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Attribute Inference on Multilevel Attributed Networks

Attribute Inference on Multilevel Attributed Networks

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Yaddala Pavan Kumar Reddy L R Deepthi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multilevel attribute inference for attributed networks is essential in network analysis with applications in various fields such as social networks, biological networks, and recommender systems. Attributed networks contain structural information expressed as network topology and attribute information characterized by node-level features. The task of multilevel attribute inference is to predict the missing attribute values of a node based on the network structure and the attributes of neighboring nodes. This paper employed various machine learning algorithms: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest, Logistic Regression, Naive Bayes, and Feed Forward Neural Network (FFNN) to predict the missing attributes at all levels. A novel hierarchical inference algorithm, the HierarInfer algorithm, which exploits attributed network properties to infer attributes at multiple levels, was proposed in this paper. Through experiments, it is seen that our algorithm outperforms existing methods regarding attribute prediction accuracy.

关键词： Support vector machines Machine learning algorithms Accuracy Social networking (online) Predictive models Prediction algorithms Inference algorithms Graph neural networks Recommender systems Random forests

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Pedestrian Intention Detection for Autonomous Driving: A Novel Object Detection Approach

Pedestrian Intention Detection for Autonomous Driving: A Nov...

引用

International Conference on Electrical Energy Systems (ICEES)

作者： A Padmavathi Dheeraj Reddy Pullela Shaik Nazeer Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

Detecting pedestrian intentions is crucial for safer interactions between autonomous driving and pedestrians. This study addresses this challenge by proposing an object detection model for autonomous driving using YOLOv8. Our model identifies pedestrians and categorizes their intentions as "waiting" or "crossing" based on bounding box analysis. Utilizing YOLOv8, the model recognizes pedestrians in real-time video streams and interprets their actions to determine their intentions. The model's performance is evaluated through metrics such as precision, recall, and F1 score, achieving high accuracy. This approach enables proactive decision-making based on pedestrian intentions, significantly enhancing autonomous driving and driver assistance system safety. The results highlight the model's potential to improve pedestrian safety in traffic, contribute to autonomous driving technology, and benefit connected vehicles.

关键词： Pedestrians Laser radar Connected vehicles Transfer learning Measurement by laser beam Object detection Real-time systems Safety Autonomous vehicles Streams

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End-To-End Encrypted Agriculture Chatbot

End-To-End Encrypted Agriculture Chatbot

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Pennabadi Devendra Reddy K Satya Sampath Reddy P Jayanth C R Kavitha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture sector is considered to be one of the most prominent sectors in the Indian economy. Farmers constantly face challenges such as a lack of information on best practices, disease detection, and market information. An End-to-End Agriculture chatbot can solve this problem by providing real-time information and support to farmers to enhance their productivity. This paper introduces a secure End-to-End Agriculture chatbot designed with strong encryption techniques to ensure data security. The proposed system will use the different asymmetric algorithm, a well-established public key cryptography method, to establish a safe and secure platform. Users sensitive data like soil reports and crop information will be encrypted using a widely available public key before sending it to the chatbot. After then, the chatbot uses its safe private key to decode the data. The asymmetric encryption method makes sure that the data being sent can only be accessed by authorized parties.

关键词： Productivity Crops Soil Chatbots Public key cryptography Agriculture Real-time systems Encryption Faces Diseases

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Improving Obfuscated Malware Classification: A Machine Learning Approach with Feature Selection and Data Balancing

Improving Obfuscated Malware Classification: A Machine Learn...

引用

International Conference on Advanced computer Control, ICACC

作者： Gundu Bhavana Mokshagna Bhuvan Chowdary Pudota S Saravanan K Deepak Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Obfuscated malware refers to malicious software that has been deliberately modified to hide its malicious purpose and avoid detection by conventional security mechanisms. Classifying obfuscated malware poses a significant challenge due to its deceptive nature and the dynamic techniques used by attackers to obfuscate code. Existing approaches in malware analysis often struggle to accurately classify obfuscated malware, leading to potential security vulnerabilities and data breaches. In this research, we investigate the application of machine learning algorithms for malware analysis, specifically focusing on multi-class classification of obfuscated malware using the CIC-MalMem-2022, a latest dataset. We apply data preprocessing techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and normalization, to enhance the dataset's balance and consistency. Subsequently, we train and evaluate various machine learning algorithms, including Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), LightGBM, and Decision Trees (DT), on different datasets comprising SMOTE, normalized, and SMOTE+normalized instances. Our study also explores the effect of feature selection process on the performance of the model. We identify the top 35 features for optimal accuracy, addressing the dimensionality challenge inherent in large-scale datasets. Our findings reveal that the Random Forest (RF) model achieves the highest accuracy of 96.23% on the dataset that is balanced by SMOTE with top 35 features.

关键词： Radio frequency Accuracy Machine learning algorithms Scalability Nearest neighbor methods Feature extraction Solids Malware Random forests Testing

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Revolutionizing Morse Code Security: Dual-Layer Encryption with AES and Machine Learning

Revolutionizing Morse Code Security: Dual-Layer Encryption w...

引用

Communication & computing (WCONF), World Conference on

作者： Samudrala Sai Santhoshi Haneesha Poluru Reddy Jahanve Siwani Karna Kavitha C. R. Department of Computer Science and Engineering Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Morse code is a method used in telecommunication to encode text characters as sequences of two different signal durations, called dots and dashes, or dits and dahs. It is a well-known, traditional method of communication that is frequently connected to radio transmission and telegraphy. In today's world, to protect sensitive data from cyber-attacks and maintain privacy and integrity in digital conversations, transactions, and storage, encryption and decryption are essential. These procedures provide security and build user confidence in this age of cybercrime by ensuring that their data is protected from unauthorized access. Advanced Encryption Standard (AES) is a popular encryption model which to date has been trusted for its effectiveness in safe-guarding sensitive data in digital communications and storage. AES Modern innovations also include other new mechanisms like - artificial intelligence (AI) to support pattern recognition, which later reduces the need for human decryption efforts for decryption and also improves the speed and efficiency of decryption of encrypted data. Various techniques such as artificial intelligence or machine learning are also used in decryption techniques. In the current interconnected digital environment, this integration contributes not only to enhancing the efficiency of encrypting but also allows for faster decryption of data for accurate and timely transmission of data. To make this research more realistic, data is transmitted between two systems using a wireless LAN connection. Besides this, the evaluation of these models is done based on the time taken for the encryption process and decryption process to ensure the efficiency is optimally vested in real life.

关键词： Wireless LAN Data privacy Telegraphy Technological innovation Codes Accuracy Machine learning Data models Encryption Wearable devices

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Scrutinization of Threats from a Cloud-based Network Traffic Environment

Scrutinization of Threats from a Cloud-based Network Traffic...

引用

International Conference on computing for Sustainable Global Development (INDIACom)

作者： Shraddha Chopra A. G. Sreedevi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

In this age of digitalization, securing computer networks is of utmost importance to protect sensitive data and critical infrastructure. Threat scrutinization plays an integral role in identifying and thwarting cyber risks. This study focuses on a pioneering strategy to bolster the network’s reliability by harnessing Artificial Intelligence (AI) techniques in conjunction with cloud computing infrastructure, particularly AWS SageMaker. The focus lies in leveraging a comprehensive and realistic cyber dataset, CSE-IDS-2018, to assess the efficacy of the proposed model. The study commences by architecting a threat scrutinization mechanism that harnesses the feasibility of machine learning algorithms, deep learning models, and cloud computing resources. Specifically, the Random Forest algorithm is employed for the multi-class classification of seven types of attacks within the CSE-CICIDS2018 dataset, facilitated by AWS SageMaker’s machine-learning capabilities.

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Enhancing Multimedia Security through Advanced Image Authentication and Encryption

Enhancing Multimedia Security through Advanced Image Authent...

引用

International Conference for Convergence of Technology (I2CT)

作者： Kotha Chakradhar K Thulasi Tharun Shinu M. Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

With this flood of multimedia data, protecting it is an absolute necessity. This paper describes a complete method for securing images that combines encryption and watermarking. A technique of the sort suggested further prolongs picture encryption, by interweaving a Rubik's idea with advanced encryptions like those based on Triple DES and AES. As further, a brand new strong watermarking algorithm is displayed. In this way, a high degree of protection for the copyright and genuineness is hoped to be achieved. The proposed method has encouraging results ensuring harvested photographs 'accessibility, confidentiality and integrity.

关键词： Limiting Authentication Watermarking Real-time systems Hybrid power systems Encryption Reliability

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Thyroid Disease Prediction Using Machine Learning Techniques

Thyroid Disease Prediction Using Machine Learning Techniques

引用

Electronic Systems and Intelligent computing (ICESIC), International Conference on

作者： Sompalli Pranath Kumar Pasuladi Narsimha Reddy V Nivethitha Suthir Sriram Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Thyroid disorders are primarily observed in people in the age range of 30 to 60 years; however, the disease is said to be very prevalent in women. Some of the risk factors for thyroid disorders include other diseases that are autoimmune in nature, a family history of thyroid disease, and past exposure to radiation of the neck. More correctly, thyroid diseases are meaningful metabolic disorders and determinants of health. Toward this end, this study used a large dataset from the patient medical record to predict the onset of thyroid disease. The models that are used in this work are K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. Further each of the models’ performances is also assessed based on recall, accuracy, and precision. The MLP model developed a good predictive ability in comparison with the other models, along with an accuracy of 99.4 percent. Therefore, this study puts forward a very important message concerning the vital importance of machine learning.

关键词： Support vector machines Logistic regression Accuracy Predictive models Nearest neighbor methods User interfaces Bayes methods Medical diagnostic imaging Diseases Thyroid

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Advancing Ophthalmic Diagnostics: Employing Deep Learning for Precision Detection of Retinal Damage in OCT Images

Advancing Ophthalmic Diagnostics: Employing Deep Learning fo...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： M Rithani R Prasanna Kumar Veda Chatiyode Janani Srinivasan Anusha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Retinal optical coherence tomography imaging is an important diagnostic tool for early detection and monitoring of various retinal disorders. This study aims to improve diagnostic accuracy by leveraging deep learning methods such as VGG16 and InceptionV3 models to classify retinal OCT images automatically, ultimately enhancing ocular diagnostics and clinical decision-making. Utilizing a dataset comprising approximately 85,000 high-resolution images across primary retinal condition categories including diabetic macular edema (DME), age-related macular degeneration (AMD), Choroidal Neovascularization (CNV), and healthy retinas, the efficacy of these deep learning models is assessed in this comprehensive study. Results demonstrate that VGG16 excels in CNV classification and InceptionV3 is proficient in identifying normal retinal conditions. Comparative analysis underscores their distinct strengths, with both models achieving an impressive overall accuracy of 92.76%. These findings demonstrate the potential of deep learning for automating OCT image interpretation and enhancing ocular diagnostics, thereby improving patient outcomes and clinical decision-making resulting in advanced healthcare practices.

关键词： Deep learning Accuracy Optical coherence tomography Decision making Refining Medical services Network security Retina Monitoring Diseases

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Deciphering Poxvirus Strains: Feature-Driven classification of Monkeypox

Deciphering Poxvirus Strains: Feature-Driven classification ...

引用

Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Daripelli Mehul Sasikala. D Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

This research outlines a method for monkeypox virus classification from images, emphasizing the effective management of augmented datasets to ensure balanced class distribution and provide a robust foundation for model training. The study harnesses the capabilities of advanced pre-trained deep learning models, specifically ResNet50, EfficientNetB3, and DenseNet169, utilizing transfer learning techniques to adapt them to the task at hand. Techniques such as batch normalization and dropout are used to reduce overfitting and improve the generalization capacity of the models. Then, using the optimizer and categorical cross-entropy loss, these models are fine-tuned for monkeypox virus classification. One noteworthy addition is the inclusion of interactive learning, which gives users dynamic control over the training process by enabling them to intervene at predetermined intervals. Performance metrics such as accuracy, confusion matrix are used in the evaluation of the model. In order to guarantee a trustworthy evaluation of the models’ performance, an independent test dataset is utilized. The most impressive accomplishment is the ensemble learning method, which combines the predictions from all three models,ResNet50,EfficientNetB3 and DenseNet169 with a simple averaging procedure. The classification report shows that this all-encompassing methodology consistently produces excellent results, with precision, recall, and F1-score consistently exceeding 0.97. In a time when healthcare challenges are constantly changing, this method shows promise as a useful tool for image-based diagnosis.

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