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检索条件"机构=Computer Engineering Program and Dept. of Computer Science"

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment

Enhanced Machine Learning Approach for Detecting Spammers in...

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

作者： K. Sai Bhavana B. Abhinandu P. Sai Sriyesh K. Rama S. Satheesh Kumar Dept. Computer Science & Information Technology Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities.

关键词： Cloud computing Machine learning algorithms Clustering algorithms Production Machine learning Intelligent systems Hypertext systems Principal component analysis Industrial Internet of Things Graphical user interfaces

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UAV and Deep Learning: Detection of selected riparian species along the Ganga River

UAV and Deep Learning: Detection of selected riparian specie...

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ISPRS Technical Commission I Midterm Symposium on Intelligent Sensing and Remote Sensing Application

作者： Tripathi, Ravindra Nath Ramachandran, Aishwarya Agarwal, Karan Tripathi, Vikas Badola, Ruchi Hussain, Syed Ainul Ganga Aqualife Conservation Monitoring Center Wildlife Institute of India Dehradun248001 India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun248002 India Dept. of Remote sensing Birla Institute of Technology Mesra Ranchi835215 India

Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and wellbeing. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 x 3200 resolution. Additionally, our

关键词： Object detection

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Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks 3

Computational Overhead Analysis for Mitigation of Content Po...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Priya, M. Castro, S. Kumar, Sathesh K. Kalaivani, N. Dept. of Computer Science and Engineering E. G. S. Pillay Engineering College Nagapattinam611002 India Dept. of Information Technology Karpagam College of Engineering Coimbatore641032 India Dept. of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tiruchengode637205 India Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore641041 India

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs. © 2023 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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Notes Application using Android Studio and Cloud-based Data Management

Notes Application using Android Studio and Cloud-based Data ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Ratnam Dodda Maneesh Reddy Yanala Srija Pavishetti Dept. of Computer Science and Information Technology CVR College of Engineering Hyderabad India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In today’s rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app’s core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age.

关键词： Cloud computing Visualization Technological innovation Java Operating systems Image processing Speech recognition

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

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International Conference on computer and Information Technology (ICCIT)

作者： Tanvir Ahmed Farzana Sharmin Mou Dept. of Computer Science Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh Dept. of Computer Science University of Birmingham Birmingham UK

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security.

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A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications 21

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alja'Afreh, Saqer S. Altakhaineh, Amjaad T. Al-Shamaileh, Mallak H. Matarneh, Aser M Daoud, Omar R. Al-Khawaldah I, Mohammad School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Pioneers International Academy Data Science and Machine Learning Dept. Amman Jordan Al-Ahliyya Amman University Communications and Computer Engineering Amman Jordan

ISBN: (纸本)9798350374131

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features. © 2024 IEEE.

关键词： Antenna feeders

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Modular Design of Solid-State Transformer with High Flexibility

Modular Design of Solid-State Transformer with High Flexibil...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Zhuxuan Ma Hui Cao Yue Zhao Dept. of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper presents a modular solid state transformer (SST) design incorporating dual active bridge and cascaded H-Bridge topologies to enhance electric vehicle (EV) charging infrastructure. By using modular design concept for the power cell hardware, the proposed SST can be suitable for industrial usage with low manufacturing cost, and low maintenance complexity. With various combinations of the power cells, the whole SST converter has high flexibility for the power scale and circuit topology. This guarantees the fast installation and high transportability of the SST, suitable for the high-demanded EV charging applications. The design detail of the busbar used for the SST has been presented, which shows how to reduce the stray inductance of the busbar while keeping its high current conduction ability and high insulation. To validate the high current conduction ability of the busbar, finite element analysis is conducted to assess busbar current density and temperature. Finally, an advanced control strategy is introduced to effectively reduce DC link ripple, eliminating the need for bulky components.

关键词： Inductance Circuit topology Bridge circuits Transportation Thermal conductivity Electric vehicle charging Topology Thermal stresses Stress Power transformer insulation

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Letting 3D Guide the Way: 3D Guided 2D Few-Shot Image Classification

Letting 3D Guide the Way: 3D Guided 2D Few-Shot Image Classi...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Jiajing Chen Minmin Yang Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

Existing few-shot image classification networks aim to perform prediction on images belonging to classes that were not seen during training, with only a few labeled images, which are randomly picked from the same image pool as the support set. However, this traditional approach has two main issues: (i) in real-world applications, since support images are randomly picked, the angle they were captured from can be very different from that of the query image, causing the images to look very different and making it hard to match them; (ii) since support and query images, for both training and testing, are sampled from the same image pool, models can overfit the dataset, especially if the image pool contains images with similar color, texture or view angle. Thus, good performance on a dataset does not reflect a model’s real ability. To address these issues, we propose a novel few-shot learning approach referred to as the 3D guided 2D (3DG2D) few-shot image classification. In our proposed approach, the queries are 2D images, and the support set is composed of 3D mesh data, providing different views of an object, in contrast to randomly picked images providing a single view. From each 3D mesh, 14 projection images are generated from different angles. Thus, these projections have significant variance among themselves. To address this challenge, we also propose the Angle Inference Module (AIM), which is used to infer the view angle of a query image so that more attention is given to projection images corresponding to the same view angle as the query image to achieve better prediction performance. We perform experiments on ModelNet40, Toys4K and ShapeNet datasets with 4-fold cross validation, and show that our 3DG2D few-shot classification approach consistently outperforms the state-of-the-art baselines.

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Active Control for Resonance Mitigation in Electric Drive Applications

Active Control for Resonance Mitigation in Electric Drive Ap...

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International Symposium on Industrial Electronics (INDEL)

作者： Emelie Berglund Johan Borg Jonny Johansson Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance.

关键词： Voltage measurement Monte Carlo methods Heuristic algorithms Filtering algorithms Pulse width modulation Resonance Inverters Timing Surges Voltage control

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Dirt Identification and Removal based on Non-Contact Infrared Temperature Sensor on Solar Panel

Dirt Identification and Removal based on Non-Contact Infrare...

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

作者： Rezmah Jemima Rupin Imran Hasan Rownak Jahan Md. Rakibul Haque Ontor Md. Motaharul Islam Dept. of Computer Science Engineering East West University Dhaka Bangladesh Dept. of Computer Science Engineering United International University Dhaka Bangladesh

Temperature sensing is a technique to detect dirt on a solar-panel-surface that can influence its surface temperature and can reduce its performance, which necessitates monitoring to optimize solar panel output generation, reduce unnecessary costs, and perform efficient cleaning. Temperature sensing can achieve faster and more accurate detection of dirt without using any robotic cameras or manual observation. In this study, a method is proposed to detect dirt on a solar panel with the help of an MLX90614 sensor, a DHT22 sensor connecting with Arduino UNO components. DC motors and gear motors are being used for the removal of accumulated dirt. The temperature of the solar panel surface is measured by an MLX90614 sensor using infrared radiation, while the environment's humidity and temperature are measured by a DHT22 sensor. The system implies measuring the solar panel’s temperature and comparing it to the ambient temperature. This system aims at improving previously proposed dirt detection methods using this approach and suggests a motor-driven dirt removal technique.

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