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Enhancing Cloud Resource Allocation Efficiency with Harris Hawk Optimization Algorithm(HHOA)

Enhancing Cloud Resource Allocation Efficiency with Harris H...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Manickavasagan, V. Petchiammal, Eswari Sudha, S Rahini Rajesh, C. Sree, S. Jindhiya Yamini, V.S. Sri Sai Ram Engineering College Dept. of Csbs Chennai India Velammal Institute of Technology Dept. of Information Technology Chennai India Panimalar Engineering College Dept. of Csbs Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Dept. of Artificial Intelligence and Data Science Chennai India Velammal Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350348460

The demand for efficient cloud resource allocation has surged with the growing reliance on cloud computing for a wide array of applications. Optimizing resource allocation in cloud environments is crucial for cost-effectiveness, performance, and energy conservation. In this context, bio-inspired optimization algorithms have gained prominence for their ability to address complex allocation problems. This paper investigates the application of the Harris Hawk Optimization algorithm (HHOA) to enhance cloud resource allocation efficiency. HHOA, inspired by the hunting behavior of Harris's hawks, is an efficient and versatile metaheuristic algorithm known for its ability to find optimal solutions in complex, dynamic environments. By harnessing the HHOA algorithm, this study aims to improve the allocation of resources such as virtual machines and storage within cloud infrastructures as demanded by the tasks. In our proposed approach we present a comprehensive examination of HHOA's suitability for cloud resource allocation and compares its performance with conventional optimization algorithms like Stimulated Annealing (SA), Genetic Algorithm (GA) and Tabu Search (TS). Through a series of experiments, we found that Harris Hawk Optimization Algorithm (HHOA) is proven to be very effective in improving operational efficiency by enhanced reliability, lowered latency and lowered average response time. © 2024 IEEE.

关键词： Cloud computing GA HHOA Resource allocation Resource optimization Tabu search

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Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images 31

Active Learning Based Domain Adaptation for Tissue Segmentat...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Fuster, Saul Khoraminia, Farbod Eftestøl, Trygve Zuiverloon, Tahlita C.M. Engan, Kjersti Dept. of Electrical Engineering and Computer Science University of Stavanger Stavanger4021 Norway Dept. of Urology Erasmus MC Cancer Institute University Medical Center Rotterdam3015 GD Netherlands

ISBN: (纸本)9789464593600

Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image analysis, as the visual characteristics of tissues can vary significantly across datasets. Yet, acquiring sufficient annotated data in the medical domain is cumbersome and time-consuming. The labeling effort can be significantly reduced by leveraging active learning, which enables the selective annotation of the most informative samples. Our proposed method allows for fine-tuning a pre-trained deep neural network using a small set of labeled data from the target domain, while also actively selecting the most informative samples to label next. We demonstrate that our approach performs with significantly fewer labeled samples compared to traditional supervised learning approaches for similar F1-scores, using barely a 59% of the training set. We also investigate the distribution of class balance to establish annotation guidelines. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Tissue

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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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An Overview of Protocols-Based Security Threats and Countermeasures in WLAN 4

An Overview of Protocols-Based Security Threats and Counterm...

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4th International Conference for Emerging Technology, INCET 2023

作者： Ojha, Nitish Kumar Baray, Elyas Indian Institute of Information Technology Dept. of Information Technology Allahabad211012 India Sharda University Knowledge Park - III Dept. of Computer Science and Engineering Greater Noida201310 India

ISBN: (纸本)9798350335750

WLAN security protocols are the most important section for wireless networks which controls all the security-related issues by securing the network with some pre-defined rules made by wireless-protected access organizations, further which makes it the main target of hacking and threats by interested parties and to be safe from such threats. There should be countermeasures for such threats where there will be a solution for any threat encountered and to mention. WIFI or wireless local area Network is a cost-effective easy deployment Network that makes users take advantage of vast data shared on the internet and by the network without the need for any cable for connections. Here in this overview paper by reviewing many researchers' techniques and works on the WLAN security topic, there are shared threats to WLAN and the ways of taking precautions against those threats as countermeasures and for which the tables are used. For comparison of techniques used by authors and some famous and highly dangerous threats are also defined further. WPA3's new security protocol is also defined with its newly found vulnerabilities and countermeasures except there is a conclusion on the downgrade attack problem with WPA3. Further, there are also countermeasures for that as well, nowadays people think the network is secured with standards defined by the IEEE organization but some know this is not the case so it makes it a more important area for research. © 2023 IEEE.

关键词： Wireless local area networks (WLAN)

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Study of the contextual factors of a project affecting the build performance in Continuous Integration

Study of the contextual factors of a project affecting the b...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Benjamin, Jetty Mathew, Juby Jose, Rubin Thottupurathu Amal Jyothi College of Engineering Dept. of Computer Applications Kerala Kanjirappally686 518 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India Amal Jyothi College of Engineering Dept. of Computer Science and Engineering Kerala Kanjirappally686 518 India

ISBN: (纸本)9798350303452

Continuous Integration (CI) has a significant and substantial impact on software engineering. CI optimize the development processes, enhance the code quality and facilitate a collaborative environment resulting in the effective release of high quality software features to the end users. The performance and outcome of CI build is a major concern in CI environment. In this paper we address the impact of contextual factors of software projects that can influence the performance of build in Continuous Integration/Continuous Deployment (CI/CD) pipeline. We studied 588 large-scale GitHub projects that uses the Travis CI build system. An experimental study is conducted on these projects to leverage the history of prior build success and failures. Three ML models are trained on the data. The aim of the study was to identify the contextual factors of a project, influencing the performance of build. We used Logistic Regression, Decision Tree and Extreme Gradient Boost model to predict the build outcome. We identified that the contextual factors has a significant impact and the study will further helps to develop techniques to preemptively detect when a CI build environment is most likely to trigger a failure. © 2023 IEEE.

关键词： Decision trees

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Analysing Breast Cancer Classification Using Explainable Artificial Intelligence

Analysing Breast Cancer Classification Using Explainable Art...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Kazi Fardeen Harshadeep Bhaduri Soham Hazra Praloy Mondal Sarbajit Manna Tapas Si Dept. of Computer Science & Electronics Ramakrishna Mission Vidyamandira Howrah West Bengal India Dept. of Computer Science & Engineering University of Engineering & Management Jaipur Rajasthan India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Worldwide, breast cancer is becoming the most serious illness that affects women. It is believed that early diagnosis and treatment of breast cancer can increase survival rates and decrease the need for surgery. Machine Learning model is very reliant on features for their proper training. However, understanding how a prediction is being affected by specific features is very important for a model’s interpretation. Understanding what features support a prediction is important as it provides some transparency to the inner workings of the model. Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are used to classify the breast cancer data, and accuracy, sensitivity, specificity, false-positive rate, precision, F 1 -score, and Geometric-Mean (GM) are used for the performance assessment. Furthermore, Multi-Criteria Decision Making (MCDM) is used to evaluate overall performance assessment based on the aforementioned performance measures and DT is found to be best among all the classifiers. Finally, an Explainable AI model namely LIME is used to interpret the predicted outcomes and impact of the different features of the data on the model’s prediction.

关键词： Support vector machines Radio frequency Analytical models Accuracy Explainable AI Predictive models Nearest neighbor methods Breast cancer data models MCDM

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)

作者： Md. Sariful Islam Md Tusher Ahmad Bappy Jubayer Ahmed Shawon Mehedi Hasan Wahidur Rahman Lija Akter Mir Mohammad Azad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pretrained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models' performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients.

关键词： Cataracts Measurement Deep learning Accuracy Image color analysis Static VAr compensators Feature extraction Real-time systems Residual neural networks Principal component analysis

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A K-means Clustering Based Under-Sampling Method for Imbalanced dataset Classification

A K-means Clustering Based Under-Sampling Method for Imbalan...

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International Conference on Information Networking

作者： Chih-Ming Huang Chuan-Sheng Hung Yao-Yuan Hsu You-Cheng Zheng Cheng-Han Yu Chun-Hung Richard Lin Shi-Huang Chen Dept. of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Dept. of Computer Science and Information Engineering Shu-Te University Kaohsiung Taiwan

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

This paper explores the challenges of applying imbalanced datasets to machine learning models. There is a significant disparity in the quantities of different classes, leading to biases in the learning process. Consequently, many models favor predicting the majority class to enhance accuracy, neglecting potentially crucial minority class data. This bias results in an overly optimistic perception of predictive outcomes, leading to erroneous decision-making. This paper proposes a classification sampling method that solves the issue of original imbalanced data. The process is divided into two main parts. The first part involves data preprocessing, utilizing the K-Means algorithm to cluster majority class data. Additionally, it replaces the traditional Euclidean distance with the Hellinger distance as the similarity metric for data clustering. The second part utilizes the representative data obtained in the previous step as training input, thereby reducing the imbalance between different classifications. Finally, experimental results using the imbalanced Kawasaki Disease (KD) dataset, with an imbalance rate of 64.35%, demonstrate that the proposed Hellinger method improves Precision (PPV) by 20.2% compared to the XGBoost method when Recall is above 90%. This effectively addresses the classification bias of traditional learning methods towards imbalanced data, enhancing predictive outcomes for imbalanced datasets. This approach holds potential applications in medical disease prediction, financial fraud detection, and other related domains in the future.

关键词： Measurement Training Accuracy Finance Machine learning Predictive models Sampling methods

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Detection of Mid Mesial Canal in Dental CBCT Images Using Combined YOLO and 2D U-Net Model

Detection of Mid Mesial Canal in Dental CBCT Images Using Co...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madhuri, J. Mohan, Shreya Praveen, Sneha Chandan, Tanvi Reddy Gurram, Tanusha Anitha Kumari, R. Krishna, Soumya Bangalore Institute of Technology Dept. of Computer Science and Engineering Bangalore India V. S. Dental College and Hospitals Bangalore India

ISBN: (纸本)9798350300826

Failures in root canal treatments often arise from undetected canals and a limited comprehension of the intricate structure of the root canal system. An illustrative instance is the Middle Mesial Canal (MMC), situated between the mesiobuccal and mesiolingual canals of the mesial root of the first mandibular molar. Neglecting to identify and address these canals in the molar tooth can lead to subsequent treatment complications, resulting in persistent infection. The primary objective of this study is to evaluate the potential of three-dimensional dental Cone Beam Computed Tomography (CBCT) scans in detecting the presence of mid mesial canals through a deep learning approach. The 3D dental CBCT images are segmented into two-dimensional cross sections and processed to ascertain the occurrence of MMC. To achieve this, a deep learning Convolutional Neural Network (CNN) algorithm, known as YOLO (You Only Look Once), an object detection algorithm, and a segmentation algorithm called 2D U-Net are employed. The results obtained from YOLO reveal a confidence score of 0.89, a precision of 0.945, a recall of 0.902, and a Mean Average Precision (MAP) of 0.988, while the 2D U-Net achieves an accuracy of 0.9772. © 2023 IEEE.

关键词： Convolutional neural networks

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Framework for Cloud-Based data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Harshith Pallapothu Gopi Krishna G Digvijay Sunil Teja Yadav S Nagasundari S Dept. of Computer Science & Engineering PES University Bengaluru India Dept. of Computer Science & Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bengaluru India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs.

关键词： Cloud computing Connected vehicles Web services Vehicle safety Propagation losses Real-time systems Reliability Autonomous vehicles Payloads Predictive maintenance

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