Search results for: algorithm integration

Authors: Shreyas Kumar, Mili Shangari

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Integrating artificial intelligence (AI) in the cyber insurance sector represents a significant advancement, offering the potential to revolutionize risk assessment, fraud detection, and claims processing. However, this integration introduces a range of regulatory and economic challenges that must be addressed to ensure responsible and effective deployment of AI technologies. This paper examines the multifaceted regulatory landscape governing AI in cyber insurance and explores the economic implications of compliance, innovation, and market dynamics. AI's capabilities in processing vast amounts of data and identifying patterns make it an invaluable tool for insurers in managing cyber risks. Yet, the application of AI in this domain is subject to stringent regulatory scrutiny aimed at safeguarding data privacy, ensuring algorithmic transparency, and preventing biases. Regulatory bodies, such as the European Union with its General Data Protection Regulation (GDPR), mandate strict compliance requirements that can significantly impact the deployment of AI systems. These regulations necessitate robust data protection measures, ethical AI practices, and clear accountability frameworks, all of which entail substantial compliance costs for insurers. The economic implications of these regulatory requirements are profound. Insurers must invest heavily in upgrading their IT infrastructure, implementing robust data governance frameworks, and training personnel to handle AI systems ethically and effectively. These investments, while essential for regulatory compliance, can strain financial resources, particularly for smaller insurers, potentially leading to market consolidation. Furthermore, the cost of regulatory compliance can translate into higher premiums for policyholders, affecting the overall affordability and accessibility of cyber insurance. Despite these challenges, the potential economic benefits of AI integration in cyber insurance are significant. AI-enhanced risk assessment models can provide more accurate pricing, reduce the incidence of fraudulent claims, and expedite claims processing, leading to overall cost savings and increased efficiency. These efficiencies can improve the competitiveness of insurers and drive innovation in product offerings. However, balancing these benefits with regulatory compliance is crucial to avoid legal penalties and reputational damage. The paper also explores the potential risks associated with AI integration, such as algorithmic biases that could lead to unfair discrimination in policy underwriting and claims adjudication. Regulatory frameworks need to evolve to address these issues, promoting fairness and transparency in AI applications. Policymakers play a critical role in creating a balanced regulatory environment that fosters innovation while protecting consumer rights and ensuring market stability. In conclusion, the integration of AI in cyber insurance presents both regulatory and economic challenges that require a coordinated approach involving regulators, insurers, and other stakeholders. By navigating these challenges effectively, the industry can harness the transformative potential of AI, driving advancements in risk management and enhancing the resilience of the cyber insurance market. This paper provides insights and recommendations for policymakers and industry leaders to achieve a balanced and sustainable integration of AI technologies in cyber insurance.

Keywords: artificial intelligence (AI), cyber insurance, regulatory compliance, economic impact, risk assessment, fraud detection, cyber liability insurance, risk management, ransomware

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Authors: Elizelle Juanee Cilliers

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Recent planning thinking has laid the foundations for a general sense of best practice that aims to enhance the quality of life, suggesting an open and participatory process. It is accepted that integration of top-down and bottom-up approaches may lead to efficient action in environments and sustainable planning and development, although it is also accepted that such an integrated approach has various challenges of implementation. A flexible framework in which the strengths of both the top-down and bottom-up approaches were explored in this research, based on the EU Interreg VALUE Added project and five case studies where student education and student involvement played a crucial role within the participation process of the redesign of the urban environment. It was found that international student workshops were an effective tool to integrate bottom-up and top-down structures, as it acted as catalyst for communication, interaction, creative design, quick transformation from planning to implementation, building social cohesion, finding mutual ground between stakeholders and thus enhancing overall quality of life and quality of environments. It offered a good alternative to traditional participation modes and created a platform for an integrative planning approach. The role and importance of education and integration within the urban environment were emphasized.

Keywords: top-down, bottom-up, flexible, student involvement

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Wenjun Hou, Marek Perkowski

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The Traveling Salesman problem is famous in computing and graph theory. In short, it asks for the Hamiltonian cycle of the least total weight in a given graph with N nodes. All variations on this problem, such as those with K-bounded-degree nodes, are classified as NP-complete in classical computing. Although several papers propose theoretical high-level designs of quantum algorithms for the Traveling Salesman Problem, no quantum circuit implementation of these algorithms has been created up to our best knowledge. In contrast to previous papers, the goal of this paper is not to optimize some abstract complexity measures based on the number of oracle iterations, but to be able to evaluate the real circuit and time costs of the quantum computer. Using the emerging quantum programming language Q# developed by Microsoft, which runs quantum circuits in a quantum computer simulation, an implementation of the bounded-degree problem and its respective quantum circuit were created. To apply Grover’s algorithm to this problem, a quantum oracle was designed, evaluating the cost of a particular set of edges in the graph as well as its validity as a Hamiltonian cycle. Repeating the Grover algorithm with an oracle that finds successively lower cost each time allows to transform the decision problem to an optimization problem, finding the minimum cost of Hamiltonian cycles. N log₂ K qubits are put into an equiprobablistic superposition by applying the Hadamard gate on each qubit. Within these N log₂ K qubits, the method uses an encoding in which every node is mapped to a set of its encoded edges. The oracle consists of several blocks of circuits: a custom-written edge weight adder, node index calculator, uniqueness checker, and comparator, which were all created using only quantum Toffoli gates, including its special forms, which are Feynman and Pauli X. The oracle begins by using the edge encodings specified by the qubits to calculate each node that this path visits and adding up the edge weights along the way. Next, the oracle uses the calculated nodes from the previous step and check that all the nodes are unique. Finally, the oracle checks that the calculated cost is less than the previously-calculated cost. By performing the oracle an optimal number of times, a correct answer can be generated with very high probability. The oracle of the Grover Algorithm is modified using the recalculated minimum cost value, and this procedure is repeated until the cost cannot be further reduced. This algorithm and circuit design have been verified, using several datasets, to generate correct outputs.

Keywords: quantum computing, quantum circuit optimization, quantum algorithms, hybrid quantum algorithms, quantum programming, Grover’s algorithm, traveling salesman problem, bounded-degree TSP, minimal cost, Q# language

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Authors: Fares Hedayatit, Arnauld Joly, Panagiotis Papadimitriou

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Many machine learning tasks such as text annotation usually require training over very big datasets, e.g., millions of web documents, that can be represented in a sparse input space. State-of the-art tree-based ensemble algorithms cannot scale to such datasets, since they include operations whose running time is a function of the input space size rather than a function of the non-zero input elements. In this paper, we propose an efficient splitting algorithm to leverage input sparsity within decision tree methods. Our algorithm improves training time over sparse datasets by more than two orders of magnitude and it has been incorporated in the current version of scikit-learn.org, the most popular open source Python machine learning library.

Keywords: big data, sparsely representable data, tree-based models, scalable learning

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: L. J. de Jager

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In the South African English school curriculum, one of the aims is to achieve communicative competence, the knowledge of using language competently and appropriately in a speech community. Communicatively competent speakers should not only produce grammatically correct sentences but also produce contextually appropriate sentences for various purposes and in different situations. As most speakers of English are non-native speakers, achieving communicative competence remains a complex challenge. Moreover, the changing needs of society necessitate not merely language proficiency, but also technological proficiency. One of the burning issues in the South African educational landscape is the replacement of the standardised literacy model by the pedagogy of multiliteracies that incorporate, by default, the exploration of technological text forms that are part of learners’ everyday lives. It foresees learners as decoders, encoders, and manufacturers of their own futures by exploiting technological possibilities to constantly create and recreate meaning. As such, 21st century learners will feel comfortable working with multimodal texts that are intrinsically part of their lives and by doing so, become authors of their own learning experiences while teachers may become agents supporting learners to discover their capacity to acquire new digital skills for the century of multiliteracies. The aim is transformed practice where learners use their skills, ideas, and knowledge in new contexts. This paper reports on a research project on the integration of technology for language learning, based on the technological pedagogical content knowledge framework, conceptually founded in the theory of multiliteracies, and which aims to achieve communicative competence. The qualitative study uses the community of inquiry framework to answer the research question: How does the integration of technology transform language teaching of preservice teachers? Pre-service teachers in the Postgraduate Certificate of Education Programme with English as methodology were purposively selected to source and evaluate apps for teaching and learning English. The participants collaborated online in a dedicated Blackboard module, using discussion threads to sift through applicable apps and develop interactive lessons using the Apps. The selected apps were entered on to a predesigned Qualtrics form. Data from the online discussions, focus group interviews, and reflective journals were thematically and inductively analysed to determine the participants’ perceptions and experiences when integrating technology in lesson design and the extent to which communicative competence was achieved when using these apps. Findings indicate transformed practice among participants and research team members alike with a better than average technology acceptance and integration. Participants found value in online collaboration to develop and improve their own teaching practice by experiencing directly the benefits of integrating e-learning into the teaching of languages. It could not, however, be clearly determined whether communicative competence was improved. The findings of the project may potentially inform future e-learning activities, thus supporting student learning and development in follow-up cycles of the project.

Keywords: apps, communicative competence, English teaching, technology integration, technological pedagogical content knowledge

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Authors: Ashhar Ahmed Shaikh, Ayush Tandon

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The swift reduction of fossil fuels from nature has crucial need for alternative energy sources to cater vital demand. It is essential to boost alternative energy sources to cover the continuously increasing demand for energy while minimizing the negative environmental impacts. Solar energy is one of the reliable sources that can generate energy. Solar energy is freely available in nature and is completely eco-friendly, and they are considered as the most promising power generating sources due to their easy availability and other advantages for the local power generation. This paper is to review the implementation of power electronic devices through Solar Energy Grid Integration System (SEGIS) to increase the efficiency. This paper will also concentrate on the future grid infrastructure and various other applications in order to make the grid smart. Development and implementation of a power electronic devices such as PV inverters and power controllers play an important role in power supply in the modern energy economy. Solar Energy Grid Integration System (SEGIS) opens pathways for promising solutions for new electronic and electrical components such as advanced innovative inverter/controller topologies and their functions, economical energy management systems, innovative energy storage systems with equipped advanced control algorithms, advanced maximum-power-point tracking (MPPT) suited for all PV technologies, protocols and the associated communications. In addition to advanced grid interconnection capabilities and features, the new hardware design results in small size, less maintenance, and higher reliability. The SEGIS systems will make the 'advanced integrated system' and 'smart grid' evolutionary processes to run in a better way. Since the last few years, there was a major development in the field of power electronics which led to more efficient systems and reduction of the cost per Kilo-watt. The inverters became more efficient and had reached efficiencies in excess of 98%, and commercial solar modules have reached almost 21% efficiency.

Keywords: solar energy grid integration systems, smart grid, advanced integrated system, power electronics

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Authors: Wenjie Qin, Kewei Lv

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In 1996, Boneh and Venkatesan proposed the Hidden Number Problem (HNP) and proved the most significant bits (MSB) of computational Diffie-Hellman key exchange scheme and related schemes are unpredictable bits. They also gave a method which is a lattice rounding technique to solve HNP in non-uniform model. In this paper, we put forward a new concept that is Schnorr-MSB-HNP. We also reduce the problem of solving Schnorr signature private key with a few consecutive most significant bits of random nonce (used at each signature generation) to Schnorr-MSB-HNP, then we use the rounding technique to solve the Schnorr-MSB-HNP. We have come to the conclusion that if there is a ‘miraculous box’ which inputs the random nonce and outputs 2loglogq (q is a prime number) most significant bits of nonce, the signature private key will be obtained by choosing 2logq signature messages randomly. Thus we get an attack on the Schnorr signature private key.

Keywords: rounding technique, most significant bits, Schnorr signature algorithm, nonce, Schnorr-MSB-HNP

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Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

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Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

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Authors: Modesta E. Ezema, Chikwendu V. Alabekee, Victoria N. Ishiwu, Ifeyinwa NwosuArize, Chinedu I. Nwoye

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The advent of wireless networking in computing technology cannot be overemphasized, it opened up easy accessibility to information resources, networking made easier and brought internet accessibility to our doorsteps, but despite all these, some mishap came in with it that is causing mayhem in today ‘s overall information security. The cyber criminals will always compromise the integrity of a message that is not encrypted or that is encrypted with a weak algorithm.In other to correct the mayhem, this study focuses on cryptosystem and cryptography. This ensures end to end crypt messaging. The study of various cryptographic algorithms, as well as the techniques and applications of the cryptography for efficiency, were all considered in the work., present and future applications of cryptography were dealt with as well as Quantum Cryptography was exposed as the current and the future area in the development of cryptography. An empirical study was conducted to collect data from network users.

Keywords: algorithm, cryptography, cryptosystem, network

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Authors: Somoshree Datta, Chithra A. V., Sandeep Nithyanandan, Smitha K. K.

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Space exploration has always been at the forefront of technological innovation, pushing the boundaries of human knowledge and capabilities. In recent years, the integration of Artificial Intelligence (AI) has revolutionized the field, offering unprecedented opportunities to enhance the efficiency, autonomy and intelligence of space missions. This survey paper aims to provide a comprehensive overview of the multifaceted applications of AI in space exploration, exploring the evolution of this synergy and its impact on mission success, scientific discovery, and the future of space endeavors. Indian Space Research Organization (ISRO) has achieved great feats in the recent moon mission (Chandrayaan-3) and sun mission (Aditya L1) by using artificial intelligence to enhance moon navigation as well as help young scientists to study the Sun even before the launch by creating AI-generated image visualizations. Throughout this survey, we will review key advancements, challenges and prospects in the intersection of AI and space exploration. As humanity continues its quest to explore the cosmos, the integration of AI promises to unlock new frontiers, reshape mission architectures, and redefine our understanding of the universe. This survey aims to serve as a comprehensive resource for researchers, engineers and enthusiasts interested in the dynamic and evolving landscape of AI applications in space exploration.

Keywords: artificial intelligence, space exploration, space missions, deep learning

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Authors: S. Yaman, S. Rostami

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In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.

Keywords: function tuner method (FTM), fuzzy modeling, fuzzy PID controller, genetic algorithm (GA)

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Authors: Jisun Lee, Jay Hyoun Kwon

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As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

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Authors: L. L. Ivy-Yap, H. A. Bekhet

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As the Malaysian residential electricity consumption continued to increase rapidly, effective energy policies, which address factors affecting residential electricity consumption, is urgently needed. This study attempts to investigate the relationship between residential electricity consumption (EC), real disposable income (Y), price of electricity (Pe) and population (Po) in Malaysia for 1978-2011 periods. Unlike previous studies on Malaysia, the current study focuses on the residential sector, a sector that is important for the contemplation of energy policy. The Phillips-Perron (P-P) unit root test is employed to infer the stationary of each variable while the bound test is executed to determine the existence of co-integration relationship among the variables, modeled in an Autoregressive Distributed Lag (ARDL) framework. The CUSUM and CUSUM of squares tests are applied to ensure the stability of the model. The results suggest the existence of long-run equilibrium relationship and bidirectional Granger causality between EC and the macroeconomic variables. The empirical findings will help policy makers of Malaysia in developing new monitoring standards of energy consumption. As it is the major contributing factor in economic growth and CO2 emission, there is a need for more proper planning in Malaysia to attain future targets in order to cut emissions.

Keywords: co-integration, elasticity, granger causality, Malaysia, residential electricity consumption

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Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

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In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

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Authors: Prosper Tafadzwa Denhere, Ephias Ruhode, Munyaradzi Zhou

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Health Information Systems (HISs) interoperability is emerging to be the future of modern healthcare systems Enterprise Architecture (EA), where healthcare entities are seamlessly interconnected to share healthcare data. The reality that the healthcare industry has been characterised by an influx of fragmented stand-alone e-Health systems, which present challenges of healthcare information sharing across platforms, desires much attention for systems integration efforts. The lack of an EA conceptual framework resultantly crates the need for investigating an ideal solution to the objective of Health Information Systems interoperability development assessment. The study takes a qualitative exploratory approach through a design science research context. The research aims to study the various themes withdrawn from the literature that can help in the assessment of interoperable HISs development through a literature study. Themes derived from the study include HIS needs, HIS readiness, HIS constraints, and HIS technology integration elements and standards tied to the EA development architectural layers of The Open Group Architecture Framework (TOGAF) as an EA development methodology. Eventually, the themes were conceptualised into a framework reviewed by two experts. The essence of the study was to provide a framework within which interoperable EA of HISs should be developed.

Keywords: enterprise architecture, eHealth, health information systems, interoperability

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Authors: Sello Mokwena, Monyepao Thabang

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In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: camera network, convolutional neural network topology, person tracking, person re-identification, siamese

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Authors: Nadaniela Egidi, Pierluigi Maponi

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The approximate solution of a time-harmonic electromagnetic scattering problem for inhomogeneous media is required in several application contexts, and its two-dimensional formulation is a Fredholm integral equation of the second kind. This integral equation provides a formulation for the direct scattering problem, but it has to be solved several times also in the numerical solution of the corresponding inverse scattering problem. The discretization of this Fredholm equation produces large and dense linear systems that are usually solved by iterative methods. In order to improve the efficiency of these iterative methods, we use the Symmetric SOR preconditioning, and we propose an algorithm for the evaluation of the associated relaxation parameter. We show the efficiency of the proposed algorithm by several numerical experiments, where we use two Krylov subspace methods, i.e., Bi-CGSTAB and GMRES.

Keywords: Fredholm integral equation, iterative method, preconditioning, scattering problem

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Authors: Zhihan Li

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Nowadays, most universities use the course enrollment system considering students’ registration orders. However, the students’ preference level to certain courses is also one important factor to consider. In this research, the possibility of applying a preference-first system has been discussed and analyzed compared to the order-first system. A bipartite graph is applied to resemble the relationship between students and courses they tend to register. With the graph set up, we apply Ford-Fulkerson (F.F.) Algorithm to maximize parings between two sets of nodes, in our case, students and courses. Two models are proposed in this paper: the one considered students’ order first, and the one considered students’ preference first. By comparing and contrasting the two models, we highlight the usability of models which potentially leads to better designs for school course registration systems.

Keywords: bipartite graph, Ford-Fulkerson (F.F.) algorithm, graph theory, maximum matching

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Authors: Messaoud Eljamai, Sami Hidouri

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Massive multiple-input multiple-output (MIMO) is an emerging technology for new cellular networks such as 5G systems. Its principle is to use many antennas per cell in order to maximize the network's spectral efficiency. Inter-cellular interference remains a fundamental problem. The use of massive MIMO will not derogate from the rule. It improves performances only when the number of antennas is significantly greater than the number of users. This, considerably, limits the networks spectral efficiency. In this paper, a coordinated detector for an uplink massive MIMO system is proposed in order to mitigate the inter-cellular interference. The proposed scheme combines the coordinated multipoint technique with an interference-cancelling algorithm. It requires the serving cell to send their received symbols, after processing, decision and error detection, to the interfered cells via a backhaul link. Each interfered cell is capable of eliminating intercellular interferences by generating and subtracting the user’s contribution from the received signal. The resulting signal is more reliable than the original received signal. This allows the uplink massive MIMO system to improve their performances dramatically. Simulation results show that the proposed detector improves system spectral efficiency compared to classical linear detectors.

Keywords: massive MIMO, COMP, interference canceling algorithm, spectral efficiency

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Authors: Pei-Chann Chang, Jhen-Fu Liao, Chin-Hung Teng, Meng-Hui Chen

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This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset.

Keywords: artificial immune system, collaborative filtering, recommendation system, similarity

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Authors: Aadiv Shah, Hari Nair, Vedant Mittal, Alice Cheeran

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Present-day agricultural practices such as blanket spraying not only lead to excessive usage of pesticides but also harm the overall crop yield. This paper introduces an algorithm to optimize the traversal of an unmanned aerial vehicle (UAV) in constrained environments. The proposed system focuses on the agricultural application of targeted spraying for locust elimination. Given a satellite image of a farm, target zones that are prone to locust swarm formation are detected through the calculation of the normalized difference vegetation index (NDVI). This is followed by determining the optimal path for traversal of a UAV through these target zones using the proposed algorithm in order to perform pesticide spraying in the most efficient manner possible. Unlike the classic travelling salesman problem involving point-to-point optimization, the proposed algorithm determines an optimal path for multiple regions, independent of its geometry. Finally, the paper explores the idea of implementing reinforcement learning to model complex environmental behaviour and make the path planning mechanism for UAVs agnostic to external environment changes. This system not only presents a solution to the enormous losses incurred due to locust attacks but also an efficient way to automate agricultural practices across the globe in order to improve farmer ergonomics.

Keywords: locust, NDVI, optimization, path planning, reinforcement learning, UAV

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Authors: Bruce Hunter

Abstract:

Background: The transition for student veterans from military service to higher education can be a challenging endeavor, especially for those pursuing an education in nursing. While the experiences and perspectives of each student veteran is unique, their successful integration into an academic environment can be influenced by a complex array of barriers and facilitators. This mixed-methods study aims to explore the themes and concepts that can be found in the transition experiences of student veterans in nursing education, with a focus on identifying the barriers they face and the facilitators that support their success. Methods: This study utilizes an explanatory mixed-methods approach. The research participants include student veterans enrolled in nursing programs across three academic institutions in the Southeastern United States. Quantitative Phase: A Likert scale instrument is distributed to a sample of student veterans in nursing programs. The survey assesses demographic information, academic experiences, social experiences, and perceptions of institutional support. Quantitative data is analyzed using descriptive statistics to assess demographics and to identify barriers and facilitators to the transition. Qualitative Phase: Two open-ended questions were posed to student veterans to explore their lived experiences, barriers, and facilitators during the transition to nursing education and to further explain the quantitative findings. Thematic analysis with line-by-line coding is employed to identify recurring themes and narratives that may shed light on the barriers and facilitators encountered. Results: This study found that the successful academic integration of student veterans lies in recognizing the diversity of values and attitudes among student veterans, understanding the potential challenges they face, and engaging in initiative-taking steps to create an inclusive and supportive academic environment that accommodates the unique experiences of this demographic. Addressing these academic and social integration concerns can contribute to a more understanding environment for student veterans in the BSN program. Conclusion: Providing support during this transitional period is crucial not only for retaining veterans, but also for bolstering their success in achieving the status of registered nurses. Acquiring an understanding of military culture emerges as an essential initial step for nursing faculty in student veteran retention and for successful completion of their programs. Participants found that their transition experience lacked meaningful social interactions, which could foster a positive learning environment, enhance their emotional well-being, and could contribute significantly to their overall success and satisfaction in their nursing education journey. Recognizing and promoting academic and social integration is important in helping veterans experience a smooth transition into and through the unfamiliar academic environment of nursing education.

Keywords: nursing, education, student veterans, barriers, facilitators

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Authors: Hyeon-Seung Kim, Young-Hwan Kim, Sang-Mi Park, Min-Seo Kim, Jong-Myeung Shin, Leen-Seok Kang

Abstract:

The project budget at the planned stage might be changed by the insufficient government budget or the design change. There are many cases more especially in the case of a project performed for a long period of time. If the actual construction budget is insufficient comparing with the planned budget, the construction schedule should also be changed to match the changed budget. In that case, most project managers change the planned construction schedule by a heuristic approach without a reasonable consideration on the work priority. This study suggests an optimized methodology to modify the construction schedule according to the changed budget. The genetic algorithm was used to optimize the modified construction schedule within the changed budget. And a simulation system of construction cost histogram in accordance with the construction schedule was developed in the BIM (Building Information Modeling) environment.

Keywords: 5D, BIM, GA, cost optimization

Procedia PDF Downloads 579

Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

Abstract:

In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Derkaoui Orkia, Lehireche Ahmed

Abstract:

The topic of this article is to exploring the potentialities of a powerful optimization technique, namely Semidefinite Programming, for solving NP-hard problems. This approach provides tight relaxations of combinatorial and quadratic problems. In this work, we solve the maximum clique problem using this relaxation. The clique problem is the computational problem of finding cliques in a graph. It is widely acknowledged for its many applications in real-world problems. The numerical results show that it is possible to find a maximum clique in polynomial time, using an algorithm based on semidefinite programming. We implement a primal-dual interior points algorithm to solve this problem based on semidefinite programming. The semidefinite relaxation of this problem can be solved in polynomial time.

Keywords: semidefinite programming, maximum clique problem, primal-dual interior point method, relaxation

Procedia PDF Downloads 209