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12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023

作者： Gonen, Serkan Karacayilmaz, Gokce Artuner, Harun Bariskan, Mehmet Ali Yilmaz, Ercan Nurcan Software Engineering Department Istanbul Gelisim University Istanbul Turkey Hacettepe University Institute of Science Ankara Turkey Computer Engineering Department Hacattepe University Ankara Turkey Computer Engineering Department Istanbul Gelisim University Istanbul Turkey Electrical – Electronic Engineering Department Gazi University Ankara Turkey

ISBN: (纸本)9789819960613

The evolution of science and technology has led to increasingly complex cyber security threats, with advanced evasion techniques and encrypted communication channels making attacks harder to detect. While encryption has improved privacy and confidentiality for users, it has also provided a new avenue for attackers to exploit. Traditional intrusion detection systems, which transitioned from signature-based to behavior-based approaches, have struggled to keep up with these challenges. To address this issue, researchers have turned to continuous system monitoring and network traffic packet analysis. However, this method can be resource-intensive and time-consuming, particularly when analyzing encrypted packets. In this study, the JA3 fingerprint infrastructure was examined as a potential solution for quickly detecting attacks conducted over encrypted sessions while minimizing system downtime and damage. The results demonstrated that the JA3 infrastructure effectively detected attacks carried out via encrypted channels. Although Windows 10 and Kali 2020.4 operating systems were used as the victim and attacker systems respectively, the methodology can be applied to other operating systems and network hardware by following the outlined steps. This research is expected to make a significant contribution to the field of encryption-based attack prevention. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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computers, Materials & Continua 2024年第7期80卷 341-359页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Daniyar Sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

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Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan Khalil Ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Use of Large Language Models for Medical Synthetic Data Generation in Mental Illness 7

Use of Large Language Models for Medical Synthetic Data Gene...

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7th IET Smart Cities Symposium, SCS 2023

作者： Aygün, İrfan Kaya, Mehmet Department of Software Engineering Celal Bayar University Manisa Turkey Department of Computer Engineering Fırat University Elazığ Turkey

ISBN: (纸本)9781839539831

Data quantity and quality are very important for the development of medical artificial intelligence research. Nowadays, thanks to easier access to data, studies in this field produce very successful results. However, many factors such as protection of patient rights in medical data and confidentiality of personal data prevent researchers from directly accessing the data. For this reason, synthetic data creation studies are often needed both to expand the training and test sets and to create sample cases to be used in the relevant field. In this study, various synthetic patient data are created to be presented to a language model that enables the detection of psychological disorders through patient text. Synthetic data sets were produced with 200 artificial patient data created with popular LLM examples ChatGPT and Google Bard. The quality of synthetic data was measured with the help of a pre-trained BERT model using these datasets. In the experiments, it was observed that chatbots that generate instant data, such as ChatGPT and Google Bard, produced successful results at rates of 89% and 86% with the language representation model. With the experimental results, it appears that LLM studies can provide more successful results than advanced language models in various medical text production tasks. © The Institution of engineering & Technology 2023.

关键词： Hospital data processing

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Self-Adaptive Genetic Algorithm For Permutation Flow Shop Scheduling Problems 10

Self-Adaptive Genetic Algorithm For Permutation Flow Shop Sc...

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10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Çaliskan, Cihanser Erdogdu, Kazim Graduate School Yasar University Department of Computer Engineering Izmir Turkey Yasar University Department of Software Engineering Izmir Turkey

ISBN: (纸本)9798350304299

The permutation flow shop scheduling problem (PFSSP) is a well-known, extensively researched, and heavily applied non-polynomial (NP)-Hard combinatorial optimization problem. It is encountered in various real-life manufacturing problems such as automotive manufacturing, integrated circuit fabrication, and agricultural food industries. It continues to gain popularity in operational research areas as new manufacturing areas are developed. Therefore, finding a solution to these NP-Hard problems attract the attention of scientists. In this paper, we studied a PFSSP and proposed a new heuristic for its solution: The Self-Adaptive Genetic Algorithm (GA). This proposed algorithm uses a conventional GA with cycle crossover and random swap mutation. Its novelty, on the other hand, lies in incorporating an adaptive mechanism in the GA. The proposed algorithm uses three different local searches (i.e. 2-Opt, Greedy Insert, and Greedy Swap local searches) based on their successes. In other words, the proposed algorithm evaluates the performance of each local search at each generational iteration and makes a decision on which one to use based on their previous performances. The more successful local searches increase their probability of selection and vice versa. This way, Self-Adaptive GA, hence the name, adapts and directs its exploitation by the information it obtains in its previous generations. The proposed algorithm was applied to a subset of well-known Taillard problem instances. The experimental studies show its successful performance. Self-Adaptive GA obtained the optimum results for 7 out of 18 benchmark instances. In the rest of the 11 instances, the differences between the results of the proposed method and the optimum values are less than 2%. © 2023 IEEE.

关键词： Genetic algorithms

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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Evaluating machine learning and statistical learning techniques for cancer classification and diagnosis

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Iran Journal of computer Science 2025年 1-20页

作者： Alwazy, Asmaa Salim Hussaien Buyrukoğlu, Gonca Buyrukoğlu, Selim Baker, Mohammed Rashad Department of Electronics and Computer Engineering Çankırı Karatekin University Çankırı 18100 Türkiye Department of Statistics Çankırı Karatekin University Çankırı 18100 Türkiye Department of Computer Engineering Çankırı Karatekin University Çankırı 18100 Türkiye Department of Software Engineering University of Kirkuk Kirkuk Iraq

Accurate cancer diagnosis is critical for effective treatment and positive patient outcomes. This study investigates the robustness of machine learning (ML) and statistical learning (SL) algorithms in classifying and diagnosing lung, prostate, breast, and heart disease. We evaluated and compared the performance of several algorithms, including support vector classifier (SVC), random forest (RF), XGBoost, decision tree (DT), elastic net, Lasso, and ridge, using four medical data sets. Multiple metrics, such as accuracy, precision, recall, F1-score, and area under the curve (AUC), are used to assess the performance. Hyperparameter tuning is conducted using GridSearchCV. The results demonstrated that RF and SVC achieved near-perfect accuracy (up to 98%) and AUC scores (1.00) in distinguishing between benign and malignant lung and breast cancer samples. SL algorithms exhibited robust and consistent performance for prostate cancer, achieving accuracy, precision, recall, and F1-scores of approximately 90%, making them suitable for data sets with smaller sample sizes. However, SVC-based ML algorithms outperformed SL methods in overall accuracy for prostate cancer, although SL methods provided a stable baseline. For heart disease, SVC achieved the highest accuracy (82%), while regularized SL models such as Elastic Net and Lasso performed similarly, with an accuracy of 80%. This study highlights the importance of selecting algorithms tailored to the data set’s characteristics and the specific diagnostic requirements. It also underscores the complementary strengths of ML and SL approaches, suggesting that an integrated strategy may offer the most effective solution. The findings contribute to the growing evidence supporting integrating computational methods into clinical practice to enhance diagnostic accuracy and improve patient care. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cancer classification Data mining Diagnosis Machine learning algorithms Statistical learning algorithms

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Internet of Things (IoT): survey of most important security risks 3

Internet of Things (IoT): survey of most important security ...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Alaskri, Ahmed Salem Ahmed, Nuredin Ali Shaari, Hala Collage of Computer Technology Software Engineering Dep. Tripoli Libya Faculty of Engineering Computer Engineering Department Tripoli Libya Faculty of Information Web Technologies Department Tripoli Libya

ISBN: (纸本)9798350319897

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field. © 2023 IEEE.

关键词： Internet of things

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