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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Md Robiul Islam Md Mahamodul Islam Mst. Suraiya Afrin Anika Antara Nujhat Tabassum Al Amin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Informatics University of Oslo Oslo Norway Dept. of Building Engineering and Construction Management Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronics Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Abstract-Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models’ decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a $1 D$ Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.

关键词： Uniform resource locators Deep learning Analytical models Accuracy Phishing Organizations Feature extraction

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FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network

arXiv

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arXiv 2024年

作者： Go, Young-Min Yu, Seong-Hyun Park, Hyeong-Yeong Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Chungbuk28644 Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Gyeonggi14662 Korea Republic of

Brain-computer interface (BCI) technology facilitates communication between the human brain and computers, primarily utilizing electroencephalography (EEG) signals to discern human intentions. Although EEG-based BCI systems have been developed for paralysis individuals, ongoing studies explore systems for speech imagery and motor imagery (MI). This study introduces FingerNet, a specialized network for fine MI classification, departing from conventional gross MI studies. The proposed FingerNet could extract spatial and temporal features from EEG signals, improving classification accuracy within the same hand. The experimental results demonstrated that performance showed significantly higher accuracy in classifying five finger-tapping tasks, encompassing thumb, index, middle, ring, and little finger movements. FingerNet demonstrated dominant performance compared to the conventional baseline models, EEGNet and DeepConvNet. The average accuracy for FingerNet was 0.3049, whereas EEGNet and DeepConvNet exhibited lower accuracies of 0.2196 and 0.2533, respectively. Statistical validation also demonstrates the predominance of FingerNet over baseline networks. For biased predictions, particularly for thumb and index classes, we led to the implementation of weighted cross-entropy and also adapted the weighted cross-entropy, a method conventionally employed to mitigate class imbalance. The proposed FingerNet involves optimizing network structure, improving performance, and exploring applications beyond fine MI. Moreover, the weighted Cross Entropy approach employed to address such biased predictions appears to have broader applicability and relevance across various domains involving multi-class classification tasks. We believe that effective execution of motor imagery can be achieved not only for fine MI, but also for local muscle MI. © 2024, CC BY-NC-ND.

关键词： Electroencephalography

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Enhancing Wrist Abnormality Detection with YOLO: Analysis of State-of-the-art Single-stage Detection Models

arXiv

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arXiv 2024年

作者： Ahmed, Ammar Imran, Ali Shariq Manaf, Abdul Kastrati, Zenun Daudpota, Sher Muhammad Dept. of Computer Science Sukkur IBA University Sukkur65200 Pakistan Gjøvik2815 Norway Dept. of Informatics Linnaeus University Växjö351 95 Sweden

Diagnosing and treating abnormalities in the wrist, specifically distal radius, and ulna fractures, is a crucial concern among children, adolescents, and young adults, with a higher incidence rate during puberty. However, the scarcity of radiologists and the lack of specialized training among medical professionals pose a significant risk to patient care. This problem is further exacerbated by the rising number of imaging studies and limited access to specialist reporting in certain regions. This highlights the need for innovative solutions to improve the diagnosis and treatment of wrist abnormalities. Automated wrist fracture detection using object detection has shown potential, but current studies mainly use two-stage detection methods with limited evidence for single-stage effectiveness. This study employs state-of-the-art single-stage deep neural network-based detection models YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. Through extensive experimentation, we found that these YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in bone fracture detection. Additionally, compound-scaled variants of each YOLO model were compared, with YOLOv8x demonstrating a fracture detection mean average precision (mAP) of 0.95 and an overall mAP of 0.77 on the GRAZPEDWRI-DX pediatric wrist dataset, highlighting the potential of single-stage models for enhancing pediatric wrist imaging. © 2024, CC BY.

关键词： Pediatrics

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Enhanced Identification of Care Preference Documentation in Patients' Discharge Summaries Using Pre-Trained Large Language Models

Enhanced Identification of Care Preference Documentation in ...

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Artificial Intelligence for Medicine, Health and Care (AIMHC), IEEE International Conference on

作者： Saksham Arora Inas S. Khayal Department of Computer Science Dartmouth College Hanover NH USA Dept. of Computer Science The Dartmouth Institute & Biomedical Data Science Geisel School of Medicine at Dartmouth Dartmouth College Hanover NH USA

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than the more appropriately broad population of ‘seriously ill’ patients. This study addresses this gap, first by matching decedent patients to non-decedent counterparts by using propensity score matching, accounting for important variables like age, gender, primary diagnoses, and patient co-morbidities, and then by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods. We trained and fine-tuned Bio_ClinicalBERT and ClinicalLong-former models on a large dataset of patient discharge summaries, enhancing their signal-to-noise ratio by focusing on key textual aspects, thereby capturing contextually nuanced mentions of care preference documentation. These models demonstrated high sensitivity and specificity compared to industry-standard keyword-search methods, proving adept.at interpreting complex clinical concepts. This study highlights the potential of transformer-based models specifically trained for clinical domain tasks. Our findings not only contribute to enhancing end-of-life communication and aligning treatment with patients' care objectives but also pave the way for future research in this promising domain, with potential implications for improving patient care quality.

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Precision Localization: An Experimental Study on BLE Fingerprinting and Trilateration with ESP32

Precision Localization: An Experimental Study on BLE Fingerp...

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Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Ahmed H. Elhusseiny Marwa Zamzam Yasmine Zaghloul Dept. of Electrical Engineering The German International University Cairo Egypt Dept. of Informatics and Computer Science The German International University Cairo Egypt

This paper presents a method for indoor localization using two complementary techniques: Fingerprinting (FP) and Trilateration. The system is based on Bluetooth Low Energy (BLE) technology and a network of wireless anchors to determine the location of a mobile device. The FP approach collects Received Signal Strength Indicator (RSSI) data from BLE anchors in various indoor locations and creates a map of the environment in an offline stage. During the online stage, the device’s location is determined by comparing its current RSSI measurements to the map. The FP technique showed an accuracy of 79.31% without positions on the other side of a separating concrete wall and 71.88% including those positions. In contrast, trilateration calculates the position of the device using distance measurements from multiple anchors, achieving an accuracy of 44.44%. Both techniques were tested in various environments and layouts, and the results indicate that FP is more suitable for indoor localization, particularly in large buildings with many rooms. This paper highlights the potential of these two techniques for practical indoor localization applications. Moreover, one of the experimental setups for the fingerprinting technique was tested in multiple rooms separated by a concrete wall to provide information about the potential scalability of the technique.

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End-to-End n-ary Relation Extraction for Combination Drug Therapies

arXiv

引用

arXiv 2023年

作者： Jiang, Yuhang Kavuluru, Ramakanth Department of Computer Science University of Kentucky LexingtonKY United States Division of Biomedical Informatics Dept. of Internal Medicine Univ. of Kentucky LexingtonKY United States

Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the combination drug therapy MeSH heading with at least 10K articles published per year over the past two decades. Extracting combination therapies from scientific literature inherently constitutes an n-ary relation extraction problem. Unlike in the general n-ary setting where n is fixed (e.g., drug-gene-mutation relations where n = 3), extracting combination therapies is a special setting where n ≥ 2 is dynamic, depending on each instance. Recently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset, CombDrugExt, for extracting such therapies from literature. Here, we use a sequence-to-sequence style end-to-end extraction method to achieve an F1-Score of 66.7% on the CombDrugExt test set for positive (or effective) combinations. This is an absolute ≈ 5% F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. Our model seamlessly extracts all drug entities and relations in a single pass and is highly suitable for dynamic n-ary extraction scenarios. © 2023, CC BY.

关键词： Extraction

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Enhancing patient Comprehension: An effective sequential prompting approach to simplifying EHRs using LLMs

Enhancing patient Comprehension: An effective sequential pro...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dehkordi, Mahshad Koohi H. Zhou, Shuxin Perl, Yehoshua Deek, Fadi P. Einstein, Andrew J. Elhanan, Gai He, Zhe Liu, Hao Njit Department of Computer Science NewarkNJ United States Njit Department of Informatics NewarkNJ United States Dept. of Radiology Columbia University Irving Medical Center Cardiology Division Dept. of Medicine New YorkNY United States University of Nevada Center for Genomic Medicine School of Medicine RenoNV United States Florida State University College of Communication & Information TallahasseeFL United States Montclair State University Department of Computer Science MontclairNJ United States

ISBN: (纸本)9798350386226

Electronic Health Record (EHR) notes often contain complex medical language, making them difficult to understand for patients lacking medical background. Simplifying EHR notes to a 6th-grade reading level is recommended by the American Medical Association to enhance patient comprehension and engagement. Large Language Models (LLMs) show promise in achieving this goal but also face challenges, such as missing and generating false information. In our previous work, we have shown that providing LLMs with highlighted EHRs, where the important information is highlighted, results in more accurate summaries compared to summarizing unhighlighted notes. In this study, we simplify highlighted EHRs with LLMs, specifically ChatGPT-4o, using two approaches: two-step simplification (sequential) and one-step (CoT-based) simplification. In the sequential approach, we generate a structured summary of the highlighted EHR, as a first step, and then we convert this summary into language suitable for a 6th-grade reader, as a second step. In the CoT-based approach, we convert the highlighted EHR into a structured summary understandable for a 6th-grade reader in one step. Evaluating the simplified notes obtained from the two approaches, the sequential approach shows higher completeness (82.35% vs. 75.89%) and correctness, as well as better readability scores (FKGL: 7.72 vs. 10.73;Flesch: 67.71 vs. 45.31) and higher average understandability ratings from ChatGPT-4 (3.92 vs. 3.28), demonstrating its overall superiority in simplifying notes. © 2024 IEEE.

关键词： Electronic health record

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning

Phoneme Classification in Imagined Speech Using Explainable ...

引用

International Winter Workshop on Brain-computer Interface (BCI)

作者： Sejin Kim Hyunwook Kang Ji-Hoon Jeong Minji Lee Dept. Artificial Intelligence The Catholic University of Korea Bucheon South Korea Dept. Electrical and Computer Engineering Inha University Incheon South Korea Dept. Computer Science Chungbuk National University Cheongju South Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36 %, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke's area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke's area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future.

关键词： Accuracy Machine learning algorithms Closed box Machine learning Predictive models Brain modeling Brain-computer interfaces Classification algorithms Reliability Next generation networking

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Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

arXiv

引用

arXiv 2023年

作者： Hong, Xudong Sayeed, Asad Mehra, Khushboo Demberg, Vera Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Saarland University Germany Dept. of Philosophy Linguistics Theory of Science University of Gothenburg Sweden

Current work on image-based story generation suffers from the fact that the existing image sequences collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent and more diverse compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and more diverse than stories generated with the current state-of-the-art model. This is a pre-MIT Press publication version. © 2023, CC BY.

关键词： Image enhancement

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Real-Time Object Detection with IOT Using a Smart Cart

Real-Time Object Detection with IOT Using a Smart Cart

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Muhammad Omer Sardar Jaffar Ali Syed Muhammad Raza Duc-Tai Le Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of AI System Engineering Sungkyunkwan University Suwon Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea

With Industry 4.0's technological advancements, the convenience and affordability of Artificial Intelligence (AI) and IoT have increased. However, customers still face long waiting times in payment queues during the smart shopping experience. Current solutions rely on expensive sensor-based RFIDs or simple barcodes, making them impractical and limited in scalability. This paper proposes an economically smart solution with two cameras on NVIDIA JETSON NANO for realtime object monitoring and automated shopping. Communication between modules is facilitated by TCP/IP and HTTP protocols. By utilizing the YOLO v7, real-time monitoring is achieved. This innovative approach revolutionizes the customer experience by providing a seamless interface and time-saving features. Additionally, it offers potential integration with e-commerce businesses and payment gateways, further enhancing the overall customer and vendor experience.

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