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Minimization of Energy Consumption in Cloud 3

Minimization of Energy Consumption in Cloud

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Kirti Smriti, Manu Shaily, Kumud Sudha, Danthuluri CMR Institute of Technology Department of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9781665494007

With advancement in technology and the internet Cloud computing is becoming more popular and hence the number of datacentres is increasing which causes the amount of power consumption to increase and more cooling equipment are required. How to efficiently distribute Virtual Machines (VMs) to the Physical Machines (PMs) or hosts in order to conserve power is the task. There are numerous existing energy-conscious methods which concentrate on reducing energy use in a cloud environment. Here two models, a gradient regression-based method and a maximum percentage of correlation-based method are proposed;these can significantly minimize use of energy in cloud computing. CloudSim is a simulation tool for simulating cloud computation environments. The VM migration policy has been modified to give more energy efficient results. © 2023 IEEE.

关键词： Electric power utilization

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XAI: An Explainable AI Model for the Diagnosis of COPD from CXR Images 2

XAI: An Explainable AI Model for the Diagnosis of COPD from ...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Ikechukwu, Agughasi Victor Murali, S. Maharaja Institute of Technology Mysore Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798350314168

Chronic Obstructive Pulmonary Disease (COPD) is a major health concern worldwide, the third leading cause of death. However, it often goes unnoticed until it reaches severe stages. Although spirometry tests are the definitive method for COPD diagnosis, their accessibility is limited in under-resourced regions. In contrast, Chest X-rays (CXRs) are more universally available, suggesting their potential utility as preliminary screening tools for COPD. This study applies deep learning algorithms to the large-scale VinDR-CXR dataset to identify early-stage COPD using CXRs. The research employs the ChestX-ray14 dataset for model pre-Training and then utilizes VinDR-CXR for model development and validation. Through transfer learning, it was observed that the Xception base model with a recall of 98.2% obtained via fine-Tuning was better than the ResNet50 model. Grad-CAM (Gradient Class Activation Mapping) heatmaps and SHAP (SHapley Additive exPlanations) offer explainability for true positive cases on the Xception model from both datasets. The findings underscore the potential of DL models in facilitating early COPD detection via CXRs, especially where spirometry remains less accessible. Further exploration into multimodal-based COPD diagnosis is recommended for subsequent research. © 2023 IEEE.

关键词： Large datasets

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Anomaly Detection in the Cloud Environment with Clustering Optimization Model for Attack Detection in IDs

Anomaly Detection in the Cloud Environment with Clustering O...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Jain, Ashima Tripathi, Khushboo Jatain, Aman Manju Amity University Department of Computer Science and Engineering Haryana Gurugram India Jaypee Institute of Information Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350302820

In recent decades the role of the Internet in day-to-day activities are rapidly increased due to the accessibility and completion of tasks with in fraction of a second, as well as the banking and billing process proceeds online. Due to the character of openness in networks, security is the big threat to users. The intruders enter into the network in different manners to get access or change the original contents as well as block the connection, etc. The intrusion detection (IDS) process is a mandate to overcome the issues, the IDS is classified as misuse detection as well as anomaly detection. This paper constructed a framework for anomaly detection in the cloud environment. The constructed framework model uses the Rule-based Bayesian Optimization (RBBO) for the attack and malicious activities detection. The RBBO model uses the weighted rule-based approach with the estimation of mean and standard deviation in the dataset attributed to the attack and anomaly detection. Based on the generated rules the conditions are provided within the Bayesian Network for the dimensionality reduction. Through implementation of the Bayesian Network classification of attacks are performed for the KDD cup and real-time dataset. The comparative analysis expressed that the RBBO model performance is effective for the detection of anomalies and attacks in the cloud with the reduced false positive rate. © 2023 IEEE.

关键词： Bayesian networks

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Real-Time Employee Monitoring System using CCTV Cameras

Real-Time Employee Monitoring System using CCTV Cameras

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Harshavardhan, G. Singh, Nongmeikapam Thoiba Chanu, Asem Debala Chandigarh University Department of Computer Science and Engineering Punjab India Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350319200

In this approach, we detail an approach to monitoring employees using preexisting setups such as CCTV camera feeds and a network connection. With this approach, we try to lower the system requirements to make it available to a wide range of setups. By relying on a software-based approach, employers can try it out and decide whether they wish to continue to subscribe, unlike a hardware-based approach, where they are locked onto the plan after the installation of the hardware. This approach will be modular in nature, where certain steps can be swapped out for more effective ones. It entails security personnel watching live CCTV footage. Instead of looking for anomalies, they might occasionally examine employees to ensure they are working, thus acting as remote supervisors. This function can be automated to create a more streamlined approach. This approach will predominantly use an employee's estimated position to predict whether they are working. This will depend on the type of job that they have. Through frequent sampling at regular intervals, we can estimate the amount of time an employee has spent working. This is a measure of the time spent working and not an employee's productivity. Though this model provides a basis for further derivative models, it has things that can be improved. One such factor would be to add further evaluation metrics to make the model more precise. © 2023 IEEE.

关键词： binary classifier CCTV Camera pose estimation real time employee monitoring technology (REMT)

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Efficient Prediction and Analysis of Monkeypox Skin Lesion: A Comparative Study for Web based Application 2

Efficient Prediction and Analysis of Monkeypox Skin Lesion: ...

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Gupta, Piyush Mittal, Uday Jha, Tushar Agarwal, Mini Tiwari, Ajay Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350322064

After the coronavirus pandemic, another deadly disease, known as monkeypox, began to spread. This was so alarming that the World Health Organization declared it a global public health emergency. As of 31st January 2023, there have been 85449 cases of monkeypox reported in 110 countries worldwide. It is difficult to diagnose because its symptoms resemble those of chicken pox, small-pox, etc. The clinical diagnosis of monkeypox is performed using the Polymerase chain reaction (PCR) test, which takes a considerable amount of time to determine the result. Any non-clinical test that could aid in identifying monkeypox in suspected patients would be advantageous. Various deep learning models were found to be useful for this purpose, provided sufficient training data is available. We've used already existing datasets and extended them to have a sufficiently large dataset. This dataset is then fed to pre-trained deep-learning models, including ResNet50, EfficientNetB3, InceptionV3, and MobileNetV2. It has aided in comparing the accuracy of these models. Based on our analysis, we have employed best performing model i.e., EfficientNetB3 in a web application with a user-friendly interface to interact with it. © 2023 IEEE.

关键词： Polymerase chain reaction

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Detection of Asperger Syndrome at Early Stages in Children using Deep Neural Networks 8

Detection of Asperger Syndrome at Early Stages in Children u...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Battu, Vishnu Vardhan Vinay Kumar, B. Boddapati, Chaitanya Sreya Chaparala, Pujitha Chava, Sri Harsha Damisetty, Leela Hemanth Pvp Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350396638

ASD (Autism Spectrum Disorder) is behavioral and considered to be a developmental disorder found in human beings. It includes disorders in brain development, eventually affecting functional brain capabilities. It also affects how a person converses with others and causes problems in interaction and communication. The vast range of symptoms, abilities, and functioning levels in ASD patients is known as the "spectrum."It is essential to diagnose ASD at early stages so that autistic child can reach their full *** some cases, parents cannot accept that their child's mental growth is not developing along with physical development. With early diagnosis, children with autism can lead fulfilling and joyful lives through the provision of appropriate medication. Screening tests are used to diagnose this type of disorder but are time-consuming and costly. This research study has employed a convolutional neural network to detect the likelihood of ASD in young children at an early stage. Deep Neural Networks function well in many applications. This study has used Xception and Resnet50 model to predict ASD based on static facial images, especially in children. According to the results obtained, ResNet50's performance is better than Xception in Autism detection. © 2023 IEEE.

关键词： Deep neural networks

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Hybrid Cryptographic Algorithm to Secure Internet of Things 7

Hybrid Cryptographic Algorithm to Secure Internet of Things

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Amala Nikitha, G. Kathrine, G. Jaspher W. Duthie, Christina Rajesh Ebenezer, V. Silas, Salaja Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

Small IoT devices can only support light protocols since they lack significant processing or storage power. Lightweight encryption standards or techniques will be utilized for encryption and decryption to prevent hackers from accessing the data in order to increase the security of IoT devices' network connections and commands. The encryption has to be robust enough to safeguard and lightweight enough to enable light processing hardware. There aren't many standards for lightweight cryptography that are used to safeguard devices in an existing network. If the network is not secured properly, an intruder can get in and take control of the devices without making a visible appearance. A hybrid lightweight cryptographic methodology have been proposed to address this problem. LWC algorithms are designed to provide strong security while minimizing the computational and memory resources required for encryption and decryption. This is particularly important for IoT devices, which often have limited processing power, storage capacity, and battery life. LWC algorithms offer a range of benefits for IoT devices, including improved security, reduced power consumption, and increased battery life. They are also designed to be easy to implement and integrate with existing IoT platforms and protocols. Proposed system's analytical work implementation will combine Salsa20 and AES algorithms to provide greater security and making it well-suited for real-time cryptography. © 2023 IEEE.

关键词： Internet of things

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Comparative Analysis of Machine Learning Techniques for Mental Health Prediction 8

Comparative Analysis of Machine Learning Techniques for Ment...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Naveen Paul, E. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350396638

The prevalence of mental health problems has prompted investigations into the use of machine learning to tackle the issues. Mental health is a crucial component of an individual's overall well-being and can be detected and treated early on, significantly improving the quality of life for those affected. This study examines the use of machine learning algorithms to predict mental health disorders using a dataset of self-reported information. Four commonly used machine learning models K-nearest neighbor classifier, logistic regression, random forest and decision tree are compared in terms of their performance. The objective of this study is to compare the performance of these machine learning algorithms on a self-reported mental health dataset and identify the most suitable model for predicting mental health. The challenges faced by the system include the limited size and quality of the dataset, the need for ethical considerations in handling sensitive mental health information, and potential biases in the *** results of the experiments identify the most suitable model for predicting mental health. © 2023 IEEE.

关键词： Decision trees

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Generalized Unique Reconstruction From Substrings

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IEEE Transactions on Information Theory 2023年第9期69卷 5648-5659页

作者： Yehezkeally, Yonatan Bar-Lev, Daniella Marcovich, Sagi Yaakobi, Eitan Institute for Communications Engineering School of ComputationInformation and Technology Technical University of Munich Munich80333 Germany Department of Computer Science Technion-Israel Institute of Technology Haifa3200003 Israel

This paper introduces a new family of reconstruction codes which is motivated by applications in DNA data storage and sequencing. In such applications, DNA strands are sequenced by reading some subset of their substrings. While previous works considered two extreme cases in which all substrings of pre-defined lengths are read or substrings are read with no overlap for the single string case, this work studies two extensions of this paradigm. The first extension considers the setup in which consecutive substrings are read with some given minimum overlap. First, an upper bound is provided on the attainable rates of codes that guarantee unique reconstruction. Then, efficient constructions of codes that asymptotically meet that upper bound are presented. In the second extension, we study the setup where multiple strings are reconstructed together. Given the number of strings and their length, we first derive a lower bound on the read substrings' length that is necessary for the existence of multi-strand reconstruction codes with non-vanishing rates. We then present two constructions of such codes and show that their rates approach 1 for values of that asymptotically behave like the lower bound. © 2023 IEEE.

关键词： Digital storage

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The Probabilistic and Geometric Approach to Multivariate Logistic Regression 10

The Probabilistic and Geometric Approach to Multivariate Log...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Malhotra, Rahul Hirani, Nishit Panda, Prabhat Dwivedi, Pulkit Chandigarh University Department of Computer Science and Engineering Gharuan India Apex Institute of Technology Chandigarh University Department of Computer Science and Engineering Gharuan India

ISBN: (纸本)9789380544472

This paper gives an overview of the two approaches to implement the Multivariate Logistic Regression. There is the Geometric way and the Probabilistic way. The aim of this paper is to establish that both the approaches effectively produce the same result and the Probabilistic approach can be converted to the geometric approach with the help of some mathematical manipulation. The paper is to prove mathematically that the geometric approach which divides the classes using hyperplane and the Probabilistic method that uses the log of odds ratio to divide the classes are essentially the same and the Probabilistic approach with some mathematical manipulation can essentially be converted to the equation of hyperplane. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Geometry

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