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Cyber Security in Smart Cities and Its Challenges: A Systematic Literature Review

Cyber Security in Smart Cities and Its Challenges: A Systema...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International Conference on

作者： Benedicto Theodore Ruatta Gabriel Jonatan Piekarsa Jeremia Christian Ivan Sebastian Edbert Alexander Agung Santoso Gunawan Computer Science Department Cyber Security Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

At this time, the development of information and communication technology has mostly been used maximally to improve the quality of life of citizens around the world. The benefits that are obtained make some cities evolve into smart cities in improving living standards and provide services in the health sector because it can control information technology and communication and cybersecurity challenges that are always evolving in every era. However, Behind the developments and benefits of it all, there are risks that cannot be avoided. Risk and these threats can be overcome through the national spectrum by implementing cybersecurity in the virtual world in developing the city into a smart city. Therefore, to respond to cybersecurity in smart cities, cybersecurity must evolve in the same direction, so that the strategy that has been formed will become the basis and reference in implementing cyber security in support of smart city development and the challenges that exist. This paper will outline the relationship between the two factors, the significance of cyber security for smart cities, how such cities can be attacked, and how to defend against such attacks. And after conducting the study, the authors reach the conclusion that in order for any smart cities to develop, flourish, and ensure that everyone living there is safe from any threat, cyber security is essential.

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Enhancing Road Segmentation in Satellite Images via Double U-Net with Advanced Pre-Processing

Enhancing Road Segmentation in Satellite Images via Double U...

引用

International Conference on Informatics and Computing (ICIC)

作者： Raden Bagus Muhammad Adryanputra Adhy Wijaya Wahyono Undergraduate Program in Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Roads play an important role in people’s lives by providing access to essential facilities such as schools and hospitals, thereby increasing people’s productivity. The use of satellite imagery and computer vision technology can speed up and improve the efficiency of road map creation. Deep learning methods such as U-Net have been used for road segmentation from satellite images, but the results have yet to be optimal. This study aims to examine the performance of DoubleU-Net and the effect of image pre-processing in satellite image-based road segmentation. This research implements the DoubleU-Net deep learning model for road segmentation based on satellite images. Pre-processing methods such as rotating augmentation, contrast stretching, and edge enhancement are applied to increase the contrast value and clarify the edges in the satellite images used. The results show that combining the two U-Net models into a DoubleU-Net model results in a significant improvement in the IoU and dice coefficient values, achieving an average increase of $\mathbf{2 \%}$ and integrated with image processing, the model performance experiences an average increase in IoU and dice coefficient values of $\mathbf{2 - 4} \%$. The highest performance was achieved by the DoubleU-Net model with rotating augmentation pre-processing, resulting in an IoU performance value of $\mathbf{4 5. 9 9 \%}$ and a dice coefficient performance of $\mathbf{6 1. 1 2 \%}$.

关键词： Deep learning Productivity Image segmentation computer vision Smoothing methods Roads Image edge detection Semantic segmentation Satellite images Remote sensing

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Meal Image Recognition and Healthy Meal Combination Recommendation System Integrated with Generative Artificial Intelligence 6

Meal Image Recognition and Healthy Meal Combination Recommen...

引用

6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Chang, Chin-Chih Wei, Chi-Hung Hsiao, Sean Yang, Chyuan-Huei Thomas Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan School of Information Engineering Shandong Vocational and Technical University of International Studies Shandong China

ISBN: (纸本)9798350396133

People increasingly prioritize a balanced diet to enhance well-being, yet making informed dietary choices remains challenging amidst the abundance of options. To address this, we developed a meal image recognition and healthy meal combination recommender system, integrating generative artificial intelligence (AI). Convolutional neural networks were used for precise meal image recognition to identify diverse food items. The generative AI-augmented recommendation engine offers personalized meal suggestions aligned with nutritional goals and dietary preferences, utilizing a nutrition knowledge base to ensure overall well-being. The system's feasibility was validated, illustrating its excellence in meal recognition accuracy, recommendation diversity, and user engagement. By integrating generative AI, the system shows its potential to enhance dietary recommendations and public health. © 2024 IEEE.

关键词： Generative adversarial networks

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Air Pollution Level Prediction in Jakarta Using Artificial Neural Network

Air Pollution Level Prediction in Jakarta Using Artificial N...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Roger Ferdinan Krestella Margareta Stevan Christyan Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Air pollution has emerged as a critical issue in several major urban areas, including Jakarta. To address this problem, the study explores the utilization of the Artificial Neural Network (ANN) method. Three distinct approaches are investigated in this research: the Support Vector Classifier (SVC), Deep Artificial Neural Network (Deep ANN), and Long Short-Term Memory (LSTM). The available Air Quality data were utilized to train and assess the performance of the proposed ANN models. The research findings reveal that the Deep ANN model surpasses the other methods, achieving an impressive accuracy of approximately 96.57% and a cross-entropy value of 0.1103. In predicting air quality in Jakarta, Deep ANN has demonstrated superior performance when compared to SVC and LSTM. These results highlight the significant potential of deploying Deep Artificial Neural Network (ANN) methodologies for air quality prediction. Such an approach could play a pivotal role in the development of monitoring and early warning systems aimed at effectively addressing air quality issues in the Jakarta region.

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Systematic Literature Review on IT Strategic Workspaces: Approach to Develop an Ideal Environment

Systematic Literature Review on IT Strategic Workspaces: App...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Alden Ardiwinata Putra Princessa Fortunata Fusanto Nebrisca Valonika Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Technology and information will always develop dynamically; this statement demands programmers to always be creative and keep up with the times. Despite this, their work ethic is always the same and tends to stagnate. programmers’ perspectives on their workplace: this perspective is rarely seen as important and the opportunity for a comprehensive study is still widely open. This study aims to conduct a systematic literacy study to ascertain that the workplace has a crucial relationship with a programmer’s performance. The focus of the study includes a discussion of how programmers relate to their workplace in a tech company, benchmarking between various office layouts, and criteria that can be derived from the literature review regarding a degree of whether such a workplace is good or bad. The proper base facts are obtained. Nevertheless, the two entities are proven to be interrelated to each other. Such correlations are derived into tangible metrics and parameters (e.g., human cognitive, cultural, physical, behavioral, etc.). Relating issues regarding these topics are also presented.

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Analyzing Microservices and Monolithic Systems: Key Factors in Architecture, Development, and Operations

Analyzing Microservices and Monolithic Systems: Key Factors ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Juan Christian Steven Afdhal Kurniawan Maria Susan Anggreainy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Microservices and monolithic systems are two prevalent architectural approaches in software development. This study provides a complete review and analysis of the key components involved in design, development, and operation in software development. A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review examined various sources debating monolithic systems vs microservices, highlighting their benefits, drawbacks, and implementation in enterprises. The paper addresses important research questions, aiming to further analyze this architectural approach. Findings show that microservices offer benefits such as scalability, flexibility, and independent deployment, while monolithic systems provide simplicity and ease of development. However, challenges related to network communication, data consistency, and operational complexity were also found with microservices. This research focuses on discussing the trade-offs and factors to consider when deciding between monolithic systems and microservices, which provides in-depth information for practitioners in decision-making for software development. This research aims to help readers understand the effects of using monolithic or microservice-based systems in software development.

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Random Forest Based QSAR Model Analysis for Predicting Drug Effectiveness Against Mycobacterium Tuberculosis Bacteria

Random Forest Based QSAR Model Analysis for Predicting Drug ...

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International Conference on ICT For Smart Society (ICISS)

作者： Alicia Josephine Ekosputri Amanda Ardianti Maria Susan Anggreainy Brychan Artanto Nora Fitriawati Fanny Angelia Valentina Vincent Kartamulya Santoso Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research.

关键词： Drugs Accuracy Tuberculosis Biological system modeling Linear regression Predictive models Compounds Reliability Random forests Overfitting

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A Survey Study of Social Media User Awareness Towards Personal Identifiable Information's Data Security in Indonesian Millennial Generation

A Survey Study of Social Media User Awareness Towards Person...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International Conference on

作者： Gabriel Alexander Christie Ivan Javier Trevan Said Achmad Franz Adeta Junior Nadia Computer Science Department Cyber Security Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Online Social Network is a network communication platform where users have profiles that can be uniquely identified by the content sent. This content can be produced, consumed, and interacted with by other users. To connect with other users on social media, users must register by providing Personally Identifiable Information (PII) to social media platforms. PII is specific information that can identify or track individuals directly. This specific information may include your name, address, social security number, or other identifying code numbers such as telephone numbers, email addresses, and others. Personal identifiable information leakage is a problem in data security. Basically, every individual does not want their personal data to be known by anyone. Utilizing a sample size of 50 respondents, this study aims to ascertain the percentage of individuals who are aware of PII security on social media. This research will use quantitative methods by distributing questionnaires. The questionnaire in this study uses a social media attribute design. The results of the survey indicate that many respondents are unaware of the security of their data and have a limited understanding of how their personal data is managed by technology companies, particularly the 80% of non-IT respondents.

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The Usage of Machine Learning on Penetration Testing Automation

The Usage of Machine Learning on Penetration Testing Automat...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International Conference on

作者： Clintswood Delbert Giovanni Lie Lexiandy Kuswandana Nadia Said Achmad Derwin Suhartono Computer Science Department Cyber Security Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Penetration testing is a method to assess the security within a network by performing or simulating a real-world cyber-attack on the network. It has been one of the best ways preferred by organizations to strengthen their network defenses against cyber threats. However, penetration testing proves that it has several drawbacks such as requiring a significant number of skills and time to perform. A survey made by The International Information System Security Certification Consortium also shows that the world is lacking cybersecurity workforces. To address this problem, this paper conducts research regarding the usage of machine learning to automate penetration testing activity. To start off, automated penetration testing tools will be created using machine learning algorithms, specifically reinforcement learning and deep reinforcement learning algorithms. The second step is to put the tools in a learning stage where it will be provided with the MITRE ATT&CK Framework. This framework will be the base for the attacks and exploitations used within the tools. The third step is to conduct a series of automated penetration testing over different target networks. The result will be then compared and analyzed with a manual penetration testing report to see how efficient and better it is compared to a manual penetration test. Lastly, the final data will then be collected for future developments and recommendations.

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Machines Develop Consciousness Through Autonomous programming for General Purposes (APFGP) 1

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2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Weng, Juyang Department of Computer Science Cognitive Science Program Neuroscience Program Michigan State University East LansingMI48824 United States GENISAMA LLC OkemosMI48864 United States

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Consider a question, "Can machines be conscious?" The subject "consciousness" is vague and challenging. Although there has been a rich collection of literature on consciousness, computational modeling of consciousness that is both holistic in scope and detailed in simulatable computation is lacking. Based on recent advances on a new capability—Autonomous programming For General Purposes (APFGP)—this work presents APFGP as a clearer, deeper and more practical characterization of consciousness, for natural (biological) and artificial (machine) systems. All animals have APFGP but traditional AI systems do not. This work reports a new kind of AI systems—conscious machines. Instead of arguing what static tasks a conscious machine should be able to do, this work suggests that APFGP is a computationally clearer and necessary criterion for us to dynamically judge whether a system can become maturely conscious through lifelong development, even if it (e.g., a fruit fly) does not have a full array of primate like capabilities such as vision, audition, and natural language understanding. The results here involve a series of new concepts and experimental studies for vision, audition, and natural languages with new developmental capabilities that are not present in many published systems, e.g., IBM Deep Blue, IBM Watson, AlphaGo, AlphaFold and other traditional AI systems and intelligent robots. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Audition

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