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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Vincent Saputra, Benecia Jean Izzatunnisa, Salsabila Yuka Lucky, Henry Iswanto, Irene Anindaputri School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

The stock market is a current significant trend in modern life who seek to improve their wealth through investing, thus accurate stock market predictions are essential to maximize gains and minimize losses. This research focuses on enhancing stock market prediction performance using a combination of several technical indicators and artificial neural network technology, specifically Long Short-Term Memory (LSTM). Technical indicators used include Moving Average Convergence Divergence (MACD), Bollinger Bands, Relative Volume (RVOL), Williams %R, Chaikin Money Flow (CMF), Detrended Price Oscillator, Stochastic Oscillator, Volume Weighted Average Price (VWAP), Relative Strength Index (RSI), Moving Average (MA), and TRIX. Each technical indicator generates buy or sell signals that collectively determine whether a stock is worth buying, selling, or holding. Historical stock price data is collected and preprocessed, followed by training and testing the LSTM model with accuracy metrics to evaluate the performance. Using appl dataset from Kaggle, the training and testing generated RMSE values of 0.004 and 0.04 respectively, whereas the standard deviation is 0.04 and 0.16. These results demonstrate high accuracy which corresponds to our expectations given that a threshold of 5 or below represents the minority and a threshold of 6 or above enters the majority voting region. In the future, we plan to incorporate more variables into the LSTM model to achieve greater accuracy and use different stocks to test our findings. © 2023 IEEE.

关键词： Long short-term memory

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Development of Weather Forecast Application Using API 1

Development of Weather Forecast Application Using API

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Chadha, Shikha Ahmad, Salman Khursheed Sharma, Dolly Singh, Manu Srivastavaline, Anubhava Sharda University Department of Computer Science Greater Noida India School Of Computer Science & Engineering Galgotias University Department Of Computer Science Engineering Greater Noida India Graphic Era Hill University School of Computing Department Haldwani India ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

This research paper presents the development of a weather forecasting model that incorporates real-time data through Application programming Interfaces. This model utilises simple algorithms to analyse meteorological parameters from diverse API data sources, aiming to improve forecast accuracy compared to traditional methods. Extensive datasets are used for training and validation, demonstrating the model's effectiveness in providing more precise predictions. The study highlights the significance of API integration in advancing weather forecasting methodologies and fostering innovation in the field. The model created has yielded highly reliable results with minimal error. © 2024 IEEE.

关键词： Prediction models

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GAN.s Approach On Generating Synthetical Skin Image dataset for Imbalance dataset 8

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Setiawan, Tri Agus Heryadi, Yaya Lukas Wulandari, Lili Ayu Sonata, Ilvico Binus Graduate Program -Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350368970

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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computers, Materials & Continua 2023年第4期75卷 149-164页

作者： K.Kalyani Sara A Althubiti Mohammed Altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

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Abstractive Text Summarization Using BERT for Feature Extraction and Seq2Seq Model for Summary Generation

Abstractive Text Summarization Using BERT for Feature Extrac...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Widowati, Kania Galih Budiman, Nicholas Foejiono, Kezia Purwandari, Kartika School of Computer Science Bina Nusantara University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350369359

Humans are very easy to access various existing data about text using search engines and text generate applications. Text summarization is a technological development of the Natural Language Processing branch which takes points and main information from a paragraph. This is done to increase efficiency in understanding a paragraph or document. In this study, we used the type of abstractive text summarization, which is a summarization resulting from deeper sentence understanding. That the resulting summarization will produce a new summary that is not always in the input text, so this will increase the reader's understanding. In text summarization, various methods have been used such as Support Vector Machine (SVM), Long Short Term Memory (LSTM), Bayesian Networks, and so on. In this study, we used pre-trained model Bidirectional Encoder Representations (BERT) derived from transformers, and Sequence to Sequence (Seq2Seq) and the dataset used was the CNN Daily Mail dataset. The pre-trained BERT model is used to perform feature extraction, the results will be forwarded by Seq2Seq for summarization. We also conduct evaluations using ROUGE, by comparing the results of the summary model with the results of the summary made by humans. ROUGE's evaluation results show that typing accuracy in shorter articles is not significantly different from that in longer articles. © 2023 IEEE.

关键词： Feature extraction

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Exploring the Performance of BERT Models for Multi-Label Hate Speech Detection on Indonesian Twitter 4

Exploring the Performance of BERT Models for Multi-Label Hat...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Mahardika, Muhammad Razi Wijaya, I Putu Janardana Prayoga, Arvin Rayhandi Lucky, Henry Iswanto, Irene Anindaputri School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Due to its widespread distribution and the anonymity it offers users, hate speech on social media platforms, particularly Twitter, is a major problem. Because of Twitter's echo chamber algorithm and viral nature, hate speech can spread quickly and lead to societal unrest. The use of BERT-based models for hate speech identification in the Indonesian language has been studied in previous research. This study compares how well several pre-trained BERT models, IndoBERT and mBERT, perform in identifying hate speech on Twitter. The methodology includes gathering datasets, preparing the data, and utilizing the proper metrics to assess the models. IndoBERT successfully outperforms mBERT model according to the average of each evaluation metric shown. By carrying out this study, we hope to advance knowledge of techniques for identifying hate speech on Indonesian social media platforms and enhance the use of BERT models for such analysis. © 2023 IEEE.

关键词： Speech recognition

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Recommendation System Based Collaborative Filtering for Deciding Travelling Place 4

Recommendation System Based Collaborative Filtering for Deci...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Oktavius, Anthony Kevin Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

This Indonesia has a vast number of tourist destinations, and it can be overwhelming for people to decide where to go, either because they have many options to choose from or because they are bored with places they have visited before. To address this issue, this research uses collaborative filtering-based recommendation systems to provide users with personalized recommendations based on their previous ratings. The research will compare five recommendation system algorithms: the Hybrid Algorithm with ContentKNN (Content K Nearest Neighbors) and RBM (Restricted Boltzmann Machine), SVD++ (Singular Value Decomposition++), Efficient Deep Learning, Scalable Deep Learning, and Naïve Bayes. These algorithms will be tested based on their evaluation metrics, namely RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error). The model that has the best evaluation metrics will be selected to provide travelling place recommendations. To develop algorithm models for the recommendation system, the research will use the CRISP-DM (Cross-Industry Standard Process for data Mining) model. Based on the workflow, Efficient Deep Learning has the best RMSE and MAE value from all of the models, with these following parameters: 250 neurons and SELU (Scaled Exponential Linear Unit) activation type. Future works for this research includes additional rating dataset, adding different combinations of fields, distance metrics, and other evaluation metrics. © 2023 IEEE.

关键词： Recommender systems

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