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21st OITS International Conference on Information Technology, OCIT 2023

作者： Mittal, Pragya Bapatla, Anand Kumar Lenka, Manas Ranjan Mohanty, Saraju P. KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India University of North Texas Department of Computer Science and Engineering United States

ISBN: (纸本)9798350358230

Student feedback data is typically stored on centralized servers and can potentially be linked to individual identities, leading to concerns about repercussions or bias in evaluations. This lack of anonymity can inhibit students from providing honest and candid feedback. Because of this reason, a secure anonymous platform is needed as it provides the students with an uninhibited channel to offer their feedback. To overcome this, an architecture incorporating Blockchain into a Student Feedback System is proposed to address traditional systems' anonymity and transparency issues. This paper proposes an implementation of a robust student feedback system that fosters a dynamic exchange of feedback and enhances interpersonal relationships and collaboration between students and teachers. This constructive feedback loop facilitates the improvement of faculty performance and facilitates a greater understanding of students' educational requirements, ultimately leading to enhanced academic outcomes. © 2023 IEEE.

关键词： Smart contract

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Comparing Artificial Neural Network and Decision Tree Algorithm to Predict Tides at Tanjung Priok Port

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Procedia computer science 2023年 227卷 406-414页

作者： Arif Musadi Christian Carlos Tertius Jonas Steven Hanis Amalia Saputri Kristien Margi Suryaningrum Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Tanjung Priok Port is an international port in Indonesia located in Tanjung Priok, North Jakarta. For all activities carried out at Tanjung Priok Port to run smoothly, this research was made which aims to predict the height of tides using the Artificial Neural Network (ANN) and Decision Tree methods with a quantitative approach. Artificial Neural Network (ANN) is a technique inspired by the way the biological nervous system works, namely in brain cells in processing information received by humans. while Decision Tree is also known as a decision tree which is an algorithm for building a decision hierarchy structure. The process of making a Decision Tree starts from the Root Node to the Leaf Node which is done recursively. This research was conducted to predict the height of tides in January 2018 - June 2018. By using both methods that have been computed, the ANN method produces a smaller MSE value than the Decision Tree method. The ANN method produces an MSE value of 0.003727983. While the Decision Tree method produces an MSE value of 0.009870259. If the dataset used has larger amount of data and the architecture of each algorithm is more complex, then the calculation results obtained will be more accurate.

关键词： Tidal prediction Artificial Neural networks Decision tree

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Role of Neural Network, Fuzzy, and IoT in Integrating Artificial Intelligence as a Cyber Security System 2

Role of Neural Network, Fuzzy, and IoT in Integrating Artifi...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Das, Papri Illa, Manikumari Pokhariyal, Rajesh Latoria, Akhilesh Hemlata Saini, DilipKumar Jang Bahadur Auro University School of Information Technology Gujarat Surat India India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Central University of Haryana Department of Computer Science and Engineering Haryana Mahendergarh India Sawmi Rama Himalayan University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350346640

The "Internet of Things"has a vast number interconnected devices. These interconnected devices collect vital data that may have a significant effect on the company, society, and the environment as a whole. IoT application has grown significantly in recent times, and with it, so do worries about cybersecurity. Artificial intelligence (AI) is at the forefront of the technology of cybersecurity and is employed to create intricate algorithms to safeguard systems and networks like IoT devices. But cybercriminals have learned how to take advantage of AI, and they have even started to deploy AI in analyzing cyberattacks. Due to the limited computing power and memory capacities of IoT systems, conventional high-end cybersecurity measures are inadequate to protect an IoT system. The need for accessible, distributed, and robust smart security systems is highlighted by this. Large- and small-scale heterogeneous datasets are no match for DL. In this research, a multilayer cybersecurity strategy based on DL is used to safeguard the TL of IoT systems. The developed framework tests the proposed multi-layer strategy using the intrusion identification statistics obtained from CIC-IDS (2018), ToN, and BoT-IoT. Consequently, depending on the analyzed parameters, the proposed model has outperformed the other approaches and achieved 98% accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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Advancing Dermatology: Deep Convolutional Neural Networks for Timely Hair Disease Diagnosis 3

Advancing Dermatology: Deep Convolutional Neural Networks fo...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Pant, Himanshu Joshi, Garima Pant, Janmejay Singh, Devendra Maurya, Sudhanshu Goyal, Himanshu Rai Graphic Era Hill University School of Computing Bhimtal India Kumaun University Department of Zoology Nainital India Computer Science & Engineering Symbosis Institute of Technology Pune India Graphic Era University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384598

Hair and scalp diseases represent a significant medical challenge, affecting millions worldwide. Early and accurate detection of these conditions is crucial for effective treatment and management. The proposed system integrates advanced DL algorithms with image processing methodologies to analyze digital images of the scalp and hair. By leveraging a diverse dataset of annotated images, the system employs supervised learning techniques to train models capable of recognizing patterns indicative of different hair and scalp diseases. Feature extraction, segmentation, and classification algorithms are applied to process and interpret image data, facilitating accurate diagnosis and classification of conditions such as alopecia, dandruff, psoriasis, and fungal infections. In this research, a deep learning technique was employed to accurately forecast ten major categories of hair loss and scalp disorders, including alopecia, psoriasis, and folliculitis. However, the study faced challenges due to limited prior research, a lack of suitable datasets, and the diverse nature of images available online. To address these challenges, 9600 images were used for training and 1200 for testing, with preprocessing steps such as denoising, image equalization, enhancement, and data balancing to minimize the error rate. The preprocessed data was then fed into a convolutional neural network (CNN) model, resulting in an overall training accuracy of 99.2% and a validation accuracy of 98.1%. © 2024 IEEE.

关键词： Dermatology

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The Essence of Perceived Compatibility and Behavioral Intention in Mobile Money Use

The Essence of Perceived Compatibility and Behavioral Intent...

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2024 International Conference on Advanced Information Scientific Development, ICAISD 2024

作者： Susanti, Leni Mohamad, Siti Hajar Savitri, Mila Andria Saputra, Karen Etania Alamsyah, Doni Purnama Setiadi, Ahmad Bina Nusantara University Binus Business School Entrepreneurship Department Jakarta Indonesia Management and Science University Department of Business Management and Law Selangor Malaysia Bina Nusantara University School of Design Interior Design Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Universitas Bina Sarana Informatika Faculty of Information Technology Software Engineering Department Jakarta Indonesia

ISBN: (纸本)9798350392036

Today, financial technology is a part of consumer needs. Mobile payments are used to support daily transactions, so a study of factors that can support behavioral intention and mobile money use is conducted. This study examines the support of digital literacy, perceived security, and perceived compatibility of mobile payments in increasing behavioral intention and mobile money use. The research was conducted on mobile payment users in Bandung City (Indonesia), and 416 research participants were asked using a questionnaire. The research was conducted using a quantitative survey and was processed using the SmartPLS tool. Two tests, namely, the PLS Algorithm and Bootstrapping, were used to test the research model. Next, the research hypothesis testing was used to explain the results. The research results found positive support from digital literacy, perceived security, and perceived compatibility on mobile payment behavioral intention. However, only perceived compatibility can directly increase the use of mobile money. In line with the hypothesis test, behavioral intention can support increased mobile money use by digital payment users. The research findings explain the ability of behavioral intention to mediate support for digital literacy, perceived security, and perceived compatibility in mobile money use. © 2024 IEEE.

关键词： Mobile commerce

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data-Efficient Reinforcement Learning with data Augmented Episodic Memory

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Procedia computer science 2023年 227卷 1120-1126页

作者： William Rusdyputra Andry Chowanda Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Reinforcement learning has been implemented to model a task by doing the task repeatedly to get the maximum results based on the reward and punishment policy. It has been implemented in the game and agent-based modelling. In the game, the game agent or Non-Players character can be modelled using several techniques to achieve the goal (e.g. reinforcement learning, deep neural network and Monte Carlo Tree Search). Deep neural networks and Monte Carlo Tree Search, two more sophisticated techniques in reinforcement learning algorithms, assisted the present reinforcement learning in resolving more challenging issues. However, this area has two challenges: the minimum number of data to model and generalization to different environments. Determining a minimum number of data required by the architecture to train the model is quite a cumbersome task to be applied to real- world jobs and situations since it demands substantial data to be explicitly provided and trial-and-error re-configuration. This work proposes a data-Efficient Reinforcement Learning model by augmenting the data and implementing episodic memory. To illustrate the effectiveness of the proposed model, this research compares it to several models, such as the Deep Q-Network (DQN) with episodic memory to the same model with data augmentation and episodic memory. The model adds to the observations before being stored in the agent's memory, causing the agent to use the same logic and take the same action in comparable situations. The outcome demonstrates that the augmented model can surpass the fundamental model in speed (with an improvement of 50% quicker).

关键词： Deep Reinforcement Learning Deep Learning Reinforcement Learning data Efficient

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Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

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Journal of Social Computing 2022年第4期3卷 322-344页

作者： Jaber Valinejad Zhen Guo Jin-Hee Cho Ing-Ray Chen the Data and System Science Lab Harvard Medical SchoolHarvard UniversityCambridgeMA 01451USA. Department of Computer Science Virginia TechNational Capital Region CampusFalls ChurchVA 22043USA.

The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social ***,it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful *** this end,various types of social sensing tools,such as tweeting and publicly released news,have been employed to understand individuals’and communities’thoughts,behaviors,and attitudes during the COVID-19 ***,some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like *** paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and *** use fact-checking organizations to classify news as real,mixed,or fake,and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience(CR).Based on the news articles and tweets collected,we quantify CR based on two key factors,community wellbeing and resource distribution,where resource distribution is assessed by the level of economic resilience and community *** on the estimates of these two factors,we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from *** improve the operationalization and sociological significance of this work,we use dimension reduction techniques to integrate the dimensions.

关键词： community resilience social computing data science fake news social media urban computing computational social science machine learning

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A Novel Approach for Effective Detection and Prediction of Sophisticated Cyber Attacks Using the Stacked Attention GRU and BiLSTM 1

A Novel Approach for Effective Detection and Prediction of S...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Shammi, L. Emilin Shyni, C. Presidency University Department of Computer Science and Engineering Karnataka Bengaluru India School of CS and IS Presidency University Karnataka Bengaluru India

ISBN: (纸本)9798350371802

The dynamic and sophisticated cyber threats of today's quickly expanding cybersecurity landscape are surpassing the effectiveness of traditional security solutions. Organizations must take a proactive stance in danger identification and mitigation if they are to successfully tackle these new difficulties. Threat intelligence (TI), or the real-time sharing of cyber threat data, is crucial for both proactive defense and quick reaction to cyberattacks. This study describes a comprehensive approach that includes feature selection, data preparation, and model training for threat intelligence-based cyber security. Normalization and standardization are two preprocessing methods that maximize data representation for efficient analysis. Principal component analysis is one of the feature selection techniques used to find the most pertinent variables for model training. A Stacked BiLSTM-A-GRU model is trained with careful respect to feature selection, guaranteeing optimal performance. A comparative examination shows that the suggested method outperforms the most advanced algorithms, with a remarkable accuracy rate of 98.71% This study emphasizes how crucial it is to use threat intelligence to have a strong cybersecurity defense against ever-changing threats. © 2024 IEEE.

关键词： Cybersecurity

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Estimating Chlorophyll Content of Vegetation in Hyper Spectral Surface Images 5th

Estimating Chlorophyll Content of Vegetation in Hyper Spectr...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kannagi, A. Adhikari, Manoj S. Manjula, H.M. Agarwal, Trapty Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The Estimating Chlorophyll content material of vegetation in Hyper Spectral floor photos aims to create an automatic approach to accurately degree the concentration of chlorophyll in plant life using hyperspectral floor pix. This studies makes use of spectral evaluation and feature extraction techniques, mixed with machine studying techniques, to discover and quantify the awareness of chlorophyll from photosynthetic plants. The experimental results show that the approach has an amazing capability to discover plant life chlorophyll concentrations. This approach has correct accuracy and is beneficial inside the subject of precision agriculture for tracking the fertilizer software and nutrient imbalance of crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Concentrations Evaluation Photosynthetic

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