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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： George Popoiu PhD Student "Al. I. Cuza" University - Faculty of Computer Science Iaşi România Bitdefender Cyber Threat Intelligence Lab Iaşi România

Even though machine learning methods are being used in practice for malware detection, there are still many hurdles to overcome. Nowadays, there are still some challenges remaining regarding machine learning for malware detection: having a false positive rate as low as possible, fast classification, low volatile and disk memory usage. Because of these constraints, security solutions often have to rely on simpler models rather than on more complex ones. This paper has the purpose of reducing the training phase false positive rate of SVM models in the context of malware detection by using reformulations of the SVM optimization problem. The results obtained show that the proposed linear SVM model can be a drop in replacement with better false positive rate than regular linear SVM models or the one side class perceptron [4].

关键词： Support vector machines Training Scientific computing Computational modeling Machine learning Malware Numerical models

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Participatory Monitoring in Cultural Heritage Conservation: Case Study: The Landscape zone of the Bisotun World Heritage Site 21

Participatory Monitoring in Cultural Heritage Conservation: ...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Nasrolahi, Ahmad Gena, Cristina Messina, Vito Ejraei, Samet University of Torino Tech4Culture PhD Program Italy University of Torino Computer Science Department Italy University of Torino Historical Studies Department Italy Bisotun World Heritage Site Iran's Ministry of Cultural Heritage Iran

ISBN: (纸本)9781450383677

Community participation in cultural heritage conservation has been a concern since the Venice Charter (1964) so far. This approach has also been highlighted in the World Heritage documents. In this case, it is necessary to engage local people in all stages of protection, conservation and management. In addition, the Faro Convention (2005) emphasized that the responsibilities regarding the cultural heritage management must be shared between authorities and civil society to make possible a joint action among different stakeholders. At the site of Bisotun, participatory monitoring means the systematic reporting and recording of the issues regarding the cultural heritage properties provided by people living in the landscape zone in order to achieve sustainable monitoring of cultural heritage properties. In fact, the local people can monitor progress of safeguarding and protecting their heritage by daily observing and reporting relevant issues if any. The main purpose of participatory monitoring in this research is to engage the local community in the monitoring of cultural heritage properties in the Bisotun World Heritage Site. © 2021 ACM.

关键词： Monitoring

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Using prosody to improve dependency parsing 10

Using prosody to improve dependency parsing

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10th International Conference on Speech Prosody 2020

作者： Ghaly, Hussein Mandel, Michael Linguistics PhD Program Graduate Center City University of New York United States Computer and Information Science Brooklyn College United States

The goal of the present study is to use prosodic information to improve automatic syntactic parsing of conversational speech in the Switchboard Corpus. To achieve this, an ensemble classifier, based on a Recurrent Neural Network, is developed to predict the parse with the highest Unlabelled Attachment Score (UAS) from the outputs of multiple dependency parsers, based on syntactic and prosodic features. The main syntactic features proposed, which we refer to as "dependency configurations," represent the relative dependency location of each of a pair of consecutive words. Empirical analysis indicates that configurations with a direct dependency between consecutive words are less likely to be associated with major prosodic breaks. Using syntactic features alone, the system achieved an improvement of 1.1% of UAS on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4%. Both empirical analysis of dependency configurations and parsing improvement suggest a relationship between prosody and direct dependency relationships between consecutive words. © 2020 International Speech Communications Association. All rights reserved.

关键词： Recurrent neural networks

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain Technology

Fog Computing based Privacy Preservation via Distributed Acc...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Ramya Thatikonda Priya Nandihal Priyanka M S Mahantesh Mathapati PhD in Information Technology University of the Cumberlands Williamsburg USA Dept of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore Dept. of AIML Nitte Meenakshi Institute of Technology Bengaluru Dept.of CSE Amruta Institute of Engineering and Management Sciences (AIeMS) Bengaluru

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices.

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Medical Image Segmentation by Improved Nested Unet 2nd

Medical Image Segmentation by Improved Nested Unet

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2nd International Conference on Intelligent Systems and Data science, ISDS 2024

作者： Tran, Song-Toan Le, Minh-Hai Nguyen, Thai-Son Nghi, Vinh-Khanh Nguyen, Thanh-Nguyen School of Engineering and Technology Tra Vinh University Tra Vinh87000 Viet Nam Resource Development Institute Tra Vinh University Tra Vinh87000 Viet Nam PhD program in Computer Science Tra Vinh University Tra Vinh87000 Viet Nam Facilities Management Department Tien Giang University Tien Giang84000 Viet Nam

ISBN: (纸本)9789819796120

Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. However, variations like Nested-Unet are necessary to address the challenge of adapting network size for datasets of varying scales. This study proposes a novel model based on a multilayer Unet architecture that leverages all output features within nodes of a Nested Unet network. We evaluate the proposed model on four key medical image datasets: electron microscopy (EM), nuclei, polyps (CVC-ClinicDB), and skin lesions. The model achieves impressive Dice scores: 94.08% for EM, 92.68% for nuclei, 87.20% for skin lesions, and 93.02% for the CVC-ClinicDB dataset. These outstanding results demonstrate the superior performance of our proposed model compared to recent advancements in the field. Remarkably, the model achieves this performance while maintaining a size that is half that of a traditional Unet model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven Analytics

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Divya Nimma Ch. Anudeep Tripti Sahu Nusrat Hamid Shah Lubna Hamid Shah B Kiran Bala PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Management studies Shri Vishnu Engineering college for women (A) Bhimavaram India MIMA Institute of Management Pune Maharashtra India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Department of Management and Marketing College of Business Jazan University Kingdom of Saudi Arabia Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world.

关键词： Consumer behavior Accuracy Soft sensors Predictive models Data models Social factors Real-time systems Safety Sustainable development Business

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular Biology 2025年第15期437卷

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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The Way to Make Blind People Use the Email System: Voice Based Email Generating System Using Artificial Intelligence

The Way to Make Blind People Use the Email System: Voice Bas...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Gaurav Kumar Rajput Sachin Sharma Bibhu Prasad Dash Meraj Farheen Ansari Pawankumar Sharma Surendra Kumar Shukla College of Computing Science and Information Technology Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India School of Computer and Information Sciences University of the Cumberland’s Williamsburg KY USA Department of Information Technology School of Computer and Information Sciences University of the Cumberland’s Williamsburg KY USA Department of Information Technology Walmart. University of the Cumberland’s PhD Candidate Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

People are becoming used to fake world and vocal communication as technology advances. There are a few ways to communicate with people online in this modern invention. A large number of individuals often choose and use the simplest form of reporting, namely email. The era of encrypted email allows users to communicate with other people by posting signals and also facilitates cross-border corporate communication. Some individuals are unable to take advantages of this technology due to their ignorant or lack the necessary visible screen ability. Therefore, a Speech completely messaging device is suggested using Py but instead Ai to save time for externally examined people. Its device gives those who have been evaluated on the outside some power of contact and considerably increases their sense of stability as objectivity. With the use of that invention, blind persons will indeed be able to send out emails just like other regular citizens. Voice-based messaging systems use cutting-edge technology to ensure their legitimacy to people who have been vetted on the outside.

关键词： Technological innovation Blindness Electronic mail Cryptography Artificial intelligence

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Interactive Visualization and App Development for Precipitation Data in Sub-Saharan Africa

Interactive Visualization and App Development for Precipitat...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Divydharshini Karthikeyan Sudha Shah Aparna S. Varde Clement Alo Department of Computer Science Environmental Management and Science PhD program Department of Earth and Environmental Science Environmental Management and Science PhD program

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Precipitation plays a highly significant part in the hydrological cycle and is responsible for depositing most of the freshwater on earth. It can also impact the climate ecosystem. Considerable work has been performed to understand, model and predict precipitation. However, to the best of our knowledge, hardly any interactive visualizations exist that allow end-users to obtain a better perception of local impacts; also there are no specific apps for this purpose. The two goals of our work are thus: to design a visualization tool that illustrates changes in precipitation in a given time period with analysis; and to design a mobile application (app) that displays Sub-Saharan precipitation patterns, both using big data. This work would be advantageous to environmental scientists, as well as local residents of the respective areas. It would also be beneficial to students interested in conducting climate-based research. It addresses the theme of IoT (Internet of Things) which involves providing ubiquitous access to Internet-Based information on platforms such as smartphones and other mobile devices.

关键词： Data visualization Tools Big Data Meteorology Africa Mobile applications Human computer interaction

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A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records

arXiv

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arXiv 2025年

作者： Mota, Marco Barbero Still, John M. Gamboa, Jorge L. Strobl, Eric V. Stein, Charles M. Kawai, Vivian K. Lasko, Thomas A. Vanderbilt University Medical Center Department of Biomedical Informatics United States Vanderbilt University Medical Center Department of Medicine United States Vanderbilt University Medical Center Department of Psychiatry and Behavioral Sciences United States Vanderbilt University Medical Center Department of Computer Science United States Vanderbilt DBMI PhD program 2525 West End Ave NashvilleTN United States

Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources represent exogenous variables in the data generation process causal graph that estimate latent root causes of the presence of SLE in the health record. We objectively evaluated the sources against the original variables from which they were discovered by training supervised models to discriminate SLE from negative health records using a reduced set of labelled instances. We found 19 predictive sources with high clinical validity and whose EHR signatures define independent factors of SLE heterogeneity. Using the sources as input patient data representation enables models to provide with rich explanations that better capture the clinical reasons why a particular record is (not) an SLE case. Providers may be willing to trade patient-level interpretability for discrimination especially in challenging cases. © 2025, CC BY-NC-ND.

关键词： Electronic health record

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