the Multimodal Database of Retinal Images for Africa (MoDRIA) was to provide a publicly available repository of retinal images for responsible researchers to conduct algorithm development in a bid to curb the challenges of ophthalmic artificial intelligence (AI) in Africa. Methods: Data and retina images were ethically sourced from sites in Uganda and Kenya. Data on medical history, visual acuity, ocular examination, blood pressure, and blood sugar were collected. Retina images were captured using fundus cameras (Foru3-nethra and Canon CR-Mark-1). Images were stored on a secure online database. Results: The database consists of 7,859 retinal images in portable network graphics format from 1,988 participants. Images from patients with human immunodeficiency virus were 18.9%, 18.2% of images were from hypertensive patients, 12.8% from diabetic patients, and the rest from normal’ participants. Conclusion: Publicly available data repositories are a valuable asset in the development of AI technology. Therefore, is a need for the expansion of MoDRIA so as to provide larger datasets that are more representative of Sub-Saharan data.

Keywords: retina images, MoDRIA, image repository, African database

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Authors: Jaturong Matidtor, Krisna R. Torrissen, Saengtong Pongjareankit, Sudaporn Tongsiri, Jiraporn Rojtinnakorn

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Low survival rate has being particular problem in nursery of marble goby juvenile. The aim of this study was to investigate effect of garlic extract on protein digestive pancreatic enzymes, trypsin (T) and chymotrypsin (C). The marble goby were reared with commercial feed mixed garlic extract at concentration of 0 (control), 0.3, 0.5, 1.0, 3.0 and 5.0% (w/w) for 6 weeks. Analysis of the digestive enzymes at 2 and 6 weeks was performed. Growth parameters; weight gain (WG), specific growth rate (SGR) and feed efficiency (FE), were identified. For T, C and T/C at 2 weeks, values of T and T/C ratio of 0.3% (w/w) group showed significant difference (p < 0.05) with the highest values of 17685.64± 11981.77 U/mg protein and of 51.64 ± 27.46 U/mg protein, respectively. For C at 2 weeks, 0% (w/w) group showed the highest values of 16191.76± 2225.56 U/mg protein. Whereas value of T, C and T/C ratio at 6 weeks, there was no significant difference (p > 0.05). For growth performance, it significantly increased in all garlic extract fed groups (0.3-5.0%, w/w), both at 2 and 6 weeks. At 2 weeks, values of WG and SGR of 0.5% (w/w) group showed the highest values of 71.51 ± 1.60%, and 3.85 ± 0.07%, respectively. For FE, 0.3% (w/w) group showed the highest value of 60.21 ± 6.51%. At 6 weeks, it illustrated that all growth parameters of 5.0% (w/w) group were the highest values; WG = 35.06 ± 5.66%, SGR = 2.14 ± 0.30%, and FE = 5.86 ± 0.68%. We suggested that garlic extract could be available for protein digestive enzyme and growth enhancement in marble goby nursery with artificial feed. This result will be high benefit for commercial aquaculture of marble goby.

Keywords: marble goby, nursery, garlic extract, digestive enzyme, growth

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: Ana Paula Esteves, Diego S. Caetano, Louise L. B. Lomardo

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This paper presents an approach on the advantages of using adequate coverage in the zenithal lighting typology in various areas of architectural production, while at the same time to encourage to the design concerns inherent in this choice of roofing in Brazil. Understanding that sustainability needs to cover several aspects, a roofing system such as zenithal lighting system can contribute to the provision of better quality natural light for the interior of the building, which is related to the good health and welfare; it will also be able to contribute for the sustainable aspects and environmental needs, when it allows the generation of energy in semitransparent or opacity photovoltaic solutions and economize the artificial lightning. When the energy balance in the building is positive, that is, when the building generates more energy than it consumes, it may fit into the Net Zero Energy Building concept. The zenithal lighting systems could be an important ally in Brazil, when solved the burden of heat gains, participate in the set of pro-efficiency actions in search of "zero energy buildings". The paper presents comparative three cases of buildings that have used this feature in search of better environmental performance, both in light comfort and sustainability as a whole. Two of these buildings are examples in Europe: the Notley Green School in the UK and the Isofóton factory in Spain. The third building with these principles of shed´s roof is located in Brazil: the Ipel´s factory in São Paulo.

Keywords: natural lighting, net zero energy building, sheds, semi-transparent photovoltaics

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Elaid Bouchetob, Bouchra Nadji

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This research paper compares the efficiency and reliability (R(t)) of SiC-based and Si-based DC-DC buck converters in thin layer PV systems with an AI-based MPPT controller. Using Simplorer/Simulink simulations, the study assesses their performance under varying conditions. Results show that the SiC-based converter outperforms the Si-based one in efficiency and cost-effectiveness, especially in high temperature and low irradiance conditions. It also exhibits superior reliability, particularly at high temperature and voltage. Reliability calculation (R(t)) is analyzed to assess system performance over time. The SiC-based converter demonstrates better reliability, considering factors like component failure rates and system lifetime. The research focuses on the buck converter's role in charging a Lithium battery within the PV system. By combining the SiC-based converter and AI-based MPPT controller, higher charging efficiency, improved reliability, and cost-effectiveness are achieved. The SiC-based converter proves superior under challenging conditions, emphasizing its potential for optimizing PV system charging. These findings contribute insights into the efficiency, reliability, and reliability calculation of SiC-based and Si-based converters in PV systems. SiC technology's advantages, coupled with advanced control strategies, promote efficient and sustainable energy storage using Lithium batteries. The research supports PV system design and optimization for reliable renewable energy utilization.

Keywords: efficiency, reliability, artificial intelligence, sic device, thin layer, buck converter

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Authors: Abdul Walusansa

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The aim of this study was to assess the level of microbial pollution along Nakibiso stream. The study was carried out in polluted waters of Nakibiso stream, originating from Mbale municipality and running through ADRA Estates to Namatala Wetlands in Eastern Uganda. Four sites along the stream were selected basing on the activities of their vicinity. A total of 120 samples were collected in sterile bottles from the four sampling locations of the stream during the wet and dry seasons of the year 2011. The samples were taken to the National water and Sewerage Cooperation Laboratory for Analysis. Membrane filter technique was used to test for Erischerichia coli. Nitrogen, Phosphorus, pH, dissolved oxygen, electrical conductivity, total suspended solids, turbidity and temperature were also measured. Results for Nitrogen and Phosphorus for sites; 1, 2, 3 and 4 were 1.8, 8.8, 7.7 and 13.8 NH4-N mg/L; and 1.8, 2.1, 1.8 and 2.3 PO4-P mg/L respectively. Basing on these results, it was estimated that farmers use 115 and 24 Kg/acre of Nitrogen and Phosphorus respectively per month. Taking results for Nitrogen, the same amount of Nutrients in artificial fertilizers would cost $ 88. This shows that reuse of wastewater has a potential in terms of nutrients. The results for E. coli for sites 1, 2, 3 and 4 were 1.1 X 107, 9.1 X 105, 7.4 X 105, and 3.4 X 105 respectively. E. coli hence decreased downstream with statistically significant variations between sites 1 and 4. Site 1 had the highest mean E.coli counts. The bacterial contamination was significantly higher during the dry season when more water was needed for irrigation. Although the water had the potential for reuse in farming, bacterial contamination during both seasons was higher than 103 FC/100ml recommended by WHO for unrestricted Agriculture.

Keywords: E. coli, nitrogen, phosphorus, water reuse, waste water

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Lopita Pal, Suresh V. Madha

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Wetlands are areas of marsh, fen, peat land or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres. The rapidly expanding human population, large scale changes in land use/land cover, burgeoning development projects and improper use of watersheds all has caused a substantial decline of wetland resources in the world. Major degradations have been impacted from agricultural, industrial and urban developments leading to various types of pollutions and hydrological perturbations. Regular fishing activities and unsustainable grazing of animals are degrading the wetlands in a slow pace. The paper focuses on the spatio-temporal change detection of the area of the water body and the main cause of this depletion. The total area under study (22°19’87’’ N, 88°20’23’’ E) is a wetland region in West Bengal of 213 sq.km. The procedure used is the Normalized Difference Water Index (NDWI) from multi-spectral imagery and Landsat to detect the presence of surface water, and the datasets have been compared of the years 2016, 2006 and 1996. The result shows a sharp decline in the area of water body due to a rapid increase in the agricultural practices and the growing urbanization.

Keywords: spatio-temporal change, NDWI, urbanization, wetland

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Authors: Khairul Bariah Sulaiman, Tajul Aris Yang

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Fermentation is well known as an essential process in cocoa beans. Besides to develop the precursor of cocoa flavour, it also induce the colour changes in the beans.The fermentation process is reported to be influenced by duration of pod storage and fermentation. Therefore, this study was conducted to evaluate colour of Malaysian cocoa beans and how the pods storage and fermentation duration using shallow box technique will effect on it characteristics. There are two factors being studied ie duration of cocoa pod storage (0, 2, 4, and 6 days) and duration of cocoa fermentation (0, 1, 2, 3, 4 and 5 days). The experiment is arranged in 4 x 6 factorial design with 24 treatments and arrangement is in a Completely Randomised Design (CRD). The produced beans is inspected for colour changes under artificial light during cut test and divided into four groups of colour namely fully brown, purple brown, fully purple and slaty. Cut tests indicated that cocoa beans which are directly dried without undergone fermentation has the highest slaty percentage. However, application of pods storage before fermentation process is found to decrease the slaty percentage. In contrast, the percentages of fully brown beans start to dominate after two days of fermentation, especially from four and six days of pods storage batch. Whereas, almost all batch have percentage of fully purple less than 20%. Interestingly, the percentage of purple brown beans are scattered in the entire beans batch regardless any specific trend. Meanwhile, statistical analysis using General Linear Model showed that the pods storage has a significant effect on the colour characteristic of the Malaysian dried beans compared to fermentation duration.

Keywords: cocoa beans, colour, fermentation, shallow box

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Authors: Konysbek Aksaule

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The purpose of this work is to explore new and effective ways of teaching English to students who are studying a foreign language since the timeliness of the problem disclosed in this article is due to the high level of English proficiency that potential specialists must have due to high competition in the context of global globalization. The article presents an analysis of the feasibility and effectiveness of using an authentic feature film in teaching English to students. The methodological basis of the study includes an assessment of the level of students' proficiency in a foreign language, the stage of evaluating the film, and the method of selecting the film for certain categories of students. The study also contains a list of practical tasks that can be applied in the process of viewing and perception of an original feature film in a foreign language, and which are aimed at developing language skills such as speaking and listening. The results of this study proved that teaching English to students through watching an original film is one of the most effective methods because it improves speech perception, speech reproduction ability, and also expands the vocabulary of students and makes their speech fluent. In addition, learning English through watching foreign films has a huge impact on the cultural views and knowledge of students about the country of the language being studied and the world in general. Thus, this study demonstrates the high potential of using authentic feature film in English lessons for pedagogical science and methods of teaching English in general.

Keywords: university, education, students, foreign language, feature film

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Aishwarya Rai

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This study explores the transformative influence of generative AI on startups, businesses, and industries. We will explore how large businesses can benefit in the area of customer operations, where AI-powered chatbots can improve self-service and agent effectiveness, greatly increasing efficiency. In marketing and sales, generative AI could transform businesses by automating content development, data utilization, and personalization, resulting in a substantial increase in marketing and sales productivity. In software engineering-focused startups, generative AI can streamline activities, significantly impacting coding processes and work experiences. It can be extremely useful in product R&D for market analysis, virtual design, simulations, and test preparation, altering old workflows and increasing efficiency. Zooming into the retail and CPG industry, industry findings suggest a 1-2% increase in annual revenues, equating to $400 billion to $660 billion. By automating customer service, marketing, sales, and supply chain management, generative AI can streamline operations, optimizing personalized offerings and presenting itself as a disruptive force. While celebrating economic potential, we acknowledge challenges like external inference and adversarial attacks. Human involvement remains crucial for quality control and security in the era of generative AI-driven transformative innovation. This talk provides a comprehensive exploration of generative AI's pivotal role in reshaping businesses, recognizing its strategic impact on customer interactions, productivity, and operational efficiency.

Keywords: generative AI, digital transformation, LLM, artificial intelligence, startups, businesses

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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