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arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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Intelligent Cybersecurity Conference (ICSC)

作者： Yousef Sharrab Dimah Al-Fraihat Mohammad Alsmirat Dept. of Data Science and Artificial Intelligence Isra University Amman Jordan Dept.of Software Engineering Isra University Amman Jordan Dept. of Computer Science University of Sharjah Sharjah UAE

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the “CIC Truth Seeker dataset 2023” [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention.

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Perceptions Unveiled: Analyzing Public Sentiment on IoT and AI Integration in Revolutionizing Social Interactions

Perceptions Unveiled: Analyzing Public Sentiment on IoT and ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Shweta Saraswat Shashi Sharma Ruchi Kulshrestha Nootan Verma Monica Lamba Dept. of Artificial Intelligence With Data Science Arya College of Engineering Jaipur India Dept. Of CSE JECRC University Jaipur India Dept. Of CSE Mody University Jaipur India Dept. of CSE Arya College of Engineering Jaipur India

The IoT and AI are transforming social connections and communication. The article investigates how this symbiotic relationship impacts Twitter sentiment. Using the Twitter Sentiment dataset," this study analyzes how IoT and AI are changing linked experiences. Analyzing Twitter sentiment on this historic merger shows positive and negative sentiments. Social media allows people to express their thoughts and feelings. This evolving digital environment reflects society and reveals technology adoption sentiment. The merging of IoT with AI is a key technological achievement. Tweets provide a real-time snapshot of worldwide dialogues and reactions to understand public opinion on integration. This study examines the complicated sentiment landscape using the "Twitter Sentiment dataset," which captures IoT and AI sentiments. Social media sentiment analysis has been used to study public reactions to technological advances. Several studies show that sentiment analysis can predict technological adoption, examine attitudes and concerns, uncover issues, influence legislation, and address ethical issues. Research also emphasizes sentiment polarization's enthusiasm-realism balance and how sentiment predicts technical impact. The research used the "Twitter Sentiment dataset," with over 1.6 million tweets. Lowercase, punctuation, and stopwords were removed from the dataset. After that, NLP algorithms like sentiment analysis examined the text. The training and test sets were split, and TF- IDF vectorization retrieved features. This research tested sentiment analysis machine learning models (Bernoulli Naive Bayes, Linear Support Vector Classification, and Logistic Regression). Later, the best model was stored. The main measure of sentiment analysis model accuracy was accuracy. Logistic regression outperformed with 83% accuracy. The models accurately captured IoT and AI integration tweets, including joy and concerns. Sentiment analysis can disclose society's feelings, making IoT-AI integ

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An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network

An Enhanced Smart Intelligent Detecting and Alerting System ...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： G.A Senthil P Suganthi R. Prabha M. Madhumathi S. Prabhu S Sridevi Dept. of Information Technology Agni College of Technology Chennai India Dept. of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Dept. of Artificial Intelligence and Data Science Karpaga Vinayaga College of Engineering and Technology Chennai India Dept. of Computer Science and Engineering Sri Ramachandra Engineering and Technology Chennai India

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. The research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. The technique must be connected to the stove. The design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. The Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. The flame sensor in the technology will supervise flame and fire. The buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user’s daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. The system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. The precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas.

关键词： Industries GSM Cloud computing Vents Fires Sensor systems Intelligent sensors

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YOLOv8-Based Person Detection, Distance Monitoring, Speech Alerts, and Weapon Identification with Email Notifications

YOLOv8-Based Person Detection, Distance Monitoring, Speech A...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： V. Jyothsna Chamundi Alle Ragavendra Kurnutala Ganesh K N KushalKarthik K R Bhasha Pydala Dept. of IT Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

An advanced security system utilizing the YOLOv8 algorithm for comprehensive object detection enables real-time identification of individuals and weapons. Innovative features include precise distance estimation and email alerts upon weapon detection, enhancing communication during security breaches. Rigorous testing ensures reliability, while ethical considerations ensure adherence to standards. This project represents a significant contribution to automated threat detection and response, exemplifying advancements in security technology within ethical boundaries, effectively addressing contemporary challenges. The integration of YOLOv8 for object, person, and weapon detection in audio, alongside distance estimation and email notification, marks a substantial leap in security surveillance technology, offering swift threat detection and coordinated response capabilities. Rigorous validation confirms its efficacy, promising to fortify security protocols and proactive threat mitigation strategies, thereby paving the way for further advancements in audio-based threat detection systems.

关键词： Protocols Weapons Surveillance Prevention and mitigation Estimation Threat assessment Real-time systems

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Intra-datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts

Robust Sensor Networks: Applying AI for Fault Identification...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Manjunatha A S Venkatramana Bhat P Ramakrishna M Matti Nidhi Prabhu Dept. of Computer and Communication Engineering NITTE (Deemed to be University) NMAM Institute of Technology Nitte Mangaluru Karnataka Dept. of Computer Science and Engineering NITTE (Deemed to be University) NMAM Institute of Technology Nitte Mangaluru Karnataka Dept. of Data Science and Computer Applications Manipal Academy of Higher Education Manipal Institute of Technology Manipal India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s.

关键词： Fault diagnosis Wireless sensor networks Logistic regression Accuracy Fault detection Nearest neighbor methods Robustness Decision trees Random forests Testing

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Shrub of a Thousand Faces: An Individual Segmentation from Satellite Images Using Deep Learng

SSRN

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SSRN 2024年

作者： Khaldi, Rohaifa Tabik, Siham Puertas-Ruiz, Sergio de Giles, Julio Peñas Correa, José Antonio Hódar Zamora, Regino Segura, Domingo Alcaraz LifeWatch-ERIC ICT Core Seville41071 Spain Dept. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Botany Faculty of Science University of Granada Granada18071 Spain Dept. of Ecology Faculty of Science University of Granada Granada18071 Spain Granada18071 Spain Zaragoza50059 Spain

Monitoring the distribution and size-structure of long-living shrubs, such as Juniperus, can be used to estimate the long-term effects of climate change on high-mountain and high latitude ecosystems. Historical aerial very-high resolution imagery offers a retrospective tool to monitor shrub growth and distribution at high precision. Currently, deep learning models provide impressive results for detecting and delineating the contour of objects with defined shapes. However, adapting these models to detect natural objects that express complex patterns is still a challenging *** this study, we propose the largest dataset of shrubs with complex patterns (8580 digitized individuals), then we use Mask R-CNN to detect these shrubs above the treeline in Sierra Nevada (Spain) using RGB images (13 cm). To handle such kind of shrubs, we introduce a new model evaluation metric called Multiple Intersections over Ground Truth Area (MIoGTA) and a new dual data construction design: using photo interpreted (PI) and field work (FW) data to respectively develop and externally validate the model. Finally, we deploy the model for the first time to generate a wall-to-wall map of individuals *** results demonstrate the efficiency of the dual data construction approach and the new evaluation metric, where the model achieved an F1-score of 87,87% and 76.86% in PI and FW, respectively. According to the deployment results, these shrubs are highly concentrated in the North-West region and orientation, within a specific range of slope and altitude, where smaller shrubs tend to currently grow at higher altitudes and larger shrubs at lower altitudes. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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An Adaptive Learning Method for Sign Language Detection

An Adaptive Learning Method for Sign Language Detection

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Emerging Technology (INCET), International Conference for

作者： A. V. Sriharsha Kodudula Anusha Bramhanapudhuru Naveen Kacheri Sadak Hima Harshitha Reddy G Dept. of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept of IT Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Advancements in hand gesture recognition technology are transforming the way we interact with computers, manipulate objects in virtual worlds, and communicate using hand gestures. In this paper, we present a system that leverages the combined power of Media Pipe's holistic model and machine learning to achieve real-time hand gesture recognition with remarkable accuracy and efficiency. The pre-trained machine learning model is employed to classify detected hand gestures, providing a robust framework for gesture recognition. The system operates live, capturing video frames from your camera. Using Media Pipe's magic, it pinpoints key points on your hands, creating a digital map of their position and configuration. Detected hand landmarks are visually overlaid on the video frames, enhancing user understanding and system transparency. This information is then fed into a pre-trained machine learning model, a master of decoding hand movements, which instantly deciphers the gesture you're making. Like a secret code translator, it converts this gesture into a corresponding label, transforming your hand movements into meaningful commands or words.

关键词： Computers Codes Accuracy computational modeling System performance Machine learning Media

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A Learning Support Method for Multi-threaded Programs Using Trace Tables

arXiv

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arXiv 2024年

作者： Murata, Takumi Hashiura, Hiroaki Electronics Information and Media Engineering Major Graduate School of Engineering Nippon Institute of Technology Saitama Japan Dept. of Data Science Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan

Multi-threaded programs are expected to improve responsiveness and conserve resources by dividing an application process into multiple threads for concurrent processing. However, due to scheduling and the interaction of multiple threads, their runtime behavior is more complex than that of single-threaded programs, making which makes debugging difficult unless the concepts specific to multi-threaded programs and the execution order of instructions can be understood. In this paper, we propose a learning tool for multi-threaded programs using trace tables. Copyright © 2024, The Authors. All rights reserved.

关键词： Application programs

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