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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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License Plate Detection using YOLO v8 and Performance Evaluation of EasyOCR, PaddleOCR and Tesseract 15

License Plate Detection using YOLO v8 and Performance Evalua...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, P Praneeth Shruthi, P Sai Himanshu, P. Singh, Tripty Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

License plate recognition is critical to the functioning of the control and surveillance systems. Vehicle identification is based on number plates, which are made up of a certain arrangement of letters and digits. However, manually identifying every number plate is a laborious and difficult task. The majority of earlier automatic number plate recognition (ANPR) systems were restricted in how they could operate. These conditions included being limited to fixed illumination, background noise in the vehicle image. The primary goal of this project is to develop a reliable number plate recognition model that functions with the help of YOLO v8 for license plate segmentation followed with a comparison model of EasyOCR, PaddleOCR, Tesseract models based on best model for character recognition of license plate. The steps involved include processing of vehicle image, segmentation of license plate from the image, followed by internal segmentation of characters on the plate and pattern matching. Data pre-processing is finally performed in order to read and process the license plate. Problems like background noise in image like characters apart from the license plate and number-based characters being wrongly predicted as alphabets have been tackled. © 2024 IEEE.

关键词： Image segmentation

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Transcribing Natural Language to Python Code: A Compiler Based Approach 15

Transcribing Natural Language to Python Code: A Compiler Bas...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Coding in a programming language can indeed be a meticulous and a less interesting task, especially when compared to the fluidity of spoken or written communication. Each programming language has its own set of syntax and rules, demanding expertise to effectively write code in it. Through this study, a compiler based approach to translate Natural Language into Python code is proposed. This endeavor could revolutionize access to programming by making it more intuitive and inclusive. The research involves working with different phases of the compiler including the lexical analyser, syntax and the semantic analyser phase in combination with Natural Language Processing techniques. By automating this process, we aim to streamline programming for a wider audience, removing barriers to entry and making coding more accessible to all. © 2024 IEEE.

关键词： Code Generation Compiler Architecture Compiler-Based Translation Natural Language Processing (NLP) Semantic Understanding

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Machine Learning-driven Antenna Selection Strategies for Fading MIMO Systems 2

Machine Learning-driven Antenna Selection Strategies for Fad...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice. © 2024 IEEE.

关键词： Adversarial machine learning

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Unmasking Emotions: Deep Neural Networks for Image-Based Emotion Recognition 2

Unmasking Emotions: Deep Neural Networks for Image-Based Emo...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Sohel, Mohammad Aman Madhukar, Raghupatruni Sai Batchu, Sai Muralidhar Jyotsna, C. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350327533

Recognizing human emotions is a multifaced field that uses the technology, especially deep learning and Convolutional neural networks, to recognize and understand emotions such as happiness, sadness, anger, disgust and fear. Convolutional Neural Networks are designed for visual data explorations and they are effectively capture facial expressions for emotion recognition. The process involves the collection of labeled data, and also preprocesses it, creating an appropriate deep neural network, training the model with constant updates, and assessing its performance using metrics on a testing dataset. Proposed work tackles emotion detection in irregularly distributed datasets of preprocessed 48x48 grayscale emotion images. Significant performance gains are achieved through targeted data augmentation for marginalized classes, giving valuable insights for robust emotion recognition models. Optimizing hyperparameters enhances the model, enabling its deployment in real-world scenarios by ensuring integration, data handling, and real-Time functionality. The proposed model primarily focuses on offline image and video capturing for emotion detection, and highlighting on video-emotion recognition. © 2024 IEEE.

关键词： Emotion Recognition

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aDBF: an autonomous electromagnetic noise filtering mechanism for industrial environments 32

aDBF: an autonomous electromagnetic noise filtering mechanis...

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Herrero, Angel C. Sanguesa, Julio A. Martinez, Francisco J. Garrido, Piedad Calafate, Carlos T. University of Zaragoza Dept. of Computer Science and System Engineering Teruel Spain Universitat Politecnica de Valencia Dept. of Computer Engineering Valencia Spain

ISBN: (纸本)9798350336184

The use of proprietary systems in Industry 4.0 often involves high economic costs. To address this issue, using low-cost devices with similar capabilities is becoming an increasingly popular alternative. However, these devices are prone to suffer the negative effects of electromagnetic interference (EMI) due to their placement in electrical panels alongside other electromechanical devices. To solve this problem, this article presents the autonomous Data Base Filter (aDBF). aDBF is an enhanced electromagnetic interference filtering mechanism capable of eliminating erroneous signals generated by EMI. aDBF has been specifically designed to autonomously (i.e., without the need for operator supervision or intervention) determine both the number of different product types elaborated in a production line, and the time instants when their manufacturing process starts and ends. In particular, aDBF goes through three stages: (i) pre-filtering, (ii) product change detection, and (iii) identification of valid signals. The results obtained after validating our proposal in three different manufacturing shifts demonstrate that the aDBF filtering mechanism works very accurately, as the maximum error introduced is of 0.93%. © 2023 IEEE.

关键词： Signal interference

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A Hybrid Prediction Model for Disseminating the Factors Related to Anomaly Detection in Software Defined Networks 15

A Hybrid Prediction Model for Disseminating the Factors Rela...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Darshan, S. Radhika, N. Radhika, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

The domain of network traffic and anomaly reporting has its highlights in recent days towards real-time applications. The utilization of network data with its correlating attributes and its measures must be considered before deriving a pattern or decision making process. SDN(Software Define Networks) in turn has been facilitating more towards the efficient and reliable mechanism for data control and network management system. This research focus on the development of a hybrid prediction model for disseminating the factors that contribute towards anomaly detection in SDN. The experimentation can be made by hybridizing different classification schemes with tuned parametric components upon statistical modelling and evaluation. The proposed hybrid model is suggested up with the hybridization of SVM, NN along with Ada Boost algorithm with tuned parameter metrics. From the proposed model it is evident that the selected features will play a significant key role in anomaly prediction in a given network. © 2024 IEEE.

关键词： Prediction models

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Security and Threat Detection through Cloud-Based Wazuh Deployment 1

Security and Threat Detection through Cloud-Based Wazuh Depl...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Majid, Abdul Basit, Abdul Ebrahim, Mansoor Abro, Abdul Ahad Naeem, Mehwish Iqra University Dept. of Computer Science Karachi Pakistan Dept. of Cyber Security Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan Iqra University Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9798350373042

In today's interconnected business landscape, security is the cornerstone of all organizations, irrespective of their size or industry. This research emphasizes the critical need to safeguard organizational assets from cyber threats and highlights the pivotal role that firewalls play in fortifying network and cyber systems. The challenge lies in maintaining updated firewall defenses to prevent server downtime and the circumvention of security measures. To address these concerns, this study introduces a novel approach by integrating Cloud Wazuh, a user-friendly solution tailored for non-technical personnel, providing streamlined deployment and customizable security rules. Leveraging Wazuh suite of tools, particularly the Host-Based Intrusion Detection System (HIDS), the research detects and mitigates potential threats, including patterns associated with attacks such as Port Scanning, Denial of Service (DoS), and Metasploit. Additionally, Wazuh offers a range of features, from Security Analytics to File Integrity Monitoring, to comprehensively protect against cyber threats. While implementing Cloud Wazuh may entail initial costs, it proves cost-effective compared to maintaining a dedicated IT security team. This research underscores how Cloud Wazuh deployment serves as a pivotal tool for organizations looking to strengthen their security measures, safeguard their data, and ensure uninterrupted operations in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： Cybersecurity

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Emotion Recognition in Images: CNN-based Classification of Happy and Sad States 15

Emotion Recognition in Images: CNN-based Classification of H...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Tripty Duraisamy, Prakash Reddy, Karukonda Nithin Reddy, Mettukuru Tharun Yeswanth, Kathi Venkata Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The difficulty of recognizing emotions from pictures is a real problem for mental health evaluation and human-computer interaction. In this study, it is suggested that the use of Convolutional Neural Networks (CNNs) can help identify emotions in photographs focusing on classifying happy and sad states. The CNN architecture was trained using a dataset made up of images depicting people in various emotional states ranging from joy to sadness. After extensive testing and analysis, our model discerns between happiness and sadness. So, we investigate how the emotion recognition system performance could be maximized by trying out different CNN designs, data augmentation techniques as well as hyperparameters. Moreover, part of this chapter looks at the interpretability of the model's decisions and also discusses implications and further research directions into emotion recognition with deep learning mechanisms. On the whole, our work advances emotion recognition technology and highlights how CNN-based techniques can aid in understanding human emotions through visual cues. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Evaluation of AES, DES, RSA, and Paillier Homomorphic for Image Security 9

Performance Evaluation of AES, DES, RSA, and Paillier Homomo...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bhavitha, Medikonda Rakshitha, K. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the current digital era, it is crucial to preserve sensitive data, particularly when it comes to images. Encrypting images is essential for ensuring the confidentiality and integrity of image data both in transit and storage. To address this need, this exploration design delves into the operation of four important encryption ways Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest-Shamir-Adleman (RSA), and the Paillier homomorphic algorithm. Regarding the security of image data, each of these approaches has a distinct set of benefits and features. This study aims to explore their use in image encryption and decryption and assess their performance, security, and practicality in safeguarding images from unauthorized access and malicious threats. © 2024 IEEE.

关键词： Cryptography

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