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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing Email Spam Detection Using Advanced AI Techniques

Enhancing Email Spam Detection Using Advanced AI Techniques

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Decision Aid sciences and Application (DASA), International Conference on

作者： Nidal A. Al-Dmour Shagufta Kousar Areeba Khan Anaum Ihsan Tahir Abbas Ali Q Saeed Department of Computer Engineering College of Engineering Mutah University Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science The TIMES Institute Multan Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Computer Center Northern Technical University Ninevah Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This review paper investigates the advances of artificial intelligence (AI) in the field of email spam detection. The study addresses AI-based techniques used for email spam filtering by classifying threats such as keywords, content-based, sender IDs, and phrases. Email spam remains one of the most pressing issues today, with spammers sending malicious links in junk folders to compromise confidential information. As spam filtering becomes increasingly complex, AI and machine learning techniques like Naïve Bayes, Support Vector Machine (SVM), and Random Forest are commonly used to mitigate these threats. This study proposes a novel framework that combines traditional machine learning models with deep neural networks to develop a robust and flexible spam detection system. The framework is designed to enhance the detection capabilities of the classifier by applying a series of key sequential steps, each intended to improve the system's ability to distinguish between legitimate emails and spam. The paper details the methodology, providing a step-by-step explanation of each phase to demonstrate how the framework advances email spam detection.

关键词： Support vector machines Machine learning algorithms Filtering Reviews Unsolicited e-mail Biological system modeling Ecosystems Pressing Telephone sets Random forests

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Game Theory-Based IoT Efficient Power Control in Cognitive UAV

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computers, Materials & Continua 2022年第7期72卷 1561-1578页

作者： Fadhil Mukhlif Norafida Ithnin Omar B.Abdulghafoor Faiz Alotaibi Nourah Saad Alotaibi Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Electronic and Telecommunication Department College of EngineeringThe American University of KurdistanIraq Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia Computer Department Faculty of Applied CollegeImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attention because of their high agility as well as widespread *** this paper,a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground ***,the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource *** quality of service(QoS)related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’net utility ***,an energy efficiency non-cooperative game theory power allocation with pricing scheme(EE-NGPAP)is proposed to obtain an efficient power control within IoT wireless ***,uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through *** results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction,which is regarded to be apt with the 5Gnetworks’***,the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.

关键词： UAV drones WSN IoT game theory energy efficiency 5G&B5G networks

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Effective Negation Handling Approach for Sentiment Classification using synsets in the WordNet lexical database 1

Effective Negation Handling Approach for Sentiment Classific...

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1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Lal, Utkarsh Kamath, Priya Applied Intelligence Accenture NCR Delhi India Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9781665436472

Good quality of data preprocessing will have a greater impact on improving the performance of the classification models. Negation is one of the most important features in textual data. Only one negation word can change the polarity of the whole sentence. It is often seen that words like 'not', 'non' or suffixes like 'n't' are removed during noise removal thereby leading to blunders in Sentiment Classification. Effective Feature extraction is the cornerstone of effective Sentiment Analysis and Negation handling is simply essential for this purpose. In this paper, an effective function for handling negations based on First Sentiment Word (FSW) antonymy in the WordNet has been implemented on a set of IMDB movie reviews. The function for Negation Handling created for this paper increased the accuracy of Sentiment Classification by 4-8%. Experiments done in this paper show that improving the quality of the data gives higher results than implementing different state-of-the-art methods like n-grams and even deep learning methods like Word Embeddings, especially when used in an industry setting, where there is a need of quick deployments and changes with cost effectiveness and resource management. © 2022 IEEE.

关键词： Embeddings

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Optimizing Energy Efficiency in Smart Healthcare Systems using an Improved Enumeration Algorithm for 5G-IoT

Optimizing Energy Efficiency in Smart Healthcare Systems usi...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Aby K Thomas A. Jeraldine Viji Gunavardini V Bhukya Balakrishna Suman Mishra Amit Lathigara Department of Electronics and Communication Engineering Alliance School of Applied Engineering Alliance University Bengaluru India Department of Electrical and Electronics Engineering Mailam Engineering College Villupuram India Department of Computer Science and Engineering Bannari Amman Institute of Technology (Autonomous) Erode India Department of Computer Science and Engineering(Data Science) MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering School of Engineering RK University Rajkot India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

High-power radio connection, such as that provided by the Fifth Generation (5G) in the transmission of data, is necessary for IoT devices in order to provide immediate surveillance on patients. In the field of healthcare, the fast implementation of IoT equipment that is allowed by 5G has brought about a revolution by giving capabilities to measure and diagnostics that are both efficient and real-time. These issues include the fact that there is not a standard structure for dealing with the apps, that there is not enough of resource improvement, which results in the waste of bandwidth on the networks, that there is a lack of resources that are environmentally conscious, and that the primary worry is the lack of fraudulent information or the privacy of private healthcare information. For the purpose of enhancing the thermal effectiveness in uplink transfer, an enhanced Optimal Enumeration Algorithm (OEA) with Proximity based Service (ProSe) has been suggested for use in Device-to-Device (D2D) communications. Furthermore, the findings of the simulator demonstrate that the better outcomes are achieved with greater energy utilization and reduced computing difficulty that is achieved. Outperforming both OEA and Round Robin, the Improved OEA (Proposed Method) attains an accuracy of 92.89%.

关键词： 5G mobile communication Surveillance Supply chains Standards organizations Medical services Real-time systems Device-to-device communication Internet of Things Velocity measurement Uplink

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Towards Non-Invasive Swallowing Assessment: an AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation

Towards Non-Invasive Swallowing Assessment: an AI-Powered In...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yuewen Luo Ayman Anwar Siyi Ren James L. Coyle Ervin Sejdic Division of Engineering Science Faculty of Applied Science & Engineering University of Toronto Toronto ON Canada Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto ON Canada Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto ON Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software’s superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment.

关键词： Accuracy Software algorithms Kinematics Software Real-time systems Physiology Software reliability Software measurement Usability Pragmatics

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Blockchain and Machine Learning for Predictive Resource Distribution in Disaster Response Management

Blockchain and Machine Learning for Predictive Resource Dist...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Raj, Vijilius Helena Sreevani, N. Pal Thethi, H. Nainwal, Ankita Ziara, Sajjad Kumari, Machikuri Santoshi Department of Applied Sciences N.H.C.E Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal500043 India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq GRIET Bachupally Telangana Hyderabad India

ISBN: (纸本)9798350355338

Many disaster response plans depend on timely resource delivery. Traditional systems struggle to allocate resources, collaborate, and foresee resource needs. This paper introduces the Dynamic Resource Allocation Framework (DRAF), a blockchain-machine learning solution to these issues. DRAF demands increased transparency, accountability, and speed in crisis response. This article covers DRAF's three primary components: forecasting resource requirements (RNP), optimizing resource usage (ORA), and tracking and managing resources. They all conduct crucial resource distribution tasks. DRAF is superior at distributing resources effectively, monitoring and coordinating resources in real time, and creating accurate resource estimates. Using current and previous data, machine learning algorithms, and blockchain technology allows for flexible and fast catastrophe resource management. Blockchain's responsibility and openness reduce poor management and cheating while ensuring everyone gets the same amount. Machine learning technologies speed up catastrophe response, reducing casualties. The study found that DRAF might improve emergency response management by simplifying resource planning. As natural and man-made disasters increase, resource management is crucial. DRAF might improve disaster response by making it simpler to support the most vulnerable. © 2024 IEEE.

关键词： Decision making

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An enhanced strategy for minority class detection using bidirectional GRU employing penalized cross-entropy and self-attention mechanisms for imbalance network traffic

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European Physical Journal Plus 2024年第6期139卷 1-24页

作者： Abbasi, Asima Akber Zameer, Aneela Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Nilore 45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

The emergence of the Internet as a global communication tool has brought significant transformations across various aspects of our everyday lives. However, it has also resulted in new technical challenges, such as the risk of personal information exposure, network congestion, and limited availability of network addresses. The cybersecurity field has greatly benefited from the implementation of artificial intelligence and machine learning to model network activity and improve the detection of potential threats. Intrusion detection systems are specifically unable to tackle the challenges of high-class imbalance and the varying significance of misclassifications specifically for minority classes. The proposed SABiG (CPL) model targets the high dimensional imbalance data set CIC-IDS2017 by employing the evolutionary computing framework of genetic algorithms utilizing random forest GA(RF) for optimal feature selection. For attack classification, a powerful deep learning technique of bidirectional gated recurrent unit (Bi-GRU) with a self-attention mechanism is exploited. Our proposed model features a custom penalized cross entropy strategy by assigning three penalty values according to class imbalanced ratios to address the misclassification issue. The experimental results have demonstrated enhanced detection accuracy for all classes in general and minority classes in particular, such as infiltration, and web attacks. Comparison with the existing techniques in terms of all standard performance indices further evaluates our proposed intrusion detection system for imbalanced data. The generalization of the proposed model is evaluated with the recent CIC-CSE-IDS2018 web attack dataset. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Performance Evaluation of Fully Generalized Spatial Modulation with Hybrid Beamforming in Vehicular Networks: MmWave vs. IEEE 802.11ad 7

Performance Evaluation of Fully Generalized Spatial Modulati...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Mohamed Hashim, Sheemaa Ali Dziyauddin, Rudzidatul Akmam Bin Mohd Izhar, Mohd Azri Seman, Norhudah Rahim, Sharul Kamal Abd. Chatzimisios, Periklis Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur Malaysia King Khalid University Applied College in Khamis Mushait Computer Science Department Abha Saudi Arabia Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia International Hellenic University Department of Information and Electronic Engineering Thermi Greece

ISBN: (纸本)9798350392074

The increasing need for high data rates in vehicular networks necessitates the development of innovative technologies to improve spectral efficiency (SE) and guarantee the reliability of cellular connections. An innovative approach that uses an integration of Fully Generalized Spatial Modulation (FGSM) with hybrid beamforming (H-FGSM) to improve vehicular communication, exhibiting notable enhancements in SE. This paper explores the utilization of FGSM in conjunction with hybrid beamforming within vehicular communication systems, specifically emphasizing two predominant frameworks: millimeter-wave (mmWave) and IEEE 802.11ad. It employs a complete performance analysis to investigate the effects of H-FGSM on the aforementioned communication frameworks. It is worth mentioning that the mmWave framework has a superior SE of 7bps/Hz in comparison to IEEE 802.11ad, in addition to extended coherence time by 3 times and decreased frequency of adjustments to channel state information (CSI). These characteristics render mmWave very suitable for dynamic vehicular environments that require reliable communication routes. Consequently, the integration of FGSM with hybrid beamforming presents a potentially viable approach for addressing the requirements of advanced intelligent transportation systems in future vehicle networks, which leads to the foundation for future investigations aim at enhancing the efficiency of mmWave communication systems in dynamic vehicular situations. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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