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The Innovative Cloud Security Environment for Data Privacy

The Innovative Cloud Security Environment for Data Privacy

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Divya Rani, M.S. Rajesh, A. Agarwal, Ankita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

The use of online computing has grown quickly across a range of institutions and businesses. The storage of information in the clouds, which enables individuals and businesses to store their personal information largely in the cloud and not on local computers, is one of the main benefits of cloud computing. Safety of data maintained in the cloud is still a major worry, however. We provide a novel security architecture in this study that is intended to improve data confidentiality and safety. Data is separated into blocks of two bytes each, and each multi-byte bit sequence is subjected to a separate evolutionary process in the revised security framework. The end product of each genetic algorithm's operation is ciphertext, which is made up of two different sets of bits. The ciphertext for these encrypted messages is difficult to find since it is stored in many places across the cloud. Additionally, the newly developed safety architecture uses algorithms that evolve on fewer data blocks to improve data security. A capability list is also used to guarantee granular and secure access to information. © 2023 IEEE.

关键词： access control Cloud security cloud service providers (CSPs) data confidentiality data encryption data privacy data protection Genetic Algorithm (GA) Internet of Things (IoT) security architecture

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AI-Powered Developments in Oncological Diagnostic Imaging: Revolutionary Approaches to Cancer Treatment

AI-Powered Developments in Oncological Diagnostic Imaging: R...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Singh, Shweta Ashwini, B. Vivek, V. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

This project aims to demonstrate new uses of deep learning techniques in the detection of cancer. A huge body of examples that show the desired behaviour may be used to train a system using deep learning, which is a family of algorithms. Introduction of deep learning methods to tumour imaging may help pathologists identify and categorise cancer early on so that patients can get the right therapies to improve their chances of surviving or recovering. Utilising statistics and other analytical techniques and ScienceDirect have data, it appears that since the late 1990s, a sharp increase in research on deep learning technologies in medical imaging has been motivated by the exceptionally high mortality rates of some cancer types in order to address the concomitant issues of a more precise recognition and the identification of cancer types to use effective therapies for cancer. This study also identifies the institutions, nations, and intellectual subject areas with the greatest levels of intellectual output in multiple disciplines as the origins of the trajectory of deep learning technology in cancer detection. According to Amara's law, this novel technique may result in a change in the way that cancer diagnoses and treats its many forms. When they can submit digital photos to laboratories in other developed nations to have malignancy kinds diagnosed, this fresh innovation may also aid developing regions, narrowing the present healthcare divide as much as feasible. © 2023 IEEE.

关键词： Cancer Detection Cancer Types Deep Learning Early Diagnosis Machine Learning Medical Imaging Metastatic Cancer Pathologists and Clinical Oncology Therapies Tumour Imaging

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Optimization of Medical Staffing Costs in Hospital Emergency departments

Optimization of Medical Staffing Costs in Hospital Emergency...

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2023 International Automatic Control Conference, CACS 2023

作者： Horng, Shih-Cheng Lin, Shieh-Shing Yeh, Hsiu-Cheng Chaoyang University of Technology Department of Computer Science & Information Engineering Taichung Taiwan St. John's University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Health system managers should determine how to choose the medical staff configuration in each service within the constraints concerning the mean waiting time of all patients. Hospital managers wish to increase the utilization of available resources secured from their health services budget. Therefore, managers must construct highly efficient systems that can reduce staffing costs while providing the required level of care, such as maximizing patient satisfaction and minimizing waiting time. In this research, an algorithm embedding ordinal optimization into beluga whale optimization is developed to resolve the optimal medical staff configuration in emergency departments. Beluga whale optimization is a nature-inspired approach that depicts the behaviors of swimming, foraging, and whale falling of beluga whale populations. The developed algorithm is used to find the optimal medical staff configuration such that waiting times are minimized. Experimental results reveal that the developed algorithm has the capability of providing competitive and promising results. © 2023 IEEE.

关键词： Budget control

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ElephantGuardNet: A Comprehensive Solution for Elephant Conservation and Human-Elephant Conflict Reduction 4

ElephantGuardNet: A Comprehensive Solution for Elephant Cons...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Naveen, S. Dhamotharan, R. Sahebzathi, S. Krishnan, R. Santhana Kumar, V. Vinoth Sundaravadivel, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mechanical Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NVIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence. © 2025 IEEE.

关键词： Drones

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Review of Metaheuristic Techniques for Feature Selection 8th

Review of Metaheuristic Techniques for Feature Selection

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Jain, Sanat Jain, Ashish Jangid, Mahesh Department of Information Technology Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811998577

Due to the rise of high-dimensional datasets in various sectors, feature selection is one of the most important issues. The primary goal of the feature selection problem is to lower the dimension of the feature set while retaining the performance accuracy. Several techniques of feature selection have been developed to obtain the optimal subset of features. To obtain optimal features, metaheuristics are modern optimization techniques that are used by the research community. In this paper, four groups of metaheuristic techniques have been identified based on their behavior. The classifier name, datasets, and assessment metrics for the metaheuristic methods used to solve the feature selection challenges are provided. After reviewing the papers, difficulties and problems are also observed while trying to use various metaheuristic methods to find the best subset of features. For those researchers who desire to continue their work on creating or refining metaheuristic techniques for feature selection, several research gaps are also mentioned. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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An Enhanced Keylogger Detection Systems Using Recurrent Neural Networks Enabled with Feature Selection Model 7th

An Enhanced Keylogger Detection Systems Using Recurrent Neur...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Rodríguez, Demóstenes Zegarra Akanji, Boluwatife Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Computer Science Federal University of Lavras Minas Gerais Lavras Brazil

ISBN: (纸本)9789819764648

Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. They can steal sensitive information like credit card numbers and passwords. They pose a significant threat to users’ privacy and security since they capture keystroke data. Keylogger detection systems play a vital role in safeguarding users from cyberattacks and mitigating the potential harm caused by keyloggers. Keylogger detection systems employing deep learning have been widely used for identifying and mitigating cyber threats associated with keyloggers. However, they struggle to identify new or unfamiliar keylogger samples. This study aims to explore deep learning techniques with feature selection model to detect keylogger. This study employs Recurrent Neural Networks (RNNs) using a collection of known keylogger dataset. Furthermore, a correlation-based feature extraction method was applied to identify the most relevant features for the model, highlighting the importance of specific features, such as URG flag Count, ACK flag count, and Idle Mean, in differentiating between benign and keylog classes. The proposed model demonstrates superior accuracy of 0.8763 and precision of 0.8569 compared to a baseline model using Logistic Regression (LR) 0.7721 and 0.7407, respectively, indicating a better balance between accurate classification and minimizing false negatives and false positives. The findings from this study help to improve keylogger detection methods, making them more powerful and efficient. These results suggest that a keylogger-based detection system employing a deep learning approach can be a valuable tool for addressing the complex and evolving landscape of cyber threats related to keylogging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Classifying Clinically Important Cancers Using Deep Belief Networks

Classifying Clinically Important Cancers Using Deep Belief N...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Jain, Nikita Kamalraj, R. Agrawal, Ajay College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

This paper offers a technique to categorize an expansion of clinically critical cancers using a deep perception community (DBN) technique. The DBN version became educated with transcriptome datasets from most human cancer mobile strains to generate a set of rules for different sorts and ranges of cancers. Inside the DBN model, every schooling sample was encoded with a set of features, along with gene and isoform expression information. The entered statistics are then handed thru the layers of DBN that generate a probabilistic inference of the samples based totally on the relationships among features and output values. The mistake and misclassification rates were evaluated using leave-one-out cross-validation, with an average accuracy of ninety two.2% This method provides a speedy and computationally inexpensive manner to classify differing types and ranges of cancer, which is of specific importance for early detection and diagnosis in medical care. Deep notion Networks (DBNs) are machine-mastering algorithms that use more than one layer of neural networks to analyze complex styles from statistics. DBNs are specifically beneficial for classifying clinically-essential cancers, as they allow for the correct and effective detection of several cancerous cells. DBNs obtain this through skilled layers of statistics to extract precise features from datasets, along with pictures of the cancerous cells or biomarkers of metabolic pathways. Using those extracted capabilities, DBNs can correctly distinguish between every day and cancerous cells and which sort of cancers the cells constitute. With a greater understanding of cancerous cells, medical practitioners can higher diagnose and treat a ramification of cancers, mainly to improve affected person care.. © 2024 IEEE.

关键词： cancer features ranges schooling version

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Android Malware Detection System using Machine Learning 2024

Android Malware Detection System using Machine Learning

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16th International Conference on Contemporary Computing, IC3 2024

作者： Kaur, Amanpreet Lal, Sangeeta Goel, Shruti Pandey, Mrinal Agarwal, Astha Department of Computer Science and Engineering & IT Jaypee Institute of Information Technology India School of computing and mathematics Keele University Newcastle-under-Lyme United Kingdom

ISBN: (纸本)9798400709722

Detecting Android malware is imperative for safeguarding user privacy, securing data, and preserving device performance. Consequently, numerous studies have underscored the complexities associated with Android malware detection, prompting a multidimensional approach to tackle these challenges effectively. This research leverages machine learning techniques, emphasizing feature extraction, classification algorithms, and both supervised and unsupervised learning methodologies. The exploration begins with in-depth Exploratory Data Analysis (EDA) to gain insights into the dataset, paving the way for informed decision-making. Principal Component Analysis (PCA) is employed for dimensionality reduction, a pivotal step in handling the multivariate nature of the data. The integration of API calls, clustering, and anomaly detection further enriches the model's capability to discern between benign and malicious applications. Crucially, the study delves into the intricacies of sampling, evaluation, and the Confusion Matrix to quantify the model's performance accurately. The utilization of diverse classification algorithms, including Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), Random Forest, GaussianNB, Decision Tree, and Logistic Regression, underscores the comprehensive nature of the approach. These algorithms collectively contribute to a robust and versatile Android malware detection model capable of adapting to varying threat scenarios. The dataset employed for training and evaluation is sourced from Kaggle, encompassing 29,999 Android applications categorized as benign or malicious based on permissions sought. Current detection methods, deemed resource-intensive and exhaustive, face the challenge of keeping pace with the relentless evolution of new malware strains. This research seeks to address this gap by proposing a sophisticated, machine learning-driven model that not only enhances accuracy but also demonstrates efficiency and adaptability in the face

关键词： Android malware

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Cleanliness Follow-up using Deep Learning

Cleanliness Follow-up using Deep Learning

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Chaudhari, Feni Patel, Vishrutkumar Patel, Riya Patel, Jaimin Khamar, Krinal Trivedi, Himani Vidush Somany Institute of Technology and Research Department of Computer Science and Engineering Gujarat India Vidush Somany Institute of Technology and Research Department of Information Technology Gujarat India Vidush Somany Institute of Technology and Research Department of Computer Engineering Gujarat India Ldrp Institute of Technology and Research Gujarat India

ISBN: (纸本)9798350308167

Garbage collection is a challenging task which require tremendous efforts and manpower. This research proposes automatic garbage collection system with the help of the CCTV cameras present on the street. The proposed work includes generation of handcrafted dataset having images of garbage piles as per requirement. Deep Learning models viz. YOLOv5n, YOLOv7-tiny and YOLOv8n are employed to form a system which detects the garbage and send alert to the relevant authorities. © 2023 IEEE.

关键词： Automation Cleanliness Deep Learning Garbage detection

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Understanding the Journey to 6G Development

Understanding the Journey to 6G Development

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agrawal, Trapty Kumar, Ajit Gowrishankar, J. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The fifth-generation (5G) mobile communication technology is now being widely deployed in a number of nations, along with a significant rise in the number of 5G customers. However, it is becoming more and more clear that it is time for both academics and business to shift their emphasis towards the next generation as the mobile telecommunications market continues to change. At this point, it is crucial to provide a thorough review of the state of the art and look forward to the future of communications. This article tries to achieve this goal by providing a thorough analysis designed to outline the sixth generation (6G) system's characteristics. The motivations, use cases, use scenarios, needs, key performance indicators (KPIs), architecture, and enabling technologies that will characterize 6G are all covered in this investigation. © 2023 IEEE.

关键词： 6G AI architecture disruptive services enabling technologies generation Key Performance Indicators (KPIs) latency mobile communication mobile traffic mobility network efficiency peak data rate performance indicators technology terahertz communications use cases user-experienced data rate

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