Search results for: maritime security intelligence

Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Debasmita Misra, Arthur Nash

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Energy security and sufficiency enables the economic development and welfare of a nation or a society. Currently, the global energy system is dominated by fossil fuels, which is a non-renewable energy resource, which renders vulnerability to energy security. Hence, many nations have begun augmenting their energy system with renewable energy resources, such as solar, wind, biomass and hydro. However, with climate change, how sustainable are some of the renewable energy resources in the future is a matter of concern. Geothermal energy resources have been underexplored or underexploited in global renewable energy production and security, although it is gaining attractiveness as a renewable energy resource. The question is, whether geothermal energy resources are more sustainable than other renewable energy resources. High-temperature reservoirs (> 220 °F) can produce electricity from flash/dry steam plants as well as binary cycle production facilities. Most of the world’s high enthalpy geothermal resources are within the seismo-tectonic belt. However, exploration for geothermal energy is of great importance in conventional geothermal systems in order to improve its economic viability. In recent years, there has been an increase in the use and development of several exploration methods for geo-thermal resources, such as seismic or electromagnetic methods. The thermal infrared band of the Landsat can reflect land surface temperature difference, so the ETM+ data with specific grey stretch enhancement has been used to explore underground heat water. Another way of exploring for potential power is utilizing fairway play analysis for sites without surface expression and in rift zones. Utilizing this type of analysis can improve the success rate of project development by reducing exploration costs. Identifying the basin distribution of geologic factors that control the geothermal environment would help in identifying the control of resource concentration aside from the heat flow, thus improving the probability of success. The first step is compiling existing geophysical data. This leads to constructing conceptual models of potential geothermal concentrations which can then be utilized in creating a geodatabase to analyze risk maps. Geospatial analysis and other GIS tools can be used in such efforts to produce spatial distribution maps. The goal of this paper is to discuss how climate change may impact renewable energy resources and how could a synthesized analysis be developed for geothermal resources to ensure sustainable and cost effective exploitation of the resource.

Keywords: exploration, geothermal, renewable energy, sustainable

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Authors: Sofia Sutera

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Despite the introduction of the Women, Peace, and Security (WPS) Agenda in 2000, thanks to the UNSCR 1325 and subsequent resolutions, and the clear stance of the UN towards the support of increased participation of women in peace and security processes, women’s leadership in this context remains very low. Considering specifically the framework of peacekeeping operations, the aim of this paper is to analyze the way women’s leadership is constructed in the Swedish Armed Forces (SAF). In the context of the WPS Agenda, Sweden has been chosen as a case study because of the relevance of its singular feminist policies (the statement in 2014 from Wallström, previous and current Minister for Foreign Affairs and Deputy Prime Minister, that Sweden is pursuing a feminist foreign policy is a clear example). Moreover, the SAF adopted in 2016 the Handbok Gender. This policy addresses explicitly the gender perspective embraced by the Swedish military institution, a sui-generis organization even in the Scandinavian reality. Indeed, the SAF has assumed a clear commitment to represent its institution as gender aware and gender equal. The theoretical perspective utilized in this research, which focuses specifically on women, is feminism and particularly a feminist constructivist approach, with an institutional focus on the military institution, has been chosen. Taking into account the specificity of the feminist research, the above-mentioned gender policy has been examined by means of a critical discourse analysis (CDA) whose main aim is to investigate the social structures of discourse and the power relationships inherent to it. Thus, CDA appears to be quite relevant in order to understand the construction of women’s leadership in the Handbok Gender. Nevertheless, even in a country which officially identifies as feminist and which is characterized by a peculiar military institution, the conclusions of this analysis revealed that women’s leadership in peacekeeping operations remains very low.

Keywords: feminism, peacekeeping operations, swedish armed forces, UNSCR 1325, women's leadership, WPS agenda

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Authors: Abdulrahman Yarali, Machica McClain

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In the context of the increasing use of Cyber-Physical Systems (CPS) across critical infrastructure sectors, this paper addresses a crucial and emerging topic: the integration of Identity and Access Management (IAM) with Internet of Things (IoT) devices in Medical Cyber-Physical Systems (MCPS). It underscores the significance of robust IAM solutions in the expanding interconnection of IoT devices in healthcare settings, leveraging AI, ML, DL, Zero Trust Architecture (ZTA), biometric authentication advancements, and blockchain technologies. The paper advocates for the potential benefits of transitioning from traditional, static IAM frameworks to dynamic, adaptive solutions that can effectively counter sophisticated cyber threats, ensure the integrity and reliability of CPS, and significantly bolster the overall security posture. The paper calls for strategic planning, collaboration, and continuous innovation to harness these benefits. By emphasizing the importance of securing CPS against evolving threats, this research contributes to the ongoing discourse on cybersecurity and advocates for a collaborative approach to foster innovation and enhance the resilience of critical infrastructure in the digital era.

Keywords: CPS, IAM, IoT, AI, ML, authentication, models, policies, healthcare

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Authors: Zunaira Zaman, David Bohannon

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This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Antonella Falanga, Armando Cartenì

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"Big data" refers to extremely vast and complex sets of data, encompassing extraordinarily large and intricate datasets that require specific tools for meaningful analysis and processing. These datasets can stem from diverse origins like sensors, mobile devices, online transactions, social media platforms, and more. The utilization of big data is pivotal, offering the chance to leverage vast information for substantial advantages across diverse fields, thereby enhancing comprehension, decision-making, efficiency, and fostering innovation in various domains. Big data, distinguished by its remarkable attributes of enormous volume, high velocity, diverse variety, and significant value, represent a transformative force reshaping the industry worldwide. Their pervasive impact continues to unlock new possibilities, driving innovation and advancements in technology, decision-making processes, and societal progress in an increasingly data-centric world. The use of these technologies is becoming more widespread, facilitating and accelerating operations that were once much more complicated. In particular, big data impacts across multiple sectors such as business and commerce, healthcare and science, finance, education, geography, agriculture, media and entertainment and also mobility and logistics. Within the transportation sector, which is the focus of this study, big data applications encompass a wide variety, spanning across optimization in vehicle routing, real-time traffic management and monitoring, logistics efficiency, reduction of travel times and congestion, enhancement of the overall transportation systems, but also mitigation of pollutant emissions contributing to environmental sustainability. Meanwhile, in public administration and the development of smart cities, big data aids in improving public services, urban planning, and decision-making processes, leading to more efficient and sustainable urban environments. Access to vast data reservoirs enables deeper insights, revealing hidden patterns and facilitating more precise and timely decision-making. Additionally, advancements in cloud computing and artificial intelligence (AI) have further amplified the potential of big data, enabling more sophisticated and comprehensive analyses. Certainly, utilizing big data presents various advantages but also entails several challenges regarding data privacy and security, ensuring data quality, managing and storing large volumes of data effectively, integrating data from diverse sources, the need for specialized skills to interpret analysis results, ethical considerations in data use, and evaluating costs against benefits. Addressing these difficulties requires well-structured strategies and policies to balance the benefits of big data with privacy, security, and efficient data management concerns. Building upon these premises, the current research investigates the efficacy and influence of big data by conducting an overview of the primary and recent implementations of big data in transportation systems. Overall, this research allows us to conclude that big data better provide to enhance rational decision-making for mobility choices and is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, public transport, sustainable mobility, transport demand, transportation planning

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Inayat Ur Rehman, Georgia Sakellari

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Cloud computing has evolved into a vital technology for businesses, offering scalability, flexibility, and cost-effectiveness. However, maintaining high availability and optimal performance in the cloud is crucial for reliable services. This paper explores the significance of cloud monitoring and performance optimization in sustaining the high availability of cloud-based systems. It discusses diverse monitoring tools, techniques, and best practices for continually assessing the health and performance of cloud resources. The paper also delves into performance optimization strategies, including resource allocation, load balancing, and auto-scaling, to ensure efficient resource utilization and responsiveness. Addressing potential challenges in cloud monitoring and optimization, the paper offers insights into data security and privacy considerations. Through this thorough analysis, the paper aims to underscore the importance of cloud monitoring and performance optimization for ensuring a seamless and highly available cloud computing environment.

Keywords: cloud computing, cloud monitoring, performance optimization, high availability, scalability, resource allocation, load balancing, auto-scaling, data security, data privacy

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate and even exceed some benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. A common facet of many ITS is their use of Bayesian Knowledge Tracing (BKT) to estimate parameters necessary for the implementation of the artificial intelligence component, and for the probability of mastery of a knowledge component relevant to the ITS. While various techniques exist to estimate these parameters and probability of mastery, none directly and reliably ask the user to self-assess these. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course for which detailed transaction-level observations were recorded, and users were also routinely asked direct questions that would lead to such a self-assessment. Comparisons were made between these self-assessed values and those obtained using commonly used estimation techniques. Our findings show that such self-assessments are particularly relevant at the early stages of ITS usage while transaction level data are scant. Once a user’s transaction level data become available after sufficient ITS usage, these can replace the self-assessments in order to eliminate the identifiability problem in BKT. We discuss how these findings are relevant to the number of exercises necessary to lead to mastery of a knowledge component, the associated implications on learning curves, and its relevance to instruction time.

Keywords: Bayesian Knowledge Tracing, Intelligent Tutoring System, in vivo study, parameter estimation

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Authors: Xingyu Gao, Qiang Wu, Yuanyuan Liu, Yue Yang

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In the era of the knowledge economy, the relationship between scientific knowledge and patents has garnered significant attention. Understanding the intricate interplay between the foundations of science and technological innovation has emerged as a pivotal challenge for both researchers and policymakers. This study establishes a coupled network of artificial intelligence patents based on co-cited scientific papers. Leveraging centrality metrics from network analysis offers a fresh perspective on understanding the influence of information flow and knowledge sharing within the network on patent impact. The study initially obtained patent numbers for 446,890 granted US AI patents from the United States Patent and Trademark Office’s artificial intelligence patent database for the years 2002-2020. Subsequently, specific information regarding these patents was acquired using the Lens patent retrieval platform. Additionally, a search and deduplication process was performed on scientific non-patent references (SNPRs) using the Web of Science database, resulting in the selection of 184,603 patents that cited 37,467 unique SNPRs. Finally, this study constructs a coupled network comprising 59,379 artificial intelligence patents by utilizing scientific papers co-cited in patent backward citations. In this network, nodes represent patents, and if patents reference the same scientific papers, connections are established between them, serving as edges within the network. Nodes and edges collectively constitute the patent coupling network. Structural characteristics such as node degree centrality, betweenness centrality, and closeness centrality are employed to assess the scientific connections between patents, while citation count is utilized as a quantitative metric for patent influence. Finally, a negative binomial model is employed to test the nonlinear relationship between these network structural features and patent influence. The research findings indicate that network structural features such as node degree centrality, betweenness centrality, and closeness centrality exhibit inverted U-shaped relationships with patent influence. Specifically, as these centrality metrics increase, patent influence initially shows an upward trend, but once these features reach a certain threshold, patent influence starts to decline. This discovery suggests that moderate network centrality is beneficial for enhancing patent influence, while excessively high centrality may have a detrimental effect on patent influence. This finding offers crucial insights for policymakers, emphasizing the importance of encouraging moderate knowledge flow and sharing to promote innovation when formulating technology policies. It suggests that in certain situations, data sharing and integration can contribute to innovation. Consequently, policymakers can take measures to promote data-sharing policies, such as open data initiatives, to facilitate the flow of knowledge and the generation of innovation. Additionally, governments and relevant agencies can achieve broader knowledge dissemination by supporting collaborative research projects, adjusting intellectual property policies to enhance flexibility, or nurturing technology entrepreneurship ecosystems.

Keywords: centrality, patent coupling network, patent influence, social network analysis

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Authors: Yating Yang, Xue Zhang, Chengli Zhao

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In the increasingly interconnected global environment brought about by globalization, it is crucial for countries to timely grasp the development motivations in relevant research fields of other countries and seize development opportunities. Motivation, as the intrinsic driving force behind actions, is abstract in nature, making it difficult to directly measure and evaluate. Drawing on the ideas of social influence network theory, the research motivations of a country can be understood as the driving force behind the development of its science and technology sector, which is simultaneously influenced by both the country itself and other countries/regions. In response to this issue, this paper improves upon Friedkin's social influence network theory and applies it to motivation description, constructing a dynamic alliance network and hostile network centered around the United States and China, as well as a sensitivity matrix, to remotely assess the changes in national research motivations under the influence of international relations. Taking artificial intelligence as a case study, the research reveals that the motivations of most countries/regions are declining, gradually shifting from a neutral attitude to a negative one. The motivation of the United States is hardly influenced by other countries/regions and remains at a high level, while the motivation of China has been consistently increasing in recent years. By comparing the results with real data, it is found that this model can reflect, to some extent, the trends in national motivations.

Keywords: influence network theory, remote assessment, relation matrix, dynamic sensitivity matrix

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Seember Susan Aondoakura

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This paper examines the international legal framework established to address the challenges women and girls experience in situations of violent conflict. The United Nations (UN) women, peace, and security agenda (hereafter WPS agenda, the Agenda) aspire to make wars safer for women. It recognizes women's agency in armed conflict and their victimization and formulates measures for their protection. The Agenda also acknowledges women's participation in conflict transformation and post-conflict reconstruction. It also calls for the involvement of women in conflict transformation, encourages the protection of women from sex and gender-based violence (SGBV), and provides relief and recovery from conflict-related SGBV. Using Third World Critical Feminist Theory, this paper argues that the WPS agenda overly focus on the protection of women from SGBV occurring in the less developed and conflict-ridden states in the global south, obscures the complicity of western states and economies to the problem, and silences the privileges that such states derive from war economies that continue to fuel conflict. This protectionist approach of the UN also obliterates other equally pressing problems in need of attention, like the high rates of economic degradation in conflict-ravaged societies of the global south. Prioritising protection also 'others' the problem, obliterating any sense of interconnections across geographical locations and situating women in the less developed economies of the global south as the victims and their men as the perpetrators. Prioritising protection ultimately situates western societies as saviours of Third World women with no recourse to their role in engendering and sustaining war. The paper demonstrates that this saviour mentality obliterates chances of any meaningful coalition between the local and the international in framing and addressing the issue, as solutions are formulated from a specific lens—the white hegemonic lens.

Keywords: conflict, protection, security, SGBV

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Authors: Gianvito Colucci, Valeria Di Cosmo, Matteo Nicoli, Orsola Maria Robasto, Laura Savoldi

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The hydrogen value chain deployment is likely to be boosted in the near term by the energy security measures planned by European countries to face the recent energy crisis. In this context, some countries are recognized to have a crucial role in the geopolitics of hydrogen as importers, consumers and exporters. According to the European Hydrogen Backbone Initiative, Italy would be part of one of the 5 corridors that will shape the European hydrogen market. However, the set targets are very ambitious and require large investments to rapidly develop effective hydrogen policies: in this regard, scenario analysis is becoming increasingly important to support energy planning, and energy system optimization models appear to be suitable tools to quantitively carry on that kind of analysis. The work aims to assess the competitiveness of hydrogen in contributing to the Italian energy security in the coming years, under different price and import conditions, using the energy system model TEMOA-Italy. A wide spectrum of hydrogen technologies is included in the analysis, covering the production, storage, delivery, and end-uses stages. National production from fossil fuels with and without CCS, as well as electrolysis and import of low-carbon hydrogen from North Africa, are the supply solutions that would compete with other ones, such as natural gas, biomethane and electricity value chains, to satisfy sectoral energy needs (transport, industry, buildings, agriculture). Scenario analysis is then used to study the competition under different price and import conditions. The use of TEMOA-Italy allows the work to catch the interaction between the economy and technological detail, which is much needed in the energy policies assessment, while the transparency of the analysis and of the results is ensured by the full accessibility of the TEMOA open-source modeling framework.

Keywords: energy security, energy system optimization models, hydrogen, natural gas, open-source modeling, scenario analysis, TEMOA

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Abel Edeowede Abhulimen

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Due to supply irregularity and frequent outages, the need for reliability in power supply has grown unsatisfactorily over time in developing nations, impeding industrialization and fueling insecurity. This article attempts to break down the Nigerian power issue into its numerous sub-sectors in order to pinpoint the industry's ailment and suggest a viable fix. Monthly average performance data were obtained for the various sub-sectors across the industry for eight consecutive quarters. Whereas the amount of energy generated was found to be insufficient to engender industrialization in a nation like Nigeria, the transmission infrastructure was inadequate for the amount of power needed to be wheeled. Additionally, the distribution sub-sector was plagued with problems such as revenue collection inefficiency, severe enough to impede the growth of the entire industry. The country's goal of attaining energy sufficiency and industrialization would significantly be closer to reality with a conscious effort to increase the base of power generation through aggressive investment in Combined Cycle Gas Turbines (CCGT), decentralization of the transmission infrastructure, and strict monitoring of the distribution sub-sector for improved accountability and system reliability.

Keywords: performance, power industry, industrialization, security, energy

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Authors: Hiba El Assibi

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This study aims to weakly solve Kalah, a two-player board game, by developing a start-to-finish winning strategy using an optimized Minimax algorithm with Alpha-Beta Pruning. In weakly solving Kalah, our focus is on creating an optimal strategy from the game's beginning rather than analyzing every possible position. The project will explore additional enhancements like symmetry checking and code optimizations to speed up the decision-making process. This approach is expected to give insights into efficient strategy formulation in board games and potentially help create games with a fair distribution of outcomes. Furthermore, this research provides a unique perspective on human versus Artificial Intelligence decision-making in strategic games. By comparing the AI-generated optimal moves with human choices, we can explore how seemingly advantageous moves can, in the long run, be harmful, thereby offering a deeper understanding of strategic thinking and foresight in games. Moreover, this paper discusses the evaluation of our strategy against existing methods, providing insights on performance and computational efficiency. We also discuss the scalability of our approach to the game, considering different board sizes (number of pits and stones) and rules (different variations) and studying how that affects performance and complexity. The findings have potential implications for the development of AI applications in strategic game planning, enhancing our understanding of human cognitive processes in game settings, and offer insights into creating balanced and engaging game experiences.

Keywords: minimax, alpha beta pruning, transposition tables, weakly solving, game theory

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Emad Bataineh, Sara Al-Mutawa

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The primary focus of this study is to investigate and identify the perceptions of potential end users relating to factors which impact on e-services acceptance. Technology Acceptance Model (TAM) has been adopted in this study as it can be extended when technologies are introduced. This research validates the developed TAM model and evaluates the variance of the outcome variable (acceptance of e-services). Five factors were adopted as determinants of acceptance of e-services: ease of use, security, trust, web skills, and language. The study was undertaken in the General Directorate of Residency and Foreigners Affairs (GDRFA) in the UAE. A quantitative survey methodology was adopted in this study, which surveyed 466 customers who use the GDRFA e-services. The overall findings revealed that security language, web skills and support significantly affected ease of use and perceived usefulness. However, the trust doesn’t affect the ease of use. Further, ease of use significantly affects intention to use and perceived usefulness while in turn intention to use was influenced by perceived usefulness. This study offers an understanding of people’s adoption of e-government services with the help of established theories such as TAM and various factors that influence the e-government adoption with reference to UAE.

Keywords: e-government portal, e-service, usability, TAM model

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Authors: William Wong, Zakaria Alomari, Hon Ching Lai, Zhida Li

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Modern-day information security depends on implementing Diffie-Hellman, which requires the generation of prime numbers. Because the number of primes is infinite, it is impractical to store prime numbers for use, and therefore, primality tests are indispensable in modern-day information security. A primality test is a test to determine whether a number is prime or composite. There are two types of primality tests, which are deterministic tests and probabilistic tests. Deterministic tests are adopting algorithms that provide a definite answer whether a given number is prime or composite. While in probabilistic tests, a probabilistic result would be provided, there is a degree of uncertainty. In this paper, we review three probabilistic tests: the Fermat Primality Test, the Miller-Rabin Test, and the Baillie-PSW Test, as well as one deterministic test, the Agrawal-Kayal-Saxena (AKS) Test. Furthermore, we do an analysis of these tests. All of the reviews discussed are not based on the Elliptic Curve. The analysis demonstrates that, in the majority of real-world scenarios, the Baillie- PSW test’s favorability stems from its typical operational complexity of O(log 3n) and its capacity to deliver accurate results for numbers below 2^64.

Keywords: primality tests, Fermat’s primality test, Miller-Rabin primality test, Baillie-PSW primality test, AKS primality test

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Anthony Nyamekeh-Armah Adjei, Toshiaki Aoki

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The unreliable and poor efficiency of electricity distribution leading to frequent power outages and high losses are the major challenge facing the power distribution sector in Ghana. Distribution system routes electricity from the power generating station at a higher voltage through the transmission grid and steps it down through the low voltage lines to end users. Approximately all electricity problems and disturbances that have increased the call for renewable and sustainable energy in recent years have their roots in the distribution system. Therefore, sustaining electricity distribution efficiency can potentially contribute to the reserve of natural energy resources use in power generation, reducing greenhouse gas emission (GHG), decreasing tariffs for consumers and effective human security. Human Security is a people-centered approach where individual human being is the principal object of concern, focuses on protecting the vital core of all human lives in ways for meeting basic needs that enhance the safety and protection of individuals and communities. The vulnerability is the diminished capacity of an individual or group to anticipate, resist and recover from the effect of natural, human-induced disaster. The research objectives are to explore the causes of frequent power outages to consumers, high losses in the distribution network and the effect of poor electricity distribution efficiency on the vulnerable (poor and ordinary) people that mostly depend on electricity for their daily activities or life to survive. The importance of the study is that in a developing country like Ghana where raising a capital for new infrastructure project is difficult, it would be beneficial to enhance the efficiency that will significantly minimize the high energy losses, reduce power outage, to ensure safe and reliable delivery of electric power to consumers to secure the security of people’s livelihood. The methodology used in this study is both interview and questionnaire survey to analyze the response from the respondents on causes of power outages and high losses facing the electricity company of Ghana (ECG) and its effect on the livelihood on the vulnerable people. Among the outcome of both administered questionnaire and the interview survey from the field were; poor maintenance of existing sub-stations, use of aging equipment, use of poor distribution infrastructure and poor metering and billing system. The main observation of this paper is that the poor network efficiency (high losses and power outages) affects the livelihood of the vulnerable people. Therefore, the paper recommends that policymakers should insist on all regulation guiding electricity distribution to improve system efficiency. In conclusion, there should be decentralization of off-grid solar PV technologies to provide a sustainable and cost-effective, which can increase daily productivity and improve the quality of life of the vulnerable people in the rural communities.

Keywords: electricity efficiency, high losses, human security, power outage

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Innocensia John, Henrik Egelyng, Razack Lokina

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As the world`s population is constantly growing, food security has become a thorny trending issue. The impact has particularly been felt more in Africa as most of the people depend on food Agriculture products. Geographical Indications can aid in transforming the Tanzania agriculture-dependent economy through tapping the unique attributes of their quality products like soil, taste color etc. Consumers worldwide demand more uniquer products featuring a ´connect´ with the land use systems producing particular qualities. Tanzania has demonstrated the capacity to tap into the organic world market and has untapped potential for harvesting market value from geographical indications. This paper presents preliminary results from VALOR — a research project investigating conditions under which Tanzanian origin food producers can add value by incorporating territory specific cultural, environmental and social qualities into marketing, production and processing of unique local, niche and specialty products. Cases are investigated of the prospects for Tanzania to leapfrog perhaps into exports of geographical indications products, and certainly into allowing smallholders to create employment and build monetary value, while stewarding local food cultures and natural environments and resources, and increasing the diversity of supply of natural and unique quality products and so contribute to enhanced food security. Rice from Kyela, coffee and Sugar from Kilimanjaro, are some of the product cases investigated and provides for the in-depth case study, as ´landscape´ products incorporating ´taste of place´. Framework conditions for producers creating or capturing market value as stewards of cultural and landscape values and environments and institutional requirements for such creation or capturing to happen, including presence of export opportunities, are discussed.

Keywords: food origins, food security, protected geographical indications, case study analysis

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Gassem Alfaleh

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In Strict liability, a person can be held liable for any harm resulting from certain actions or activities without any mistake. The liability is strict because a person can be liable when he or she commits any harm with or without his intention. The duty owed is the duty to avoid causing the plaintiff any harm. However, “strict liability is imposed at the International level by two types of treaties, namely those limited to giving internal effect to treaty provisions and those that impose responsibilities on states. The basic principle of strict liability is that there is a liability on the operator or the state (when the act concerned is attributable to the state) for damage inflicted without there being a need to prove unlawful behavior”. In international human rights law, strict liability can exist when a defendant is in legal jeopardy by virtue of an internationally wrongful act, without any accompanying intent or mental state. When the defendant engages in an abnormally dangerous activity against the environment, he will be held liable for any harm it causes, even if he was not at fault. The paper will focus on these activities under international human rights law. First, the paper will define important terms in the first section of the paper. Second, it will focus on state and non-state actors in terms of strict liability. Then, the paper will cover three major areas in which states should be liable for hazardous activities: (1) nuclear energy, (2) maritime pollution, (3) Space Law, and (4) other hazardous activities which damage the environment.

Keywords: human rights, law, legal, absolute

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Authors: Samuel Kehinde Okunade, Daniel Sunday Tolorunshagba

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Globally women are classified to be part of the vulnerable group in any environment. In a conflict-ridden environment, women being vulnerable often suffer the consequences as it relates to security and access to basic social services such as medical care. This is the situation in border communities in Nigeria where cross-border crimes are on the rife, thus, putting women at a disadvantaged position and, eventually, victims of such inimical activities. Border communities in the Saki/Iseyin area of Oyo state are a case in point where the lives of inhabitants are daily threatened most, especially women. In light of the above, this article examined the security situation of the Saki/Iseyin area of Oyo State with a view to ascertaining its status in terms of safety of lives and property. This paper also explored the experiences of women in the border communities within the area as it relates to their safety, the safety of their children, access to good health facilities in their immediate environment, and above all, how they have been able to cope or manage the situation. The qualitative research model was adopted utilizing a phenomenological case study approach. A Focused Group Discussion was conducted with 10 pregnant women and 10 mothers in Okerete and Abugudu communities while a Key Informant Interview was conducted with the women leaders in both communities of the Saki/Iseyin border area of Oyo State. The findings of the study revealed the poor state of basic infrastructure. So bad to a point that inhabitants of these communities no longer see themselves as Nigerians because they have been neglected by the government for too long. The only solution is for the government to embark on developmental projects within these communities so that they can live a good life just as those in the cities do. More importantly, this will increase the loyalty of these communities to the Nigeria state by defending and resisting all forms of cross-border criminal activities that go on along the porous borders.

Keywords: security, women, Saki/Iseyin border area, cross-border criminalities, basic infrastructure

Procedia PDF Downloads 125