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共 25464 条记录，以下是151-160 订阅

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Network-Centric Malware Detection Using Dynamic Packet Analysis and Hybrid Architectures 1

Network-Centric Malware Detection Using Dynamic Packet Analy...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Raj, B.U. Naveen Utpal Raj, K.B. Srujan, V. Rajagopal, Shinu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The rapid evolution of malware necessitates advanced detection methodologies to address the limitations of traditional signature-based approaches, which often fail against zero-day malware and polymorphic attacks. This study introduces a dynamic packet analysis framework, leveraging fine-grained network features such as packet length and time-to-live (TTL) for real-time detection. A hybrid architecture, combining Gradient Boosting and Random Forest models, improves detection capabilities to 95.7% with robustness against adversarial attacks. The model's effectiveness in analyzing control flow traces outperforms traditional methods by 26.3% in detecting obfuscated malware and maintains an 83% success rate against adversarial threats. This approach not only sets a new benchmark in adapting to evolving cybersecurity challenges but also provides scalable and resilient malware detection. © 2025 IEEE.

关键词： Zero-day attack

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MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm 3rd

MRI-Based Brain Tumour Detection and Classification Using Ra...

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3rd International Conference on Intelligent Systems and Sustainable computing, ICISSC 2023

作者： Jareena Begum, D. Chokkalingam, S.P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783540

A brain tumour develops when cells in the brain multiply abnormally and out of control. The possibility of fatality makes this growth dangerous. The brain regulates everything from memory to vision to emotions. Recognizing these tumours is a challenging and complex task due to their location, size, and shape. There have been a number of successful attempts to enhance detection. However, the current level of precision is insufficient. This study details a method with a high likelihood of success in identifying brain cancers. The approach is implemented in Python LAB by use of an image segmentation technique and a classifier. One such classifier is the Random Forest Algorithm. The Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT) are also employed. The created method is put to the test using a dataset available on the Kaggle platform. The acquired results were approximately 97.68% accurate. A confusion matrix and a comparison of the developed method to other research in the literature are included in the investigation to facilitate the implementation of the proposed strategy in image testing. This Research shows that the offered system is superior to others in terms of detection rate, false positive rate, and recall. Results from this study showed that the developed method was effective at detecting brain tumours due to its high levels of accuracy, precision, and recall. According to the proposed strategy, it is essential to provide professionals who diagnose brain tumours with access to such technology. © The Author(s) 2025.

关键词： Image segmentation

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Evolving GAN-BERT Architecture for Efficient Text Categorization with Minimal Labeled Data 8th

Evolving GAN-BERT Architecture for Efficient Text Categoriza...

引用

8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Bagavathi, C. Raakesh, M. Bhanu, Chanamala Vipasyana Reddy, Katheram Venkata Sai Ram Sathwik, Kancharla Rupesh Sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031736162

Categorical text generation is an upcoming field within natural language processing focusing on producing text tailored to specific categories or domains. The goal of categorical text production is to produce text that complies with the requirements and characteristics of the chosen category while also adhering to the grammatical and structural conventions of the language. Text classification works across multiple data categories, and the synthetic data generated usually adheres to a preset pattern and ignores the text’s nuanced emotional content. The proposed approach begins with using BERT to get the text’s deep contextual embeddings. Using these text embeddings, the underlying semantics of the target category can be better understood by adding a task-specific layer that places the sentences in the closest corresponding class. After that, the architecture and settings of the GAN are optimized through evolutionary computing techniques to guarantee that the generated text conforms to the constraints and characteristics specific to each category. © IFIP International Federation for Information Processing 2025.

关键词： Generative adversarial networks

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Development of an Adaptive Smart Toy with Emotional Feedback 3

Development of an Adaptive Smart Toy with Emotional Feedback

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, Gayathri Sanay Santh, V. Padmakumar, Keshav Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Traditional toys often do not adapt to the emotional and physical needs of children, particularly those who may have limited verbal communication abilities. The project’s goal is to create a smart toy that is adaptive and can communicate with a child by using nonverbal signs to express his or her physical and emotional states. There are sensors like accelerometers to detect movement, a gesture sensor indicating light intensity, a buzzer sensor to produce sound, and an IR sensor to detect the child’s hand when the child is near the toy. All these components will help the user to get in-time feedback with a set of visual indicators (LEDs) and tactile feedback in the form of vibration motors. This smart toy seeks to improve the experience of play and developmental support for children by being more responsive and engaging with individual needs. © 2025 IEEE.

关键词： Intelligent robots

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From Pixels to Truth: Unveiling Deepfakes with a Meso-5 and XceptionNet Fusion Network 5th

From Pixels to Truth: Unveiling Deepfakes with a Meso-5 and ...

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5th International Conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Abhinandhan, S. Sreedevi, A.G. Saranya, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819773701

The growth of deepfakes, or hyper-realistic synthetic media, demands effective detection algorithms to ensure the validity of visual information. This study presents a unique deepfake detection model based on a hybrid architecture that takes advantage of the characteristics of both Meso-5 and XceptionNet. Meso-5, which is well-known for its capacity to efficiently capture mesoscopic picture characteristics, is carefully paired with Xception-Net’s outstanding feature extraction capabilities. This novel strategy tries to achieve higher deepfake detection accuracy than previous approaches. We assess the proposed model’s performance using the publicly available Deepfake Detection Challenge (DFDC) dataset, which was published by Facebook in 2018. Our results show that this hybrid technique works well, with an impressive accuracy of 81.24% in detecting deepfakes. These findings demonstrate our model’s ability to mitigate the deepfake danger and build a more dependable future for visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deepfake detection DFDC dataset Hybrid architecture Hyper-realistic synthetic visuals Image detection Meso-5 Visual threat XceptionNet

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Speech Emotion Recognition Using Machine Learning 5th

Speech Emotion Recognition Using Machine Learning

引用

5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Veeramaneni, Asritha Samitha, V. Charitha, Talluri Shriya, Sagi Singh, Tripty Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819747108

Speech emotion recognition (SER) is a technique for accurately determining a person’s emotion from their speech. Enhancing communication between people and machines. Even though emotions are arbitrary, it can be hard to annotate audio and it’s still hard to gauge someone’s emotional state. It now plays a crucial part in technological research as new technologies for human machine or medical applications are developed. It is obvious that surprise and rage feelings are more accurately predicted by our model than other emotions, It is understandable given how significantly the pitch, speed, and other qualities of the audio files for these emotions differ from those of other audio files. The system can capture and analyze various acoustic elements contained in voice signals using the SVM-based architecture and enabling the identification and categorization of various moods. Mel frequency cepstral coefficients (MFCCs), pitch, energy, and spectral features are some of these features, and they all work together to reveal important details about the emotional content that is present in speech. The deployed models attain a remarkable accuracy of 80% in identifying various emotional states from speech data after extensive training and optimization. This level of accuracy shows how well the suggested method works in properly classifying emotions based on auditory characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

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

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 IEEE.

关键词： CNNs Deep learning Early detection Feature extraction GLCM Local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

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Leveraging Machine Learning to Predict Obesity Trends: Insights from Lifestyle and Biometric Data

Leveraging Machine Learning to Predict Obesity Trends: Insig...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Adapala, Rishi Manikanta Kancharla, Yaswanth Shaik, Baba Ameer Jyotsna, C. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bangalore India

ISBN: (纸本)9798331512965

This is increasingly important as global obesity rates continue to rise: the causes need to be understood, and the ability to predict trends determined. In this study, we use machine learning techniques to predict obesity trends given lifestyle and biometric data from a large obesity dataset. Our methodology starts with data preprocessing that includes transforming categorical variables into numerical variables and standardizes the numerical variables into such a form so that machine learning algos can work with them properly. Supervised machine learning models, such as tree based methods, neural networks and various ensemble techniques are applied, and evaluated. For determining the most effective predictive approach, these models are evaluated by several performance metrics such as accuracy, precision, recall, and F1 score;the best model is chosen based on these metrics. In addition, the study includes explainable AI technique (SHAP - SHapley Additive exPlanations;LIME - Local Interpretable Model-Agnostic Explanations) to make the results more interpretable. The results from these methods provide insight into how different lifestyle and biometric factors contribute to the prediction of obesity;such as physical activity, diet patterns, and body mass index. The results from this work demonstrate not only how machine learning can improve prediction accuracy but also how these methods can provide actionable insights into the many contributing factors to obesity. © 2025 IEEE.

关键词： Self-supervised learning

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Rack Sensor Metrics Odyssey: Analyzing Sensor Behavior with Random Forest and kNN for Strategic Job Allocation Insights

Rack Sensor Metrics Odyssey: Analyzing Sensor Behavior with ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： George, Angelina Beena, B.M. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering India

ISBN: (纸本)9798331529833

Data centers are vital to modern computing but contribute significantly to carbon emissions due to their high energy consumption. To address this, machine learning models can optimize resource allocation by analyzing sensor metrics. In this study, the Random Forest Regressor was chosen for its ability to capture sensor behavior, even showing negative R2 values, aligning with observed patterns. Meanwhile, k-Nearest Neighbors (kNN) emerged as the best model for learning from the same data, despite irregularities, with R2 values ranging between 0.6 and 1. This analysis supports sustainable data center management by enhancing energy efficiency and minimizing environmental impact. Future work will explore the integration of these models into simulation environments. © 2025 IEEE.

关键词： Carbon sequestration

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IoT-based Environmental Monitoring System 3

IoT-based Environmental Monitoring System

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhiram, D. Dhanush Reddy, K. Koushik Reddy, P. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This project meets the current important demand for the constant monitoring of environmental conditions because climate change and human activities affect ecosystems. Temperature and humidity are measured using the DHT11/DHT22 sensor connected to the ESP8266 microcontroller and the GPS module is used to obtaining the location information. In contrast to current approaches targeting the communication data or energy consumption only, the contaminants of this system are unified, location-based, and central. It provides immediate data transfer to the cloud through a comfortable low power interface for usage in farming, transportation, and more for environmental monitoring. The findings show that all the environmental conditions and location change can be monitored by the IoT-based system which use ESP32/Arduino microcontroller for sensing data by a Bluetooth or Wi-Fi through cloud platform or server and it gives alert message to web interface or mobile application when temperature increases or decreases more than threshold values. This practical system is especially valuable for those areas of interest that require a constant check, such as environmental control or the logistics and health safety of the transported goods or passengers. © 2025 IEEE.

关键词： Environmental monitoring

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