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Expression of Concern for: A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time

Expression of Concern for: A Scalable Platform to Collect, S...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： Pravin Ramdas Kshirsagar Dhoma Harshavardhan Reddy Mallika Dhingra Dharmesh Dhabliya Ankur Gupta Department of Artificial Intelligence S B Jain College of Engineering and Technology Nagpur Maharashtra India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Mumbai Maharashtra India Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

ISBN: (数字)9798350336238

ISBN: (纸本)9798350336245

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the Programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection.

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Expression of Concern for: Building an Emotion detection system in Python Using Multi-Layer Perceptrons for Speech Analysis

Expression of Concern for: Building an Emotion Detection Sys...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Regula Srilakshmi Vidya Kamma Shilpa Choudhary Sandeep Kumar Munish Kumar Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (数字)9798350342338

ISBN: (纸本)9798350342345

Human speech emotion recognition analyses a speaker's speech to determine their emotional state. Included are several applications in psychology, medicine, and human-computer interaction. Automated speech emotion identification techniques have been widely used in recent years thanks to machine learning algorithms. A number of speech processing libraries are available in Python, a popular programming language for system research. This focuses on creating a Python-based full speech popularity system that uses MLP classifiers. The goal of applications in psychology, healthcare, and human-computer interaction is to routinely discern the emotional state of a speaker based on their speech. With the aid of a multi-layer perceptron (MLP) classifier, we intend to improve a speech emotion reputation device written in Python. The proposed system will derive features from speech recordings, including mal-frequency cepstral coefficients (MFCCs) and prosodic functions. We will train and evaluate our model using an open-supply speech emotion popularity dataset, including RAVDESS. The extracted features may be supplied to an MLP classifier that has been trained using the classified emotional speech statistics dataset. Once mastered, the device can accurately categorise the speaker's emotional condition into a variety of categories, including happiness, despair, anger, and fear. Finally, we can evaluate the performance of our model using existing techniques on a distinct check out dataset.

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Design and Development of an IoT Enabled Low-Cost Smart Hand Glove system for Gait detection in Stroke Patients

Design and Development of an IoT Enabled Low-Cost Smart Hand...

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Intelligent Technologies (CONIT), international conference on

作者： Shaik Shafi R Padma Priya R Nithish Reddy R Bhuvanesh M Jennifer Md Adhil Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Medak India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Stroke is a non-communicable disease in human beings signifies a critical, pervasive, and widely recognized health crisis. Stroke-induced gait alterations can significantly impact a patient’s ability to perform daily activities. In the recent past, stroke became one of the primary cause of the disability in elderly people and recorded as third leading cause of death globally. According to statistics given by National Center for Disease Informatics and Research(NCDIR), stroke is India’s 3rd leading source of death and the 6th leading reason of disability. To this end, there exists different rehabilitation models to detect human gait in stroke survivors. Human gait, a complex interplay of limb (arm or leg) movements, velocity, and energy cycles, holds crucial diagnostic information for stroke patients. Most of the existing models are cost effective and intrusive type, cause discomfort while using. Thus, in this paper, a wearable low-cost hand glove based system for gait detection and analysis for stroke survivors using IoT is proposed. The system leverages MEMS accelerometer sensor (ADXL335), offers a non-intrusive means of collecting gait dynamics like motion, shock or vibration and allows continuous monitoring. Further, the MEMS sensor is integrated into an IoT enabled hand glove with Wi-Fi communication module used for wireless monitoring of gait dynamics. Later, the collected data is analyzed using MATLAB. The analysis provides a thorough understanding of post-stroke gait patterns and cautions the stroke survivors about their abnormal gait. In addition, the proposed wearable hand glove model possibly saves the patients from future injuries and may allow to tailor rehabilitation strategies for every individual.

关键词： Wireless communication Micromechanical devices Wireless sensor networks Dynamics Medical services Stroke (medical condition) Real-time systems

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Enhancing the Leaf Disease detection Through Convolutional Neural Network 3rd

Enhancing the Leaf Disease Detection Through Convolutional N...

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Rohit, Deep Darji, Mohini Joshi, Sachi Patel, Upesh Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Anand Changa India Chandubhai S. Patel Institute of Technology Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9789819795772

In India, agriculture is the largest producer of The Gross Domestic Product (GDP). GDP significantly influences the economic conditions of any nation. Globally, the impact of plant diseases on agricultural productivity, causes major economic losses and food security problems. Plant diseases lead to a decrease in the quality and quantity of agricultural products. Cereal plant diseases are a major threat to food security. Early diagnosis of crop diseases using accurate and automated detection techniques can improve the quality of food production and reduce economic losses. Using a public dataset of 87,867 images divided into 38 healthy and diseased leaves collected under controlled conditions. This model is trained on a deep convolutional neural network to detect and classify many plant species. The proposed system can detect diseases of 14 crops such as apple, blueberry, acerola, corn, etc. Model performance is evaluated based on classification accuracy and mean score. A convolutional neural network has been involved in image classification. This model includes sequential layers, convolutional layers, and maximum voting layers. This model also completed a smoothing layer followed by a density layer using Relu activation for multi-class classification with Softmax activation. Finally, after the first dense layer, a Dropout Regularization with a rate of 0.25 is applied to avoid overfitting. The evaluation showed excellent global classification accuracy, with around 97% trained using Convolutional Neural Networks (CNN). This paper also highlights challenges and future directions to provide valuable information to researchers and practitioners involved in agricultural technology and the management of plant diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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HTTP DDoS Attack detection technology Based on PF-RING and Gaussian Naive Bayes in Containerized Environment 3rd

HTTP DDoS Attack Detection Technology Based on PF-RING and...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Lin, Zihao Zhang, Weiyong Jiang, Qiu Lu, Hui Liu, Yuan Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510555 China Huangpu Research School of Guangzhou University Guangzhou510555 China

ISBN: (纸本)9789819645022

With the widespread adoption of container technology, modern enterprises increasingly utilize cloud-based deployments to enhance system flexibility and operational efficiency. However, this shift also introduces new security challenges, particularly HTTP Distributed Denial of Service (DDoS) attacks. These attacks mimic normal HTTP user behavior at the application layer, making them difficult for traditional defense mechanisms to detect, thus posing a significant threat to network security. To address this issue, this paper proposes an HTTP DDoS attack detection method that combines eBPF and PF_RING technologies and uses an optimized Gaussian Naive Bayes algorithm for efficient detection. The proposed method deploys efficient programs in kernel space for real-time analysis and feature extraction of network packets and uses PF_RING to optimize the network traffic capture process, reducing packet loss and system resource consumption. Experimental results demonstrate that this method outperforms traditional detection methods in terms of accuracy, recall, and F1 score, especially when handling large-scale data and real-time data streams. This approach not only enhances detection accuracy but also reduces system resource consumption, offering an optimized and efficient solution for HTTP DDoS attack detection in containerized environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network security

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Ensuring Security in Internet of Everything (IoT) With Smart Sensor Networking and Mobile Ad Hoc Network

Ensuring Security in Internet of Everything (IoT) With Smart...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ashalatha. G Sanjay Modi Anil Kumar Dixit R. Ravi Kumar Hayder Al-Chilibi Malik Bader Alazzam Department of Computer Science – CS - DS & AI & DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Computer Science and IT Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Jordan

ISBN: (数字)9798350399264

ISBN: (纸本)9798350399271

A new age of connection is being ushered in by the Internet of Things (IoT), one that relies on a wide variety of objects and several modulation schemes to communicate. The followings will define this new era of correspondence: The IoT presents a substantially higher number of concerns, restrictions, and problems than do conventional computers since all of its items are interconnected and operate in unprotected contexts. This is because there are fewer linked elements in conventional computer systems. As a result, it is essential that cybersecurity be given priority in a new strategy, although that is not presently true of regular computer platforms. Two key elements in the development of an Internet of Everything system are the Smart Sensor Networking and also the Mobile Ad hoc Network. Sensing, pollution prevention, data collection, multimodal telecommunication networks, and data management are only a few of the many operations that employ this technology. IoT is vulnerable to the same types of safety concerns that other networks face since it combines elements of both Mobile ad hoc networks and Embedded system. A Suspension of Operation is a subtype of an onslaught known as a Delegates Entities Operation. The intruder delivers an unacceptably large number of command sessions that seem genuine. An SD operation is one of the various types of DoS attacks that are possible. This makes it far more difficult to identify this kind of assault than a straightforward one that drains the capacitors capability. The necessity to improve energy conservation and extend the life of Iot networks is one of the other major issues that occur in a networking throughout an SD attack. This is another important problem that develops in a network after an assault. Utilizing a Rng Generation with Heterogeneous Idss, often known as RNGHID, is advised. There are several distinct industries and regions that will make up the Web of Things economy. In order to detect any nodes within the netwo

关键词： Computers Protocols Surveillance Organizations Software Safety Internet of Things

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MTMixAD: Metric-Trace Mixed Anomaly detection Framework for Microservice systems with Limited and Mislabeled Data 3rd

MTMixAD: Metric-Trace Mixed Anomaly Detection Framework for...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Tang, Yixiang Tan, Zhuanglun Chen, Jian Yao, Xi Zhong, Guoxiang Liu, Fagui School of Computer Science and Engineering South China University of Technology Guangzhou China Peng Cheng Lab Shenzhen China

ISBN: (纸本)9789819645022

Given the significant security challenges posed to microservice systems in cloud computing, efficient anomaly detection is crucial for maintaining reliable microservice systems. Most existing anomaly detection methods require significant amounts of accurately labeled monitoring data or pure normal data. However, in the real-world scenario, the volume of labeled data is usually limited due to high cost, and the data often contains mislabeling noises owing to false alarms. To address the above problem, we propose MTMixAD, a microservice anomaly detection framework with limited and mislabeled monitoring data. Specially, MTMixAD uses Mixup for data augmentation, confident learning to filter mislabeled data, and a novel loss function to enhance model discrimination. Additionally, MTMixAD integrates metrics and trace data from microservice systems to better capture system characteristics. Experiments verify the effectiveness of our method and each part of MTMixAD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection

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LIDS: Enhancing Industrial IoT Network Security through Lightweight Machine Learning-Powered Intrusion detection system 3rd

LIDS: Enhancing Industrial IoT Network Security through Ligh...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Peng, Silin Han, Yu Zhou, Nan Liang, Xiaojun Deng, Wenfeng Gui, Weihua Pengcheng Laboratory Shenzhen China Sun Yat-Sen University Shenzhen China Central South University Changsha China

ISBN: (纸本)9789819645022

In recent years, security landscape of Industrial Internet of Things (IIoT) has emerged as a pivotal area of concern within the realms of cyberwarfare and engineering research, particularly after the rise of advanced and sophisticated malware (e.g., Stuxnet) deliberately crafted to target such systems. Although artificial intelligence has achieved a great success in the field of intrusion detection, the limited computing capabilities and storage of IIoT devices hinder the actual deployment of high-complexity models. In this paper, a lightweight intrusion detection system (LIDS) empowered by machine learning technologies is proposed to detect intrusions in an effective and efficient manner to enhance network security of IIoT. Firstly, we apply a wrapper-based approach via LightGBM model to identify the most important features. Features with the highest importance scores are effectively selected and constructed as the optimal feature subset. Then, we develop a advanced model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) for intrusion detection, where the important parameters of XGBoost are optimized and fine-tuned using BO algorithm to improve the detection accuracy, computational efficiency, and stability of the model. Experimental results on NSL-KDD, a benchmark dataset to demonstrate that the proposed method achieves an accuracy of 99.79% in binary classification and 99.84% in multi-classification, showcasing its potential as a reliable security solution for IIoT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Proceedings of 3rd 2023 international conference on Autonomous Unmanned systems (3rd ICAUS 2023) 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Yi Qu Mancang Gu Yifeng Niu Wenxing Fu

ISBN: (数字)9789819710836

ISBN: (纸本)9789819710829;9789819710850

This book includes original, peer-reviewed research papers from the 3rd ICAUS 2023, which provides a unique and engaging platform for scientists, engineers and practitioners from all over the world to present and share their most recent research results and innovative ideas. The 3rd ICAUS 2023 aims to stimulate researchers working in areas relevant to intelligent unmanned systems.;Topics covered include but are not limited to: Unmanned Aerial/Ground/Surface/Underwater systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial intelligence Algorithm/Bionic technology and their Application in Unmanned systems.;The papers presented here share the latest findings in unmanned systems, robotics, automation, intelligent systems, control systems, integrated networks, modelling and simulation. This makes the book a valuable resource for researchers, engineers and students alike.

关键词： Control, Robotics, Mechatronics Computational intelligence Communications Engineering, Networks Artificial intelligence

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Proceedings of 3rd 2023 international conference on Autonomous Unmanned systems (3rd ICAUS 2023) 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Yi Qu Mancang Gu Yifeng Niu Wenxing Fu

ISBN: (数字)9789819711031

ISBN: (纸本)9789819711024;9789819711055

关键词： Control, Robotics, Mechatronics Computational intelligence Communications Engineering, Networks Artificial intelligence

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