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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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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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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Predicting the Trend of Indonesian Minimum Regional Income Using Statistical and Deep Learning Approaches

Predicting the Trend of Indonesian Minimum Regional Income U...

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2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Reidolvo Tjan, Francis Nicolas Candika, Jedi Hardine Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Analyzing economic growth indicators is crucial in determining a country's economy in the future. One key indicator is minimum wage, in which this paper examined further by using both statistical methods and machine learning methods to forecast the indicator's future values. The data used is from Indonesian Central Bureau of Statistics' public website. Utilizing and comparing between four models: Linear Regression Model (LR), Autoregressive Integrated Moving Average Model (ARIMA), Deep Learning Model with Long Short-term Memory (LSTM), and Deep Learning Model with Gated Recurrent Unit (GRU), the paper concluded that GRU had the best result upon evaluation through finding all the models' Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2 score), in which GRU had values of 122,701.02, 104,442.75, and 0.3777 respectively. Prediction by GRU showed that Indonesian minimum wage would start to decrease and become stagnant in the 2023-2027 duration. This prediction however could potentially be false due to the GRU model having low R2 score, thus indicating the model had low predictive ability. Further research would be needed to explore larger datasets, utilize more economic indicators, and trying other models. © 2023 IEEE.

关键词： Long short-term memory

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Crude Oil Price Forecasting: A Comparative Analysis of ARIMA, GRU, and LSTM Models 9

Crude Oil Price Forecasting: A Comparative Analysis of ARIMA...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Rusman, Justin Arola Chunady, Kevin Makmud, Singgih Tulus Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

Since 2020, the world hasn't been able to recover fully from the economic crisis that is caused by the pandemic and the ongoing war between Russia and Ukraine. Crude oil is one of the key ingredients that shapes the world's economy as of today. This research paper is aimed to predict uncertainty and volatility of crude oil's prices with time series approaches. To predict crude oil prices, we need to implement Machine Learning and Deep Learning models such as RNN models (LSTM, Bi-LSTM, GRU, and Multi-LSTM) and ARIMA models. To improve the accuracy of the models, we need to preprocess certain data. Picking a variable to be processed is also necessary, in this case we used opening price and date to determine crude oil prices going forward. data scaling is necessary to maintain numeric data and Ad fuller Test is a must to acknowledge data stationarity. After preprocessing and fitting the data into models, we can see that LSTM and ARIMA stood out within other models, LSTM has an RMSE score of 6.62 and MAE of 4.96 whilst ARIMA has an RMSE score of 6.5 and MAE of 5.03. All the models used are good in predicting crude oil prices, because the R2-Score is ideal, not overfitting and underfitting. This approach on predicting crude oil prices will certainly be impactful in the global economy and avoiding panic bull and bear markets of the crude oil price. © 2023 IEEE.

关键词： Forecasting

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Enhanced Identification of Care Preference Documentation in Patients’ Discharge Summaries Using Pre-trained Large Language Models 1

Enhanced Identification of Care Preference Documentation in ...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Arora, Saksham Khayal, Inas S. Department of Computer Science Dartmouth College HanoverNH United States Dartmouth Institute & Biomedical Data Science Geisel School of Medicine at Dartmouth Dept. of Computer Science Dartmouth College HanoverNH United States

ISBN: (纸本)9798350371987

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than the more appropriately broad population of ‘seriously ill’ patients. This study addresses this gap, first by matching decedent patients to non-decedent counterparts by using propensity score matching, accounting for important variables like age, gender, primary diagnoses, and patient co-morbidities, and then by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods. We trained and fine-tuned Bio_ClinicalBERT and ClinicalLongformer models on a large dataset of patient discharge summaries, enhancing their signal-to-noise ratio by focusing on key textual aspects, thereby capturing contextually nuanced mentions of care preference documentation. These models demonstrated high sensitivity and specificity compared to industry-standard keyword-search methods, proving adept at interpreting complex clinical concepts. This study highlights the potential of transformer-based models specifically trained for clinical domain tasks. Our findings not only contribute to enhancing end-of-life communication and aligning treatment with patients’ care objectives but also pave the way for future research in this promising domain, with potential implications for improving patient care quality. ©2024 IEEE.

关键词： Large datasets

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Harnessing Large Language Models for Text-Rich Sequential Recommendation 24

Harnessing Large Language Models for Text-Rich Sequential Re...

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33rd ACM Web Conference, WWW 2024

作者： Zheng, Zhi Chao, WenShuo Qiu, Zhaopeng Zhu, Hengshu Xiong, Hui School of Data Science University of Science and Technology of China China Career Science Lab BOSS Zhipin China China China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400701719

Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior sequence. This poses significant challenges to LLM-based recommenders, such as over-length limitations, extensive time and space overheads, and suboptimal model performance. To this end, in this paper, we design a novel framework for harnessing Large Language Models for Text-Rich Sequential Recommendation (LLM-TRSR). Specifically, we first propose to segment the user historical behaviors and subsequently employ an LLM-based summarizer for summarizing these user behavior blocks. Particularly, drawing inspiration from the successful application of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) models in user modeling, we introduce two unique summarization techniques in this paper, respectively hierarchical summarization and recurrent summarization. Then, we construct a prompt text encompassing the user preference summary, recent user interactions, and candidate item information into an LLM-based recommender, which is subsequently fine-tuned using Supervised Fine-Tuning (SFT) techniques to yield our final recommendation model. We also use Low-Rank Adaptation (LoRA) for Parameter-Efficient Fine-Tuning (PEFT). We conduct experiments on two public datasets, and the results clearly demonstrate the effectiveness of our approach. © 2024 ACM.

关键词： Recommender systems

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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 Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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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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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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