Search results for: edge intelligence

Authors: Henning Lahmann, Benjamyn I. Scott, Bart Custers

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Suboptimal adoption of AI in defence organisations carries risks for the protection of the freedom, safety, and security of society. Despite the vast opportunities that defence AI-technology presents, there are also a variety of ethical, legal, and societal concerns. To ensure the successful use of AI technology by the military, ethical, legal, and societal aspects (ELSA) need to be considered, and their concerns continuously addressed at all levels. This includes ELSA considerations during the design, manufacturing and maintenance of AI-based systems, as well as its utilisation via appropriate military doctrine and training. This raises the question how defence organisations can remain strategically competitive and at the edge of military innovation, while respecting the values of its citizens. This paper will explain the set-up and share preliminary results of a 4-year research project commissioned by the National Research Council in the Netherlands on the ethical, legal, and societal aspects of AI in defence. The project plans to develop a future-proof, independent, and consultative ecosystem for the responsible use of AI in the defence domain. In order to achieve this, the lab shall devise a context-dependent methodology that focuses on the ‘analysis’, ‘design’ and ‘evaluation’ of ELSA of AI-based applications within the military context, which include inter alia unmanned aircraft. This is bolstered as the Lab also recognises and complements the existing methods in regards to human-machine teaming, explainable algorithms, and value-sensitive design. Such methods will be modified for the military context and applied to pertinent case-studies. These case-studies include, among others, the application of autonomous robots (incl. semi- autonomous) and AI-based methods against cognitive warfare. As the perception of the application of AI in the military context, by both society and defence personnel, is important, the Lab will study how these perceptions evolve and vary in different contexts. Furthermore, the Lab will monitor – as they may influence people’s perception – developments in the global technological, military and societal spheres. Although the emphasis of the research project is on different forms of AI in defence, it focuses on several case studies. One of these case studies is on unmanned aircraft, which will also be the focus of the paper. Hence, ethical, legal, and societal aspects of unmanned aircraft in the defence domain will be discussed in detail, including but not limited to privacy issues. Typical other issues concern security (for people, objects, data or other aircraft), privacy (sensitive data, hindrance, annoyance, data collection, function creep), chilling effects, PlayStation mentality, and PTSD.

Keywords: autonomous weapon systems, unmanned aircraft, human-machine teaming, meaningful human control, value-sensitive design

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Authors: Amin Nazifi, Amir Raki, Doga Istanbulluoglu

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The incorporation of technological advancements into financial services, commonly referred to as Fintech, is primarily aimed at promoting services that are accessible, convenient, and inclusive, thereby benefiting both consumers and businesses. Fintech services employ a variety of technologies, including Artificial Intelligence (AI), blockchain, and big data, to enhance the efficiency and productivity of traditional services. Cryptocurrency, a component of Fintech, is projected to be a trillion-dollar industry, with over 320 million consumers globally investing in various forms of cryptocurrencies. However, these potentially transformative services can also lead to adverse outcomes. For instance, recent Fintech innovations have been increasingly linked to misconduct and disservice, resulting in serious implications for consumer well-being. This could be attributed to the ease of access to Fintech, which enables adults to trade cryptocurrencies, shares, and stocks via mobile applications. However, there is little known about the darker aspects of technological advancements, such as Fintech. Hence, this study aims to generate scholarly insights into the design of robust and resilient Fintech services that can add value to businesses and enhance consumer well-being. Using a mixed-method approach, the study will investigate the personal and contextual factors influencing consumers’ adoption and usage of technology innovations and their impacts on consumer well-being. First, semi-structured interviews will be conducted with a sample of Fintech users until theoretical saturation is achieved. Subsequently, based on the findings of the first study, a quantitative study will be conducted to develop and empirically test the impacts of these factors on consumers’ well-being using an online survey with a sample of 300 participants experienced in using Fintech services. This study will contribute to the growing Transformative Service Research (TSR) literature by addressing the latest priorities in service research and shedding light on the impact of fintech services on consumer well-being.

Keywords: consumer well-being, financial literacy, Fintech, service innovation

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Authors: Mostafa Mjahed

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This paper concerns the control of a nonlinear system using two different methods, reference model and genetic algorithm. The quadcopter is a nonlinear unstable system, which is a part of aerial robots. It is constituted by four rotors placed at the end of a cross. The center of this cross is occupied by the control circuit. Its motions are governed by six degrees of freedom: three rotations around 3 axes (roll, pitch and yaw) and the three spatial translations. The control of such system is complex, because of nonlinearity of its dynamic representation and the number of parameters, which it involves. Numerous studies have been developed to model and stabilize such systems. The classical PID and LQ correction methods are widely used. If the latter represent the advantage to be simple because they are linear, they reveal the drawback to require the presence of a linear model to synthesize. It also implies the complexity of the established laws of command because the latter must be widened on all the domain of flight of these quadcopter. Note that, if the classical design methods are widely used to control aeronautical systems, the Artificial Intelligence methods as genetic algorithms technique receives little attention. In this paper, we suggest comparing two PID design methods. Firstly, the parameters of the PID are calculated according to the reference model. In a second phase, these parameters are established using genetic algorithms. By reference model, we mean that the corrected system behaves according to a reference system, imposed by some specifications: settling time, zero overshoot etc. Inspired from the natural evolution of Darwin's theory advocating the survival of the best, John Holland developed this evolutionary algorithm. Genetic algorithm (GA) possesses three basic operators: selection, crossover and mutation. We start iterations with an initial population. Each member of this population is evaluated through a fitness function. Our purpose is to correct the behavior of the quadcopter around three axes (roll, pitch and yaw) with 3 PD controllers. For the altitude, we adopt a PID controller.

Keywords: quadcopter, genetic algorithm, PID, fitness, model, control, nonlinear system

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Authors: Muhammad Safi, Abdul Manan

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In recent years, research in aircraft avionics systems (i.e., radars and antennas) has grown revolutionary. Aircraft technology is experiencing an increasing inclination from all mechanical to all electrical aircraft, with the introduction of inhabitant air vehicles and drone taxis over the last few years. This develops an overriding need to summarize the history, latest trends, and future development in aircraft avionics research for a better understanding and development of new technologies in the domain of avionics systems. This paper focuses on the future trends in antennas and phased arrays for avionics systems. Along with the general overview of the future avionics trend, this work describes the review of around 50 high-quality research papers on aircraft communication systems. Electric-powered aircraft have been a hot topic in the modern aircraft world. Electric aircraft have supremacy over their conventional counterparts. Due to increased drone taxi and urban air mobility, fast and reliable communication is very important, so concepts of Broadband Integrated Digital Avionics Information Exchange Networks (B-IDAIENs) and Modular Avionics are being researched for better communication of future aircraft. A Ku-band phased array antenna based on a modular design can be used in a modular avionics system. Furthermore, integrated avionics is also emerging research in future avionics. The main focus of work in future avionics will be using integrated modular avionics and infra-red phased array antennas, which are discussed in detail in this paper. Other work such as reconfigurable antennas and optical communication, are also discussed in this paper. The future of modern aircraft avionics would be based on integrated modulated avionics and small artificial intelligence-based antennas. Optical and infrared communication will also replace microwave frequencies.

Keywords: AI, avionics systems, communication, electric aircrafts, infra-red, integrated avionics, modular avionics, phased array, reconfigurable antenna, UAVs

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Yi Yang, Jiasong Sun

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Almost every aspect of our daily lives is now intertwined with some degree of human-computer interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology, and more. Our HCI courses, named the Media and Cognition course, are constantly updated to reflect state-of-the-art technological advancements such as virtual reality, augmented reality, and artificial intelligence-based interactions. For more than a decade, our course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which have gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. Our latest version of the Human-Computer Interaction course practices a semantic communication platform based on AI-generated techniques. The purpose of this semantic communication is twofold: to extract and transmit task-specific information while ensuring efficient end-to-end communication with minimal latency. An AI-generated semantic communication platform evaluates the retention of signal sources and converts low-retain ability visual signals into textual prompts. These data are transmitted through AI-generated techniques and reconstructed at the receiving end; on the other hand, visual signals with a high retain ability rate are compressed and transmitted according to their respective regions. The platform and associated research are a testament to our students' growing ability to independently investigate state-of-the-art technologies.

Keywords: human-computer interaction, media and cognition course, semantic communication, retainability, prompts

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Bander Alzahrani, Mohammed Alreshoodi

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Information-centric networking (ICN) using architectures such as the Publish-Subscribe Internet Technology (PURSUIT) has been proposed as a new networking model that aims at replacing the current used end-centric networking model of the Internet. This emerged model focuses on what is being exchanged rather than which network entities are exchanging information, which gives the control plane functions such as routing and host location the ability to be specified according to the content items. The forwarding plane of the PURSUIT ICN architecture uses a simple and light mechanism based on Bloom filter technologies to forward the packets. Although this forwarding scheme solve many problems of the today’s Internet such as the growth of the routing table and the scalability issues, it is vulnerable to brute force attacks which are starting point to distributed- denial-of-service (DDoS) attacks. In this work, we design and analyze a novel source-routing and information delivery technique that keeps the simplicity of using Bloom filter-based forwarding while being able to deter different attacks such as denial of service attacks at the ingress of the network. To achieve this, special forwarding nodes called Edge-FW are directly attached to end user nodes and used to perform a security test for malicious injected random packets at the ingress of the path to prevent any possible attack brute force attacks at early stage. In this technique, a core entity of the PURSUIT ICN architecture called topology manager, that is responsible for finding shortest path and creating a forwarding identifiers (FId), uses a cryptographically secure hash function to create a 64-bit hash, h, over the formed FId for authentication purpose to be included in the packet. Our proposal restricts the attacker from injecting packets carrying random FIds with a high amount of filling factor ρ, by optimizing and reducing the maximum allowed filling factor ρm in the network. We optimize the FId to the minimum possible filling factor where ρ ≤ ρm, while it supports longer delivery trees, so the network scalability is not affected by the chosen ρm. With this scheme, the filling factor of any legitimate FId never exceeds the ρm while the filling factor of illegitimate FIds cannot exceed the chosen small value of ρm. Therefore, injecting a packet containing an FId with a large value of filling factor, to achieve higher attack probability, is not possible anymore. The preliminary analysis of this proposal indicates that with the designed scheme, the forwarding function can detect and prevent malicious activities such DDoS attacks at early stage and with very high probability.

Keywords: forwarding identifier, filling factor, information centric network, topology manager

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Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: A. S. Adegoke, O. N. Popoola

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The central purpose of management in any organization is that of coordinating the efforts of people towards the achievement of its goal. Effective and productive management is the function of leadership. Leadership plays a critical role in helping groups, organizations and societies to achieve their goals. Factors considered to make leadership to be effective are intelligence, social maturity, inner motivation and achievement drives and lastly, human relations attitudes. The factors affecting leadership style and effectiveness were examined. Also, the study examined which of the various leadership style best befits an organization and discussed the ways in which the style was determined. In order to meet the objectives of this study, different types of methods of data gathering were carried out. The methods include data from primary and secondary sources. The primary sources include personal interview, personal observation, and questionnaire while data from secondary sources were derived from various books, journal write up and other documentary records. Data were collected from respondents through questionnaire, and the field research carried out through oral interview to test each of the related hypotheses. From the data analysed it was determined that 45% strongly agreed that leadership traits are inborn not acquired and 28.3% agreed that leadership traits are inborn, while 11.7% and 10% strongly disagreed and disagreed respectively and 5% were undecided. 48.4% strongly agreed, and 43.3% agreed that environmental factors determined the appropriate style of leadership to be employed while 3.3% strongly disagreed, 1.7% disagreed and 3.3% were undecided. From the study, no single style of leadership is appropriate in any situation instead of concentrating on single leadership style; leader can vary approaches depending on forces in the leaders, characteristic of the subordinates, situational forces of the organization, lastly the expectations and behaviour of superior.

Keywords: hypothesis, leadership, management, organization

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Authors: Adeleye Johnson Adelagunayeja

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The concept of making cities, communities, and neighborhoods smart, intelligent, and responsive is relatively new to Africa and its urban renewal agencies. Efforts must be made by relevant agencies to begin a holistic review of the implementation of infrastructural facilities and urban renewal methodologies that will revolve around the appreciation and application of artificial intelligence. The propagation of the ideals and benefits of the smart city concept are key factors that can encourage governments of African nations, the African Union, and other regional organizations in Africa to embrace the ideology. The ability of this smart city concept to curb insecurities – armed robbery, assassination, terrorism, and civil disorder – is one major reason, amongst others, why African governments must speedily embrace this contemporary developmental concept whose time has come! The seamlessness to access information and virtually cross-pollinate ideas with people living in already established smart cities, when combined with the great efficiency that the emergence of smart cities brings with it, are other reasons why Africa must come up with action plans that can enable the existing cities to metamorphose into smart cities. Innovations will be required to enable Africa to develop a smart city concept that will be compatible with the basic patterns of livelihood because the essence of the smart city evolution is to make life better for people to co-exist, to be productive and to enjoy standard infrastructural facilities. This research paper enumerates the multifaceted adaptive factors that have the potentials of making the adoption of smartcity concept in Africa seamless. It also proffers solutions to potential bottlenecks capable of undermining the execution of the smart city concept in Africa.

Keywords: smartcity compactibility innovation Africa government evolution, Africa as global village member, evolution in Africa, ways to make Africa adopt smartcity, localizing smartcity concept in Africa, bottleneck to smartcity developmet in Africa

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: Kherbache Mehdi

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Within the framework of the validation of the Master in business administration marketing and sales in INSIM institute international in management Blida, we get the opportunity to do a professional internship in Sonelgaz Enterprise and a thesis. The thesis deals with the integration of social networking in the communication strategy of a company. The problematic is: How communicate with social network can be a solution for companies? The challenges stressed by this thesis were to suggest limits and recommendations to Sonelgaz Enterprise concerning social networks. The whole social networks represent more than a billion people as a potential target for the companies. Thanks to research and a qualitative approach, we have identified tree valid hypothesis. The first hypothesis allows confirming that using social networks cannot be ignored by any company in its communication strategy. However, the second hypothesis demonstrates that it’s necessary to prepare a strategy that integrates social networks in the communication plan of the company. The risk of this strategy is very limited because failure on social networks is not a restraint for the enterprise, social networking is not expensive and, a bad image which could result from it is not as important in the long-term. Furthermore, the return on investment is difficult to evaluate. Finally, the last hypothesis shows that firms establish a new relation between consumers and brands thanks to the proximity allowed by social networks. After the validation of the hypothesis, we suggested some recommendations to Sonelgaz Enterprise regarding the communication through social networks. Firstly, the company must use the interactivity of social network in order to have fruitful exchanges with the community. We also recommended having a strategy to treat negative comments. The company must also suggest delivering resources to the community thanks to a community manager, in order to have a good relation with the community. Furthermore, we advised using social networks to do business intelligence. Sonelgaz Enterprise can have some creative and interactive contents with some amazing applications on Facebook for example. Finally, we recommended to the company to be not intrusive with “fans” or “followers” and to be open to all the platforms: Twitter, Facebook, Linked-In for example.

Keywords: social network, buzz, communication, consumer, return on investment, internet users, web 2.0, Facebook, Twitter, interaction

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Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

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Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

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Authors: Kang-Hsien Li

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Along with global industries in using technology to enhance the application, make the study drawn more close to the people’s behavior and produce data analysis, extended out from the mental accounting of prospect theory, this paper provides the marketing and financial applications in the field of exploration and discussions with the future. For the foreseeable future, the payment behavior depends on the form of currency, which affects a variety of product types on the marketing of marketing strategy to provide diverse payment methods to enhance the overall sales performance. This not only affects people's consumption also affects people's investments. Credit card, PayPal, Apple pay, Bitcoin and any other with advances in technology and other emerging payment instruments, began to affect people for the value and the concept of money. Such as the planning of national social welfare policies, monetary and financial regulators and regulators. The expansion can be expected to discuss marketing and finance-related mental problems at the same time, recent studies reflect two different ideas, the first idea is that individuals affected by situational frames, not broad impact at the event level, affected by the people basically mental, second idea is that when an individual event affects a broader range, and majority of people will choose the same at the time that the rational choice. That are applied to practical application of marketing, at the same time provide an explanation in the financial market under the anomalies, due to the financial markets has varied investment products and different market participants, that also highlights these two points. It would provide in-depth description of humanity's mental. Certainly, about discuss mental accounting aspects, while artificial intelligence application development, although people would be able to reduce prejudice decisions, that will also lead to more discussion on the economic and marketing strategy.

Keywords: mental accounting, behavior economics, consumer behaviors, decision-making

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Authors: Ersilio Tushaj

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The efficient solution that satisfies the optimal condition is an important issue in the structural engineering design problem. The new codes of structural design consist in design methodology that looks after the exploitation of the total resources of the construction material. In recent years some non-deterministic or meta-heuristic structural optimization algorithms have been developed widely in the research community. These methods search the optimum condition starting from the simulation of a natural phenomenon, such as survival of the fittest, the immune system, swarm intelligence or the cooling process of molten metal through annealing. Among these techniques the most known are: the genetic algorithms, simulated annealing, evolution strategies, particle swarm optimization, tabu search, ant colony optimization, harmony search and big bang crunch optimization. In this study, five of these algorithms are applied for the optimum weight design of a steel truss structure with variable geometry but fixed topology. The design process selects optimum distances and size sections from a set of commercial steel profiles. In the formulation of the design problem are considered deflection limitations, buckling and allowable stress constraints. The approach is repeated starting from different initial populations. The design problem topology is taken from an existing steel structure. The optimization process helps the engineer to achieve good final solutions, avoiding the repetitive evaluation of alternative designs in a time consuming process. The algorithms used for the application, the results of the optimal solutions, the number of iterations and the minimal weight designs, will be reported in the paper. Based on these results, it would be estimated, the amount of the steel that could be saved by applying structural analysis combined with non-deterministic optimization methods.

Keywords: structural optimization, non-deterministic methods, truss structures, steel truss

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Huidan Yu, Anurag Deb, Rou Chen, I-Wen Wang

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The use of left ventricle assist devices (LVADs) in patients with heart failure has been a proven and effective therapy for patients with severe end-stage heart failure. Due to the limited availability of suitable donor hearts, LVADs will probably become the alternative solution for patient with heart failure in the near future. While the LVAD is being continuously improved toward enhanced performance, increased device durability, reduced size, a better understanding of implantation management becomes critical in order to achieve better long-term blood supplies and less post-surgical complications such as thrombi generation. Important issues related to the LVAD implantation include the location of outflow grafting (OG), the angle of the OG, the combination between LVAD and native heart pumping, uniform or pulsatile flow at OG, etc. We have hypothesized that an optimal implantation of LVAD is patient specific. To test this hypothesis, we employ a novel in-house computational modeling technique, named InVascular, to conduct a systematic evaluation of cardiac output at aortic arch together with other pertinent hemodynamic quantities for each patient under various implantation scenarios aiming to get an optimal implantation strategy. InVacular is a powerful computational modeling technique that integrates unified mesoscale modeling for both image segmentation and fluid dynamics with the cutting-edge GPU parallel computing. It first segments the aortic artery from patient’s CT image, then seamlessly feeds extracted morphology, together with the velocity wave from Echo Ultrasound image of the same patient, to the computation model to quantify 4-D (time+space) velocity and pressure fields. Using one NVIDIA Tesla K40 GPU card, InVascular completes a computation from CT image to 4-D hemodynamics within 30 minutes. Thus it has the great potential to conduct massive numerical simulation and analysis. The systematic evaluation for one patient includes three OG anastomosis (ascending aorta, descending thoracic aorta, and subclavian artery), three combinations of LVAD and native heart pumping (1:1, 1:2, and 1:3), three angles of OG anastomosis (inclined upward, perpendicular, and inclined downward), and two LVAD inflow conditions (uniform and pulsatile). The optimal LVAD implantation is suggested through a comprehensive analysis of the cardiac output and related hemodynamics from the simulations over the fifty-four scenarios. To confirm the hypothesis, 5 random patient cases will be evaluated.

Keywords: graphic processing unit (GPU) parallel computing, left ventricle assist device (LVAD), lumped-parameter model, patient-specific computational hemodynamics

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Authors: Cathy Basterfield

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Issue: To engage in one’s own health care, health professionals need to be aware of an individual’s specific skills and abilities for best communication. One of the most discussed is health literacy. One of the assumed skills and abilities for adults is an individuals’ health literacy. Background: A review of publicly available health content appears to assume all adult readers will have a broad and full capacity to read at a high level of literacy, often at a post-school education level. Health information writers and clinicians need to recognise one critical area for why there may be little or no change in a person’s behaviour, or no-shows to appointments. Perhaps unintentionally, they are miscommunicating with the majority of the adult population. Health information contains many literacy domains. It usually includes technical medical terms or jargon. Many fact sheets and other information require scientific literacy with or without specific numerical literacy. It may include graphs, percentages, timing, distance, or weights. Each additional word or concept in these domains decreases the readers' ability to meaningfully read, understand and know what to do with the information. An attempt to begin to read the heading where long or unfamiliar words are used will reduce the readers' motivation to attempt to read. Critically people who have low literacy are overwhelmed when pages are covered with lots of words. People attending a health environment may be unwell or anxious about a diagnosis. These make it harder to read, understand and know what to do with the information. But access to health information must consider an even wider range of adults, including those with poor school attainment, migrants, and refugees. It is also homeless people, people with mental health illnesses, or people who are ageing. People with low literacy also may include people with lifelong disabilities, people with acquired disabilities, people who read English as a second (or third) language, people who are Deaf, or people who are vision impaired. Outcome: This paper will discuss Easy English, which is developed for adults. It uses the audiences’ everyday words, short sentences, short words, and no jargon. It uses concrete language and concrete, specific images to support the text. It has been developed in Australia since the mid-2000s. This paper will showcase various projects in the health domain which use Easy English to improve the understanding and functional use of written information for the large numbers of adults in our communities who do not have the health literacy to manage a range of day to day reading tasks. See examples from consent forms, fact sheets and choice options, instructions, and other functional documents, where Easy English has been developed. This paper will ask individuals to reflect on their own work practice and consider what written information must be available in Easy English. It does not matter how cutting-edge a new treatment is; when adults can not read or understand what it is about and the positive and negative outcomes, they are less likely to be engaged in their own health journey.

Keywords: health literacy, inclusion, Easy English, communication

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Authors: M. Zielinski, M. Debowski, P. Rusanowska, A. Glowacka-Gil, M. Zielinska, A. Cydzik-Kwiatkowska, J. Kazimierowicz

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Technologies for the micro-biogas plant with heating and mixing systems are presented as a part of the Research Coordination for a Low-Cost Biomethane Production at Small and Medium Scale Applications (Record Biomap). The main objective of the Record Biomap project is to build a network of operators and scientific institutions interested in cooperation and the development of promising technologies in the sector of small and medium-sized biogas plants. The activities carried out in the project will bridge the gap between research and market and reduce the time of implementation of new, efficient technological and technical solutions. Reactor with simultaneously mixing and heating system is a concrete tank with a rectangular cross-section. In the reactor, heating is integrated with the mixing of substrate and anaerobic sludge. This reactor is solution dedicated for substrates with high solids content, which cannot be introduced to the reactor with pumps, even with positive displacement pumps. Substrates are poured to the reactor and then with a screw pump, they are mixed with anaerobic sludge. The pumped sludge, flowing through the screw pump, is simultaneously heated by a heat exchanger. The level of the fermentation sludge inside the reactor chamber is above the bottom edge of the cover. Cover of the reactor is equipped with the screw pump driver. Inside the reactor, an electric motor is installed that is driving a screw pump. The heated sludge circulates in the digester. The post-fermented sludge is collected using a drain well. The inlet to the drain well is below the level of the sludge in the digester. The biogas is discharged from the reactor by the biogas intake valve located on the cover. The technology is very useful for fermentation of lignocellulosic biomass and substrates with high content of dry mass (organic wastes). The other technology is a reactor for micro-biogas plant with a pressure mixing system. The reactor has a form of plastic or concrete tank with a circular cross-section. The effective mixing of sludge is ensured by profiled at 90° bottom of the tank. Substrates for fermentation are supplied by an inlet well. The inlet well is equipped with a cover that eliminates odour release. The introduction of a new portion of substrates is preceded by pumping of digestate to the disposal well. Optionally, digestate can gravitationally flow to digestate storage tank. The obtained biogas is discharged into the separator. The valve supplies biogas to the blower. The blower presses the biogas from the fermentation chamber in such a way as to facilitate the introduction of a new portion of substrates. Biogas is discharged from the reactor by valve that enables biogas removal but prevents suction from outside the reactor.

Keywords: biogas, digestion, heating system, mixing system

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Authors: Sharda Kumari, Saiman Shetty

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Autonomous trucking is just one of the numerous significant shifts impacting fleet management services. The Society of Automotive Engineers (SAE) has defined six levels of vehicle automation that have been adopted internationally, including by the United States Department of Transportation. On public highways in the United States, organizations are testing driverless vehicles with at least Level 4 automation which indicates that a human is present in the vehicle and can disable automation, which is usually done while the trucks are not engaged in highway driving. However, completely driverless vehicles are presently being tested in the state of California. While autonomous trucking can increase safety, decrease trucking costs, provide solutions to trucker shortages, and improve efficiencies, logistics, too, requires advancements to keep up with trucking innovations. Given that artificial intelligence, machine learning, and automated procedures enable people to do their duties in other sectors with fewer resources, CRM (Customer Relationship Management) can be applied to the autonomous trucking business to provide the same level of efficiency. In a society witnessing significant digital disruptions, fleet management is likewise being transformed by technology. Utilizing strategic alliances to enhance core services is an effective technique for capitalizing on innovations and delivering enhanced services. Utilizing analytics on CRM systems improves cost control of fuel strategy, fleet maintenance, driver behavior, route planning, road safety compliance, and capacity utilization. Integration of autonomous trucks with automated fleet management, yard/terminal management, and customer service is possible, thus having significant power to redraw the lines between the public and private spheres in autonomous trucking logistics.

Keywords: autonomous vehicles, customer relationship management, customer experience, autonomous trucking, digital transformation

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Authors: Stephanie Liu Lu

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With the proliferation of artificial intelligence tools in the field of education, it is crucial to explore their impact on language learning outcomes. This paper examines the use of AI tools, such as ChatGPT, in practical writing within business Chinese teaching to investigate how AI can enhance practical writing skills and teaching effectiveness. The study involved third and fourth-year university students majoring in accounting and finance from a university in Hong Kong within the context of a business correspondence writing class. Students were randomly assigned to a control group, who completed business letter writing independently, and an experimental group, who completed the writing with the assistance of AI. In the latter, the AI-assisted business letters were initially drafted by the students issuing commands and interacting with the AI tool, followed by the students' revisions of the draft. The paper assesses the performance of both groups in terms of grammatical expression, communicative effect, and situational awareness. Additionally, the study collected dialogue texts from interactions between students and the AI tool to explore factors that affect text generation and the potential impact of AI on enhancing students' communicative and identity awareness. By collecting and comparing textual performances, it was found that students assisted by AI showed better situational awareness, as well as more skilled organization and grammar. However, the research also revealed that AI-generated articles frequently lacked a proper balance of identity and writing purpose due to limitations in students' communicative awareness and expression during the instruction and interaction process. Furthermore, the revision of drafts also tested the students' linguistic foundation, logical thinking abilities, and practical workplace experience. Therefore, integrating AI tools and related teaching into the curriculum is key to the future of business Chinese teaching.

Keywords: AI-assistance, business Chinese, textual analysis, language education

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Odunayo S. Faluse, Roger Telfer

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Customer relationship management is a business strategy that is centred on the idea that ‘Customer is the driving force of any business’ i.e. Customer is placed in a central position in any business. However, this belief coupled with the advancement in information technology in the past twenty years has experienced a change. In any form of business today it can be concluded that customers are the modern dictators to whom the industry always adjusts its business operations due to the increase in availability of information, intense market competition and ever growing negotiating ideas of customers in the process of buying and selling. The most vital role of any organization is to satisfy or meet customer’s needs and demands, which eventually determines customer’s long-term value to the industry. Therefore, this paper analyses and describes the application of customer relationship management operational strategies in some of the major industries in business. Both developed and up-coming companies nowadays value the quality of customer services and client’s loyalty, they also recognize the customers that are not very sensitive when it comes to changes in price and thereby realize that attracting new customers is more tasking and expensive than retaining the existing customers. However, research shows that several factors have recently amounts to the sudden rise in the execution of CRM strategies in the marketplace, such as a diverted attention of some organization towards integrating ideas in retaining existing customers rather than attracting new one, gathering data about customers through the use of internal database system and acquiring of external syndicate data, also exponential increase in technological intelligence. Apparently, with this development in business operations, CRM research in Academia remain nascent; hence this paper gives detailed critical analysis of the recent advancement in the use of CRM and key research opportunities for future development in using the implementation of CRM as a determinant factor for successful business optimization.

Keywords: agriculture, banking, business strategies, CRM, education, healthcare

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Authors: Akhilesh Kumar, Sathyanarayan G., Nirmala S.

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An unusual approach is developed to determine the orbit of satellites/space objects. The determination of orbits is considered a boundary value problem and has been solved using the finite difference method (FDM). Only positions of the satellites/space objects are known at two end times taken as boundary conditions. The technique of finite difference has been used to calculate the orbit between end times. In this approach, the governing equation is defined as the satellite's equation of motion with a perturbed acceleration. Using the finite difference method, the governing equations and boundary conditions are discretized. The resulting system of algebraic equations is solved using Tri Diagonal Matrix Algorithm (TDMA) until convergence is achieved. This methodology test and evaluation has been done using all GPS satellite orbits from National Geospatial-Intelligence Agency (NGA) precise product for Doy 125, 2023. Towards this, two hours of twelve sets have been taken into consideration. Only positions at the end times of each twelve sets are considered boundary conditions. This algorithm is applied to all GPS satellites. Results achieved using FDM compared with the results of NGA precise orbits. The maximum RSS error for the position is 0.48 [m] and the velocity is 0.43 [mm/sec]. Also, the present algorithm is applied on the IRNSS satellites for Doy 220, 2023. The maximum RSS error for the position is 0.49 [m], and for velocity is 0.28 [mm/sec]. Next, a simulation has been done for a Highly Elliptical orbit for DOY 63, 2023, for the duration of 6 hours. The RSS of difference in position is 0.92 [m] and velocity is 1.58 [mm/sec] for the orbital speed of more than 5km/sec. Whereas the RSS of difference in position is 0.13 [m] and velocity is 0.12 [mm/sec] for the orbital speed less than 5km/sec. Results show that the newly created method is reliable and accurate. Further applications of the developed methodology include missile and spacecraft targeting, orbit design (mission planning), space rendezvous and interception, space debris correlation, and navigation solutions.

Keywords: finite difference method, grid generation, NavIC system, orbit perturbation

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Authors: Ramiz Kuliev, Fuad Kuliev-Smirnov

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The 'Shadow Sparring' training exercise is widely used in the training of boxers and martial artists. The main disadvantage of the usual shadow sparring is that the trainer cannot fully control such training and evaluate its results. During the competition, the athlete, preparing for the upcoming fight, imagines the Shadow (upcoming opponent) in accordance with his own imagination. A ‘Smart-Shadow Sparring’ (SSS) is an innovative version of the ‘Shadow Sparring’. During SSS, the fighter will see the Shadow (virtual opponent that moves, defends, and punches) and understand when he misses the punches from the Shadow. The task of a real athlete is to spar with a virtual one, move around, punch in the direction of unprotected areas of the Shadow and dodge his punches. Moves and punches of Shadow are set up before each training. The system will give the coach full information about virtual sparring: (i) how many and what type of punches has the fighter landed, (ii) accuracy of these punches, (iii) how many and what type of virtual punches (punches of Smart-Shadow) has the fighter missed, etc. SSS will be recorded as animated fighting of two fighters and will help the coach to analyze past training. SSS can be configured to fit the physical and technical characteristics of the next real opponent (size, techniques, speed, missed and landed punches, etc.). This will allow to simulate and rehearse the upcoming fight and improve readiness for the next opponent. For amateur fighters, SSS will be reconfigured several times during a tournament, when the real opponent becomes known. SSS can be used in three versions: (1) Digital Shadow: the athlete will see a Shadow on a monitor (2) VR-Shadow: the athlete will see a Shadow in a VR-glasses (3) Smart Shadow: a Shadow will be controlled by artificial intelligence. These technologies are based on the ‘semi-real simulation’ method. The technology allows coaches to train athletes remotely. Simulation of different opponents will help the athletes better prepare for competition. Repeat rehearsals of the upcoming fight will help improve results. SSS can improve results in Boxing, Taekwondo, Karate, and Fencing. 41 sets of medals will be awarded in these sports at the 2020 Olympic Games.

Keywords: boxing, combat sports, fight simulation, shadow sparring

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Authors: Bipasa Dey, Varsha Panwar, Tanmay Dutta

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Laccase, a multicopper oxidase (EC 1.10.3.2) have been at the forefront as a superior industrial biocatalyst. They are versatile in terms of bestowing sustainable and ecological catalytic reactions such as polymerisation, xenobiotic degradation and bioremediation of phenolic and non-phenolic compounds. Regardless of the wide biotechnological applications, the critical limiting factors viz. reusability, retrieval, and storage stability still prevail. This can cause an impediment in their applicability. Crosslinked enzyme aggregates (CLEAs) have emerged as a promising technique that rehabilitates these essential facets, albeit at the expense of their enzymatic activity. The carrier free crosslinking method prevails over the carrier-bound immobilisation in conferring high productivity, low production cost owing to the absence of additional carrier and circumvent any non-catalytic ballast which could dilute the volumetric activity. To the best of our knowledge, the ε-amino group of lysyl residue is speculated as the best choice for forming Schiff’s base with glutaraldehyde. Despite being most preferrable, excess glutaraldehyde can bring about disproportionate and undesirable crosslinking within the catalytic site and hence could deliver undesirable catalytic losses. Moreover, the surface distribution of lysine residues in Trametes versicolor laccase is significantly less. Thus, to mitigate the adverse effect of glutaraldehyde in conjunction with scaling down the degradation or catalytic loss of the enzyme, crosslinking with inert substances like gelatine, collagen, Bovine serum albumin (BSA) or excess lysine is practiced. Analogous to these molecules, sugars have been well known as a protein stabiliser. It helps to retain the structural integrity, specifically secondary structure of the protein during aggregation by changing the solvent properties. They are comprehended to avert protein denaturation or enzyme deactivation during precipitation. We prepared crosslinked enzyme aggregates (CLEAs) of laccase from T. versicolor with the aid of sugars. The sugar CLEAs were compared with the classic BSA and glutaraldehyde laccase CLEAs concerning physico-chemical properties. The activity recovery for the fructose CLEAs were found to be ~20% higher than the non-sugar CLEA. Moreover, the 𝐾𝑐𝑎𝑡𝐾𝑚⁄ values of the CLEAs were two and three-fold higher than BSA-CLEA and GACLEA, respectively. The half-life (t1/2) deciphered by sugar-CLEA was higher than the t1/2 of GA-CLEAs and free enzyme, portraying more thermal stability. Besides, it demonstrated extraordinarily high pH stability, which was analogous to BSA-CLEA. The promising attributes of increased storage stability and recyclability (>80%) gives more edge to the sugar-CLEAs over conventional CLEAs of their corresponding free enzyme. Thus, sugar-CLEA prevails in furnishing the rudimentary properties required for a biocatalyst and holds many prospects.

Keywords: cross-linked enzyme aggregates, laccase immobilization, enzyme reusability, enzyme stability

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Authors: Vidhi Chandra, Arshpreet Singh Grewal

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A five-year-old male child born from non-consanguineous marriage, who presented with complaints of growth retardation and no appreciable increase in the penile size since birth and he was posted for de-gloving of penis with dissection of corpora under anaesthesia. After thorough preoperative evaluation it was revealed that patient had peculiar facial dysmorphism that of Robinow Syndrome, high arched palate, Mallampati grade III, mesomelic limbs, scoliotic spine and short stature. All routine investigation were within normal limit, electrocardiography (ECG) and 2D-Echocardiography (ECHO) were normal. In antero-posterior roentgenogram chest showed butterfly and hemivertebrae at multiple levels. The patient was considered to be ASA II. On the day of surgery after ensuring fasting of 6 hours, patient was taken in operation theatre, all standard ASA monitoring was done with ECG, non-invasive blood pressure, peripheral oxygen saturation (SpO2) and body temperature. The patient was pre-oxygenated with 100% oxygen with anatomical face mask. General anaesthesia was induced with Sevoflurane 1-8%, and airway was secured with an appropriate size supraglottic airway and anaesthesia was maintained with nitrous oxide and oxygen in 1:1 ratio along with sevoflurane 2%. An ultrasound guided caudal block was given owing to the skeletal deformities making it difficult even under USG guidance. Post operatively patient was given supportive care with proper hydration, antibiotics, anti-inflammatory and analgesics. He was discharged the next day and followed up weekly for a month. DISCUSSION Robinow syndrome is genetically inherited as autosomal dominant, autosomal recessive or heterogenous disorder involving tyrosine kinase ROR2 gene located on chromosome 9. It has low incidence with no preponderance for any gender. Though intelligence is normal but developmental delay and mental retardation occurs in 20%cases

Keywords: Robinow Syndrome, dwarfism, paediatric, anaesthesia

Procedia PDF Downloads 88

Authors: Farahnaz Karami

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Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

Procedia PDF Downloads 42

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

Procedia PDF Downloads 56