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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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Automatic Extractive text Summarization for Ho Language 21

Automatic Extractive text Summarization for Ho Language

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21st OITS International Conference on Information technology, OCIT 2023

作者： Bankira, Dula Panda, Srinibas Ranjan, Satya Ali, Hassen Said Parida, Shantipriya Walubita, Ngula KISS University School of Indigenous Knowledge Science and Technology India KIIT University Dash School of Computer Applications Bhubaneswar India KIIT University School of Computer Applications India SILO AI Finland Mukuni Secondary School Livingstone Zambia

ISBN: (纸本)9798350358230

Automatic text summarization is a method to shorten lengthy texts with important and understandable selected sentences using machine learning techniques. It is a part of Natural Language Processing (NLP) that uses machine learning techniques to generate accurate and non-redundant text. Manually, summarizing lengthy text is challenging for humans. So, it is required automatic text summarization. Automatic text summarization techniques reduce time and effort by automating the process of generating summaries. There are two types of text summarization for summarizing the data i.e. extractive and abstractive summarization techniques. In this paper, we have proposed extractive summarization for the Ho language, the first study of its kind. The Ho is a tribe as well as the language of India. For generating extractive summarization, we have used thirty 'Ho' language stories written in Warang Chiti Script. The stories were summarized using TF-IDF and TextRank algorithm and the generated summaries were manually evaluated on different parameters. After obtaining both machine and human-generated summaries, we compared both texts with the help of human experts. The average f1 score of TextRank is 15.17 and the average f1 score of TF-IDF is 17.56. The TF-IDF result is better than the TextRank result. © 2023 IEEE.

关键词： Learning algorithms

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Role of Artificial Intelligence (AI) to Enhance the Security and Privacy of Data in Smart Cities 3

Role of Artificial Intelligence (AI) to Enhance the Security...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Sharma, Vikas Kumar, Sunil IIMT University School of Computer Science and Applications Meerut India AURO University School of Information Technology Surat India

ISBN: (纸本)9798350399264

Because smart cities are complex and interdependent, managing them involves dealing with political, technological, and socioeconomic challenges for many organizations. Since a few years back, a lot of literary works have concentrated on privacy, dangers, security and problems with the architecture of smart cities. These all factors mainly concern data processing and its management. This research primarily focuses on important difficulties, offers a thorough analysis of the literature, and makes recommendations for creating a framework for smart cities that primarily highlights the security and privacy elements. The infrastructure needed for smart cities, the protocols used to advance security and privacy, operational dangers to smart cities, and the best use of social media are only a few of the important topics covered in this study. It also offers a helpful perspective on many important issues and suggests a course for future research. The lessons learned might provide academics and researchers with a beneficial research foundation and a point of reference. © 2023 IEEE.

关键词： Smart city

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Multibranch Co-training to Mine Venomous Feature Representation: A Solution to SnakeCLEF2024 25

Multibranch Co-training to Mine Venomous Feature Representat...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Wang, Peng Li, Yangyang Tan, Bao-Feng Zhou, Yi-Chao Li, Yong Wei, Xiu-Shen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

The SnakeCLEF2024 competition aims to develop an advanced algorithm capable of automatically identifying snake species from images. Accurate identification of snake species in snakebite cases can assist doctors in administering targeted antivenom, which is crucial for effective treatment. In this paper, we propose a multibranch co-training strategy based on Convolutional Neural Networks (CNNs) as the solution. During the training phase, our method consists of three branches which can be trained end-to-end. The first branch is used for the classification of all species and generates a gating coefficient. The second branch specifically focuses on venomous snakes, while the third branch concentrates on harmless species. The gating coefficient determines which of these branches will be utilized. During the inference phase, we only retain the first branch. Our solution significantly enhances the model's ability to distinguish between venomous and harmless snake species and achieve an accuracy of 69.83% and scored 83.57% on the track1 on the private leaderboard, which is the 1st place among all participants. The code is available at https://***/pengdadaaa/SnakeCLEF2024. © 2024 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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Say No to the Poisonous Fungi: An Effective Strategy for Reducing 0-1 Cost in FungiCLEF2024 25

Say No to the Poisonous Fungi: An Effective Strategy for Red...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Tan, Bao-Feng Li, Yang-Yang Wang, Peng Zhao, Lin Wei, Xiu-Shen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

The FungiCLEF2024 competition endeavors to precisely identify fungi species leveraging both metadata and image analysis. Pivotal to the success of this competition are two crucial evaluation metrics: minimizing the error rate and the 0-1 cost loss resulting from misclassification. To reduce the identification error rate, we introduce a Dynamic MLP framework, drawing inspiration from [1]. This approach effectively integrates image and metadata embeddings through recursive blocks, utilizing matrix multiplication for deep fusion of information. To further address the issue of 0-1 cost, we devise a novel probability-based screening strategy, which initially consolidates poisonous fungi categories into a single class, then employs marginal expected loss and a threshold parameter α to optimize the recall rate for poisonous species. These approaches significantly reduce the error rate and 0-1 cost associated with misclassification and achieve a score of 0.5548 on the private leaderboard, securing the third-place ranking. The code is available at https://***/bftan1949/FungiCLEF2024. © 2024 Copyright for this paper by its authors.

关键词： 0-1 cost loss Fine-grained image recognition Fungi Species Identification Open-Set

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An ensemble framework of decision trees for class imbalance using partitioning

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International Journal of Innovative Computing and applications 2024年第1期15卷 1-13页

作者： Kadappa, Vijayakumar Guggari, Shankru Veerabhaskar, Rajeshwari Devi Doddapoojari Department of Computer Applications B.M.S. College of Engineering Bengaluru560019 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India Department of Electronics and Communication Engineering Global Academy of Technology Bengaluru560098 India

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

Language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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GIS and ML Integrated Techniques for Detection of Landslide-Prone Areas 2

GIS and ML Integrated Techniques for Detection of Landslide-...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sul, Astha Patil, Sanjay Vellore Institute of Technology School of Computer Science and Engineering Vellore India Maharashtra Remote Sensing Applications Centre Maharashtra Pune India

ISBN: (纸本)9798350368413

Landslides are a common natural disaster that can result in substantial casualties. One of the region's most vulnerable to landslides in India is Western Ghats. The risk casualty is reduced with the use of the landslide susceptibility map (LSM) and prediction of landslide points. The aim of this research is to explore and compare the 4 ML algorithms (K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)) for generating the most promising LSM. This study focuses on 12 Landslide conditioning factors (LCFs) such as Elevation, Slope, Lithology, LULC, Distance to Road, Topographic Wetness Index (TWI), Stream Power Index (SPI), Topographic Roughness Index (TRI), Drainage Networks, NDVI, Rainfall, Sediment Transport Index (STI). It has been found that the slope of the ground, the distance to the road, and the annual rainfall all have a significant impact on the occurrence of landslides. The models made using different approaches have been analysed and compared using the following metrics: prediction accuracy, precision, recall, F1-score, and True Skill Statistics (TSS). Overall accuracy for LR, SVM, KNN, RF are 82.30, 84.00, 82.50, 87.20 respectively. The findings of the study show that there are more landslide-prone locations in Bhor, Velhe, Ambegaon, and Junnar in Pune District, Maharashtra. © 2024 IEEE.

关键词： Landslides

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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm with Niching technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Computer School Beijing Information Science and Technology University Beijing China School of Mathematics and Computer Science Tongling University Tongling China National Engineering Laboratory for Internet Medical Systems and Applications the First Affiliated Hospital of Zhengzhou University Zhengzhou China

In this paper, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multi-modality, we designed a hybrid metaheuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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