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

作者： Adithya Rajendran S Abhishek Akhbar Sha T Anjali Department of Computer Science and Engineering Amrita School of Computing Amritapuri India

Lung cancer, a complex and formidable disease, frequently necessitates surgical intervention as a pivotal aspect of its treatment. Accurate prediction of post-operative life expectancy holds paramount significance in the realm of clinical decisionmaking and patient counselling This study undertook a rigorous comparison of various machine learning algorithms, encompassing Support Vector Classifier (SVC), Logistic Regression, XGBoost, Random Forest, K-Nearest Neighbors (K-NN), and CatBoost. Using an expansive dataset containing clinical and demographic variables collected from lung cancer patients, the research team rigorously assessed the predictive capabilities of these models. The outcome of the research team analysis unveiled exceptional mean accuracy scores across the models, underscoring their utility in this context. Specifically, SVC, Random Forest, and CatBoost consistently exhibited remarkable mean accuracy rates, each reaching 0.864. Logistic Regression also demonstrated robust performance, achieving a commendable mean accuracy of 0.862, while XGBoost and K-NN delivered competitive mean accuracies of 0.846 and 0.84, respectively. These findings underscore the substantial potential of machine learning in furnishing precise predictions regarding post-operative life expectancy. By doing so, these models empower healthcare professionals to make well-informed treatment decisions, paving the path for the development of personalized treatment strategies and the enhancement of patient care in the management of lung cancer.

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Network Intrusion Classifier with Optimized Clustering Algorithm for the Efficient Classification

Network Intrusion Classifier with Optimized Clustering Algor...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Balajee R M Suresh Kallam M K Jayanthi Kannan Faculty of Engineering and Technology School of Computer Science and Engineering JAIN (Deemed to be University) Bangalore India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore Madhya Pradesh India

Nowadays network security is becoming more and more challenging due to increasing number of network attacks. The major attack which carried out on the network seems to be DoS attack, DDoS attack, Botnet Attack and Bruteforce Attack. The proposed research work will classify the incoming packets in the cloud environment (through network) as attack or non-attack packets with best clustering and cluster head optimization algorithm. The overall classified cluster lead to further classification on the specified four major attacks with the better accuracy and other network intrusion measures. There are sorne fixed techniques associated in this classification and those are Principal Component Analysis PCA for extracting the features and AutoFncoder (classifier algorithm based on deep-learning) for classifying the attacks after clustering as attack and non-attack group. This research includes a CSE--CIC--IDS dataset which had more than 1.8 crore packets. These are the packets which are having above four major attacks and also includes SQL Injection and Infiltration attacks in lesser proportionate. The motive behind this research is to find the best fit of clustering algorithm with a combination of efficient cluster head optimization technique with the fixed technique of PCA based feature reduction and deep learning based classifier on to and from of clustering mechanism for betterment of intrusion classification accuracy and other network measures.

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Exploring the Power of Deep Learning for Seamless Background Audio Generation in Videos

Exploring the Power of Deep Learning for Seamless Background...

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International Conference on computing and Networking Technology (ICCNT)

作者： Anjana S. Nambiar Kanigolla Likhita K. V. S. Sri Pujya Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

In the realm of image and signal processing, "data" refers to any piece of information that can be transformed into a format that can be efficiently processed. It is projected that by 2025, there will be 180 zettabytes of accessible data, based on previous estimates. Video processing plays a critical role in signal processing, and video filters are a key element of this process. In essence, video filters are akin to signal processing filters, with the distinction being that the input and output signals in this case are video streams. This is analogous to image processing. The importance of audio cannot be overstated, as it enhances audience engagement, raises production value, and brings video subject matter to life, while also disseminating information to viewers. This research behind the paper aimed to build and compare numerous deep classifier models that can generate background audio for a given input video file using the greatest hits dataset.

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Covid-19 Classification using Fine-tuned EfficientNet Architecture

Covid-19 Classification using Fine-tuned EfficientNet Archit...

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International Conference for Convergence of Technology (I2CT)

作者： Srigiri Sruthi Siddharth Emadaboina Pradyumna Machavarapu Rimjhim Padam Singh Sneha Kanchan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Malaysia

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This work utilizes an Efficient Net-based deep learning architecture, leveraging its efficiency and scalability for image classification tasks. We employ a Dataset of chest X-ray images, including COVID-19 cases as well as other relevant Pathology, to train and evaluate the model. The training process involves data augmentation, model optimization, and early stopping to ensure robust and generalizable performance. It specifies the image size, channels, and class count, and then proceeds to create a pre-trained model using the EfficientNetB0 architecture. The model is built upon the pre-trained EfficientNetB0 model, with additional layers including Layer Normalization, Dense, and Dropout layers for fine-tuning on the Covid-19 data. Subsequently, the model is rigorously evaluated using standard performance metrics, including precision, recall, F1-score, and accuracy, and has proven to outperform other state-of-art models by a 3% increase in F1-score value.

关键词： COVID-19 Training Pathology Scalability Data models Robustness Task analysis

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Brain Tumor Segmentation and Survival Prediction using Multimodal MRI Scans with Deep learning Algorithms

Brain Tumor Segmentation and Survival Prediction using Multi...

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2022 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Anjanayya, S. Gayathri, V.M. Pitchai, R. SRM Institute of Science and Technology School of Computing Tamilnadu Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Brain tumours developed by the brain's abnormal cell growth. Medical imaging devices are classified into two types: MRI and CT are widely used to scan brain tumors. MRI images are used to scan the interior components of the brain. There are two types of brain tumours: benign and malignant. A benign brain tumour is one that can be cured, whereas a malignant brain tumour is one that cannot be cured. Gliomas and astrocytomas are two types of malignant brain tumours. A radiologist using conventional procedures to detect and diagnose a brain tumour. Errors and delays are all too common. Because neurosurgeons generate a large number of brain tumour images, imaging technicians are unable to manually categorise and segment them. It is not suitable for densely populated emerging markets. As a result, computer-aided automatic brain tumour detection and diagnosis is preferred. Learning algorithms, such as neural networks, are hampered by a lack of hyperparameter modification, several local optima, and higher computing time. Optimizing network architecture and hyperparameters is critical for avoiding these challenges. Deep learning optimization approaches are commonly employed in healthcare. This research proposed using Deep Learning Optimization Algorithms to detect and diagnose Meningioma brain cancer. This intended study addresses the extraction, categorization, and diagnosis of tumor picture segments. © 2022 IEEE.

关键词： Learning algorithms

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LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming 36

LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based A...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Tashtarian, Farzad Bentaleb, Abdelhak Amirpour, Hadi Taraghi, Babak Timmerer, Christian Hellwagner, Hermann Zimmermann, Roger Alpen-Adria-Universitat Klagenfurt Christian Doppler Laboratory Athena Austria Concordia University Department of Computer Science and Software Engineering Canada National University of Singapore School of Computing Singapore

ISBN: (纸本)9781665477161

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session's bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE. © 2023 IEEE.

关键词： Bandwidth

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Ecoskinomics: Exploring the Ecological Factors Shaping Skin Health

Ecoskinomics: Exploring the Ecological Factors Shaping Skin ...

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2023 Innovations in Power and Advanced computing Technologies, i-PACT 2023

作者： Abhishek, S. Anjali, T. Lekshmi, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Dermatology Amrita Institute of Medical Sciences Amrita Vishwa Vidyapeetham Kochi India

ISBN: (纸本)9798350325188

This comprehensive research paper thoroughly investigates the profound implications of environmental factors on skin health. It meticulously examines the multifaceted contributions of pollution, UV radiation, climate change, and lifestyle choices to adverse skin conditions. The research elucidates the intricate processes driving oxidative stress, inflammation, DNA damage, collagen degradation, and immunosuppression generated by these factors through a comprehensive review of scholarly literature and empirical evidence. It underscores the critical importance of enhancing awareness, implementing evidence-based preventive strategies, and fostering lifestyle modifications to mitigate the detrimental effects and promote optimal skin health in an increasingly challenging environmental landscape. © 2023 IEEE.

关键词： Climate Change Hyperpigmentation Premature Aging Skin Disease UV Radiation

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AUTOTEE: Automated Migration and Protection of Programs in Trusted Execution Environments

arXiv

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arXiv 2025年

作者： Han, Ruidong Yang, Zhou Ma, Chengyan Liu, Ye Niu, Yuqing Ma, Siqi Gao, Debin Lo, David School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Engineering UNSW Sydney Australia

Trusted Execution Environments (TEEs) isolate a special space within a device’s memory that is not accessible to the normal world (also known as Untrusted Environment), even when the device is compromised. Thus, developers can utilize TEEs to provide strong security guarantees for their programs, making sensitive operations like encrypted data storage, fingerprint verification, and remote attestation protected from malicious attacks. Despite the strong protections offered by TEEs, adapting existing programs to leverage such security guarantees is nontrivial, often requiring extensive domain knowledge and manual intervention, which makes TEEs less accessible to developers. This motivates us to design AUTOTEE, the first Large Language Model (LLM)-enabled approach that can automatically identify, partition, transform, and port sensitive functions into TEEs with minimal developer intervention. By manually reviewing 68 repositories, we constructed a benchmark dataset consisting of 385 sensitive functions eligible for transformation, on which AUTOTEE achieves a high F1 score of 0.91. AUTOTEE effectively transforms these sensitive functions into their TEE-compatible counterparts, achieving success rates of 90% and 83% for Java and Python, respectively. We further provide a mechanism to automatically port the transformed code to different TEE platforms, including Intel SGX and AMD SEV, demonstrating that the transformed programs run successfully and correctly on these platforms. © 2025, CC BY-NC-SA.

关键词： Java programming language

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Machine Learning Approaches for Myers-Briggs Personality Prediction

Machine Learning Approaches for Myers-Briggs Personality Pre...

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

作者： Sethulakshmi Santhosh Meenakshi M Avani F S Abhishek Anjali T Department of Computer Science and Engineering Amrita School of Computing Amritapuri India

In order to predict Myers-Briggs personality types from text input, this research article compares the abilities of Stochastic Gradient Descent (SGD), Naive Bayes, k-Nearest Neighbours (KNN), and Logistic Regression models. The Myers-Briggs Type Indicator(MBTI) captures distinctive patterns of behaviour, cognition, and preferences of human personality. It divides people into one of sixteen personality types. This study assesses the accuracy, support, recall, precision, and F1-score for the chosen models using a dataset made up of text inputs and matching Myers-Briggs types. The project attempts to determine the most successful model among the evaluated ways for precisely predicting Myers-Briggs personality types through extensive experimentation and review. The findings of this study have applications in building accurate and reliable models for personality prediction utilizing natural language processing methods.

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Detection of the Crown of Thorns starfish using YOLOv5 8

Detection of the Crown of Thorns starfish using YOLOv5

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kunekar, Pankaj Patil, Pradnya Vishal Bbhagat, Babeetta Patil, Tejal H. Rajas, Neha Sawant, Pratiksha Yuvraj Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Pillai Hoc College of Engineering Computer Engineering Rasayani India Mit School of Computing Maharashtra Pune India Sinhgad College of Engineering Department of Ai&ds Maharashtra Pune India Vishwakarma Institute of Technology Department of Ai&ds Maharashtra Pune India PVG's College of Science and Commerce Department of Computer Science Maharashtra Pune India

ISBN: (纸本)9798350367904

The invasive Crown-of-Thorns starfish (COTS) poses a significant threat to coral reef ecosystems. Early detection and removal are crucial to mitigating their impact. This research proposes a YOLOv5-based object detection system to accurately identify and locate COTS in underwater images. By fine-tuning the YOLOv5 model with a dataset of COTS images, the system can effectively detect and classify COTS with high precision and recall. The output of the system includes bounding boxes and confidence scores for each detected COTS, enabling timely intervention and removal efforts. This research contributes to the conservation of coral reefs by providing a valuable tool for monitoring and controlling COTS populations. The proposed system can be integrated into underwater drones or diveroperated devices to facilitate efficient and accurate COTS detection and removal. © 2024 IEEE.

关键词： Reefs

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