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A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

Computers, Materials & Continua 2023年第7期76卷 1173-1193页

作者： Amjed Al Fahoum Ala’a Zyout Hiam Alquran Isam Abu-Qasmieh Biomedical Systems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Complex proteins are needed for many biological *** amino acid chains reveals their properties and *** support healthy tissue structure,physiology,and *** medicine and treatments require quantitative protein identification and *** technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains *** function inference from amino acid sequences is the main biological data *** study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family *** and validation employed 70%of the dataset,while 30%was used for *** paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all *** initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning *** single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%*** two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%*** work provides balanced,reliable,and precise forecasts for all families in all measures.

关键词： Globin-like superfamily numerical encoding bispectral analysis classification model deep convolutional neural network

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Nugroho, Yusuf Sulistyo Halim, Salsabil Aisyah Abdul Islam, Syful Kurniawan, Yogiek Indra Erlina, Tati Universitas Muhammadiyah Surakarta Informatics Engineering Faculty of Communication and Informatics Surakarta Indonesia BSMRSTU Computer Science and Engineering Faculty of Engineering Gopalganj Bangladesh Universitas Jenderal Soedirman Informatics Engineering Faculty of Engineering Purwokerto Indonesia Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350376210

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users (72.52%), followed by mid-reputation users (17.93%), and high-reputation users (9.5 5%). The distribution of the unanswered question types shows that low-reputation users ask more 'how' type of question (38.90%) followed by mid and high-reputation users. In contrast, the 'what' and 'why' questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement. © 2024 IEEE.

关键词： Python

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Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (Science and Information Organization). All rights reserved.

关键词： Logistic regression

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A Recommendation System for Double Track Program Skill Selection Using the Analytic Hierarchy Process (AHP) 10

A Recommendation System for Double Track Program Skill Selec...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Bughowi, Mohamad Kholid Sunaryono, Dwi Baskoro, Fajar Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9798331521295

The Double Track Program prepares high school students for careers or entrepreneurship through skills training, aiming to reduce post-graduation unemployment in East Java. It offers seven skill areas: multimedia, culinary arts, fashion design, beauty treatment, electrical engineering, electrical technology, and light vehicle engineering. Students choose one skill area for a year. However, mismatched expectations can lead to wasted time, poor outcomes, and frustration. To address this, a web-based recommendation system was developed to suggest skill areas based on criteria such as job availability, salary, interests, facilities, and entrepreneurial opportunities. The system employs the Analytic Hierarchy Process (AHP) method to support decision-making. It is implemented as a website using TypeScript, Go, the *** framework, and PostgreSQL for data storage. Testing with 77 eleventh-grade students from Dharma Wanita Surabaya High School showed that the top three recommendations achieved an average accuracy of 77.3036%, while the top five reached 77.5510%. Overall, the system demonstrated an average accuracy of 78.4521%, proving effective in guiding students toward skill areas that align with their goals. © 2024 IEEE.

关键词： Decision making

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Lies Uncovered: Comparing the Performance of Deep Learning Techniques in Video-Based Deception Detection 24

Lies Uncovered: Comparing the Performance of Deep Learning T...

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2nd International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2024

作者： Yeni, Rahayu Dwi Fatichah, Chastine Yuniarti, Anny Department of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muhammadiyah Jember Indonesia

ISBN: (纸本)9798400716898

The discovery of effective and efficient deception detection techniques in video data has significant implications in various fields such as psychology and law enforcement. With the advancement of computer technology, particularly in the field of deep learning, this research aims to improve the accuracy and efficiency of deception detection in video data. Although previous research has investigated deep learning techniques for deception detection in video data, there is still a need to overcome limitations in the use of real-world datasets, generalization of models, and consideration of their efficiency and effectiveness in practical applications. This research investigated seven deep learning models, including 1D CNN, 2D CNN, 3D CNN, RNN, LSTM, GRU, and Transformer Self Attention, for deception detection in video data. The experimental data comes from real case videos that have been labelled as honest or not based on court decisions. This dataset consisted of 121 videos, of which 61 were labelled as deceptive and 60 as honest. Each video contains 1600-2400 frames, with a total of 241504 data entries processed. It was found that 1-D CNNs were the most promising for detecting deceptive video data, with an accuracy of 0.99 and a testing time of 4.63 seconds, providing a good combination of high accuracy and time effectiveness. The GRU and LSTM also show strong potential, particularly in applications where the trade-off between speed and accuracy is critical. Future research should explore modifying this deep learning method to better suit the context of deception detection by recognizing each frame as a part of a unified video. © 2024 ACM.

关键词： Video analysis

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Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data 24

Vision Transformer for Audio-Based Depression Detection on M...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Pratiwi, Monica Sanjaya, Samuel Ady Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798400709586

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed depression can result in deteriorating mental well-being, which includes a higher risk of experiencing suicidal ideation and engaging in self-destructive actions. Voice analysis can be employed to distinguish between individuals with depression and those without depression. However, the research that has been conducted on voice recognition to distinguish between depressed and non-depressed individuals keeps focusing on the use of a single dataset source as input for the classification model that is being developed. This work utilizes the Vision Transformer model and various pre-trained transformers, such as DeiT (Data-Efficient Image Transformer) and Swin Transformer, to detect depression based on voice. The objective was to create a more generic model not restricted to a specific language. Integrating Mel-spectrogram characteristics with a vision transformer-based model can enhance the efficacy of voice recognition models when dealing with multi-language data. The result shows 21% higher accuracy than the previous study that also implemented a cross-dataset test. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spectrographs

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A Self-adapting Pixel Antenna - Substrate Lens System for Infrared Frequencies

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 14-25页

作者： Shubbar, Mustafa Rakos, Balázs Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

In this work, we propose a concentrator lens - antenna arrangement, potentially suitable for infrared sensing and energy harvesting rectenna systems. The structure consists of a pixel antenna, self-adapting for the direction of the incident infrared radiation, and a concentrator lens, both optimized for the mid-infrared spectrum. The silicon substrate lens is situated above the pixel antenna, enabling the concentration of the incident light on the antenna;silicon was chosen due to its transparency in the IR spectrum. We examine how various parameters of the lens, in conjunction with the different states of the pixel antenna, affects the performance of the system. The simulations show that the gain of the arrangement increases considerably in correlation with the radius of the lens. The results suggest that the energy conversion efficiency of infrared rectenna systems can be enhanced by several orders of magnitude with the utilization of the proposed arrangement. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Rectennas

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Signature Verification Based on Dex CRC and Blake2 Algorithm to Prevent Reverse engineering Attack in Android Application

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 112-122页

作者： Ilham Niswar, Muhammad Paundu, Ady Wahyudi Department of Informatics Faculty of Engineering Universitas Hasanuddin Gowa South Sulawesi Indonesia

The rapid growth of Android applications has led to more cybercrime cases, specifically Reverse engineering attacks, on Android apps. One of the most common cases of reverse engineering is application repackaging, where the application is downloaded via the Play Store or the official website and then repackaged with various additions or changes. One of the ways to avoid Application Repackaging attacks is to check the signature of an application. However, hackers can manipulate the application by adding a hook, i.e., replacing the original function for getting signatures with a new modified function in the application. In this research, the development of a verification method for Android applications is carried out by utilizing Dex CRC and the Blake2 algorithm, which will be written in C using the Java Native Interface (JNI). The results of this study indicate that the verification method using Dex CRC and the Blake2 algorithm can effectively protect Android applications from Application Repackaging attacks without burdening application performance. © 2023 by the authors of this article. Published under CC-BY.

关键词： Reverse engineering

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Semi-supervised learning for on-street parking violation prediction using graph convolutional networks

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Neural Computing and Applications 2024年第31期36卷 19643-19652页

作者： Karantaglis, Nikolaos Passalis, Nikolaos Tefas, Anastasios Group AIIA Lab Department of Informatics Faculty of Science Aristotle University of Thessaloniki Thessaloniki Greece Department of Chemical Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Controlled parking systems in cities provide designated parking zones and allow citizens to easily find parking spaces increasing comfort and potentially reducing traffic and pollution. However, illegally occupied parking spaces can negatively affect the accuracy of these systems, diminishing their benefits. This work presents a semi-supervised deep learning approach for fine-grained parking violation rate prediction using graph neural networks, which can capture both the spatial and temporal dynamics of parking systems and driver behavior, providing a valuable tool for controlled parking systems. The proposed method addresses several challenges faced in this task, including the need to appropriately construct temporal graph-based training datasets and design a model that can handle missing values. Additionally, the method includes a novel semi-supervised data augmentation and smoothing technique to mitigate the effect of the small amount of annotated data and/or missing data in the training dataset. The proposed method was evaluated using a large-scale dataset from Thessaloniki’s on-street public parking system and found to significantly improve the accuracy of parking violation prediction compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Tempe Manufacturer's Feasibility System for its Ingredients and Production Process using the K-Nearest Neighbor (KNN) Method 15

Tempe Manufacturer's Feasibility System for its Ingredients ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Bororing, Jemmy Edwin Yumarlin, M.Z. Nabilla, Shalsa Rahayu, Sri Department of Informatics Faculty of Engineering Universitas Janabadra Indonesia

ISBN: (纸本)9798331300579

Technological developments have caused changes in almost all sectors of human life, including the economy. Technology can be applied to the tempe producing industry by maintaining the quality of the tempe. So an expert system is needed that can help determine the quality of the tempe produced. Using the K-Nearest Neighbor (KNN) algorithm with the application of the Case Based Reasoning (CBR) method in the material feasibility system and tempe production process to determine the percentage value based on predetermined symptoms. The system can provide percentage results of eligibility based on the assessment carried out. System testing uses functional and non-functional methods. Based on the system testing results on 5 respondents, the results of non-functional testing on the usability aspect obtained a score percentage of 82.67%, the ease of use aspect 85.33%, the ease of learning aspect 78.67%, and the satisfaction aspect 73.67%. From these four aspects, an average score of 80% was obtained, which means the system runs well and is suitable for use. © Grenze Scientific Society, 2024.

关键词： Case based reasoning

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