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Strengthening Mobile Cloud Facilities: Techniques and Prospective Perspectives

Strengthening Mobile Cloud Facilities: Techniques and Prospe...

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： Anita Soni T. Kuppuraj Muntather Almusawi R. Udayakumar Saloni Bansal Shavnam Sharma Department of Computer Science & Engineering IES Institute of Technology and Management IES University Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Despite their quick surge in popularity and technical developments, mobile devices' limited battery life and computing power provide a serious barrier to meeting the growing needs of mobile consumers. In order to bridge the gap between customer expectations and the capabilities of mobile phones and tablets, a combination of well-established processes and cutting-edge approaches is necessary. Lately, efforts in research have been directed on using augmentation methods to improve mobile device performance. In the field of mobile cloud computing, augmentation strategies seek to maximize energy economy and processing capability via task delegation to more powerful computing resources. However, when using augmentation techniques, the heterogeneous and interrupted nature of portable internet-based computing architectures creates complications pertaining to computational management, energy saving, and system dependability. We also provide a summary of open problems and directions that might be explored in this area in the future.

关键词： Cloud computing Operating systems Energy conservation Memory computer architecture Mobile applications Telecommunication computing Internet of Things Wearable devices Smart phones

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Deep Learning Convolutional Neural Networks Classification Based on Brain Cancer MRI

Deep Learning Convolutional Neural Networks Classification B...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Diyar Qader Zeebaree Dilovan Asaad Zebari Imad Majed Zeebaree Mohammed Abduljabbar Mohammed Bafreen Abduljabbar Mohammed Nechirvan Asaad Zebari Department of Computer Network and Information Security Technical College of Informatic Duhok Polytechnic University Duhok Iraq Department of Computer Science College of Science Nawroz University Duhok Iraq Department of Health Management Information Systems Duhok Private Technical Institute Duhok Iraq Department of Mathematic College of Education University of Duhok Duhok Iraq

Brain tumor diagnosis and classification is a major challenge for medical field. The majority of the time, computer-Aided Diagnostic Systems (CADS) and methods involving Deep learning (DL) are utilized to find brain tumors quickly and precisely. Artificial diagnostic systems, on the other hand, are not very accurate, the physician is not using them very well when diagnosing Magnetic Resonance Images (MRI). By applying image processing principles, it is possible to conceptualize the different human tissue structures. It is difficult to detect diseased structures in the human brain using only the most fundamental imaging methods. In this research, we present a model-based Convolutional Neural Network (CNN) structure for identifying and classifying brain tumors. The proposed method consists of two parts: pre-processing and classification. In this study, CNN architecture outperformed in state-of-the-art methods, with 99.87% accuracy. F1-score 99.88%, 99.76% precision.

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Effective Side-Channel Attacks for Cryptographic Keys

Effective Side-Channel Attacks for Cryptographic Keys

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International Conference on Computing and Networking Technology (ICCNT)

作者： R Nishanthi Shivam Khurana S Mithun Kumar Leena A. Deshpande N. Thangarasu Lalit Khanna Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement UG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical summary examines effective aspect-channel assaults for cryptographic keys. Facet-channel assaults involve acquiring cryptographic keys or other personal statistics by observing patterns in the target gadget’s conduct or the environment it operates. Generally speaking, side-channel assaults awareness on measuring or analyzing the goal device’s bodily or electrical traits, which include power consumption, electromagnetic radiation, or statistics transfers. Such techniques are normally used to acquire the keys used to encrypt or decrypt information on an at-ease gadget, as well as many other sensitive secrets. This technical summary affords an overview of the most commonplace types of facet-channel attacks, as well as how they can be averted and strategically mitigated. It highlights recent studies on the subject, including new strategies for attack resilience and the growing sophistication of attackers.

关键词： Power demand Power measurement Side-channel attacks Organizations Electromagnetic radiation Timing Safety Cryptography Resilience Painting

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Machine Learning Techniques for Protein Structure Prediction in Bioinformatics

Machine Learning Techniques for Protein Structure Prediction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Abdiqayumovich, Aliyev Nurillo Jaisudha, J. Al-Farouni, Mohammed Khare, Neha Kaur, Gurmandeep Koduru, Siva Sai Fergana Medical Institute Of Public Health Assistants Of The Department Of Biophysics And Information Technologies Fergana Uzbekistan Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Saveetha Institute of Medical and Technical Sciences Dapartment of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

This work analyses and discusses the use of machine learning methods in the field of bioinformatics concerning protein structure prediction. The steps of the proposed methodology include: acquiring and cleaning the relevant proteins sequences datasets;and creation and training of deep learning models, including Convolutional Neural Networks (CNNs), and Recurrent Neural Networks, (RNNs). Overall, adding data augmentation and transfer learning boosts the performance of the models to a mean accuracy of 87%. Predictors Evaluation: In evaluating the predictors, the model used an accuracy of 6% and a Matthews correlation coefficient (MCC) of 0. 74. The generality and efficiency of the proposed model are tested and compared with results from other similar standard datasets. Study shows that the results of the model have higher reliability than the traditional method of the machine-learning algorithm especially in the classification of the second and third levels of varied types of proteins. The research will demonstrate the applicability and possibility of the large-scale and cost-efficient approaches to the protein structure prediction using machine learning, while emphasizing the drawbacks of experimental tools. Recommendations for future work are to enhance the interpretability of the resulting models, better combine methods based on the use of information from hybrid schemes with physical models, and use more extensive databases for enhancing accuracy. This research highlights the profound impact that machine learning can have in the field of bioinformatics as a tool to enhance knowledge of such protein structures and to further practice improvements in the understanding of life processes and a and drug design. © 2024 IEEE.

关键词： Federated learning

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Transfer Learning for Hand Arthritis Prediction from X-Ray Images

Transfer Learning for Hand Arthritis Prediction from X-Ray I...

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Innovative Practices in Technology and management (ICIPTM), International Conference on

作者： Ramakrishnan Raman T M Inbamalar N Pushpalatha S. Meenakshi Ashok Kumar Shaik Razia N. Gopinath Symbiosis Institute of Business Management Pune Maharashtra Symbiosis International Pune Maharashtra Department of Electronics &Communication R.M.K. College of Engineering Thiruvallur Tamil Nadu Department of Computer Science Marri Laxman Reddy Institute of Technology Hyderabad Telangana Department of Science and Humanities R.M.K. Engineering College Kavarapettai Tamil Nadu Department of Computer Science Banasthali Vidyapith Rajasthan Department of Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu

Arthritis is a bone disorder that includes swelling and pain in one or more joints. Everyone can develop osteoarthritis, but it grows more common as individuals get older. When arthritis deteriorates over time, it can lead to persistent pain, making it challenging to do daily tasks, and making activities like walking and climbing stairs painful and difficult. If arthritis is correctly identified and treated in its early stages, these consequences can be avoided. The goal of this project is to create two transfer learning models that, by spotting arthritis in its earliest stages, can lower the likelihood of acquiring chronic arthritis. For this purpose, Google served as the source of the images used in this study. After being purchased from Google, the data collection is preprocessed using three different methods. Image scaling, noise reduction, and image enhancement are a few of the pre-processing approaches. The transfer learning models are trained and assessed using this preprocessed dataset. In this work, two distinct transfer learning models are established. The models include SegNet and ENet. On a graph, the outcomes for the performances of both models are displayed. The training data from the first few epochs of the ENet model and SegNet model are also used in the analysis. The models' final accuracy and loss values are then assessed. In the end, it was discovered that the SegNet model had a lower loss value and more accuracy than the other. The model created in this study can be utilised as a preliminary test for arthritis when a person exhibits moderate arthritis symptoms because the final accuracy of the model is higher than or equal to 95%.

关键词： Analytical models Pain Transfer learning Training data Data collection Arthritis Data models

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Smart Sampling and Residual Bottleneck Dense Network for Reliable and Energy-Efficient Routing in WSNs

Smart Sampling and Residual Bottleneck Dense Network for Rel...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Asma Anjum Dattathreya Salman Arafath Mohammed Dhaval Rabadiya K. B. Venkata Brahma Rao Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Alva’s Institute of Engineering and Technology Moodbidri Karnataka Department of Electrical Engineering Computer Engineering Section College of Engineering King Khalid University Abha Asir Region Saudi Arabia Faculty of Management Studies Parul University Dist. Vadodara Gujarat Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The fundamental requirement for WSNs made up of power-efficient and inexpensive sensor nodes depends on short-distance multi-hop communication. The potential of WSNs expands through new digital electronics and wireless communications technology despite the ongoing critical challenge related to energy efficiency. The main energy issues in WSNs stem from the excessively high control signal overhead between base stations and sensor nodes together with packet losses that require retransmissions to reduce energy efficiency. The proposed routing technique solves energy inefficiencies by employing RBDN-HMOA as an energy-efficient routing method. The proposed approach lowers system power consumption through its algorithm which picks distance and energy residual-based optimum forwarding nodes and adapts sampling rates automatically. The algorithm adopts Human Memory Optimization Algorithm (HMOA) to optimize network parameters. Research data shows that experimental results demonstrate both power efficiency and enhanced data transfer rate. The network demonstrates scalability because both throughputs reach 1100 kbps and energy usage reduces to 0.08 J when using 10 nodes. The RBDN-HMOA creates a practical method which extends network duration in practical deployments by diminishing WSN energy utilization without compromising data integrity.

关键词： Wireless sensor networks Energy consumption Fluctuations Heuristic algorithms Throughput Routing Energy efficiency Real-time systems Delays Optimization

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Exploring the Impact of Enabling PIE on System Performance in Cloud-Native Networking

Exploring the Impact of Enabling PIE on System Performance i...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Xiaoyan Yu Qian Xu Xiaojun Yu College of Computer Science Jinan Vocational College Jinan China School of Management Engineering Shandong Jianzhu University Jinan China Register department Lucent Technologies Investment Co.Ltd. Qingdao China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Three limit simulations of real user environments were conducted to test the impact of enabling Position Independent Executable (PIE) on the performance of Linux 64-bit systems. Based on the comparative analysis with Linux 32-bit systems, the stability of the system was evaluated using fault rates, while system performance was assessed using TPS (transactions per second) values. Additionally, a security analysis of the system was conducted. The results indicate that enabling PIE significantly enhances the security of Linux 64-bit systems. The results show that enabling PIE can significantly enhance the security of 64-bit Linux systems. The system fault rate is 0,which is advantageous for improving system scalability. Although there is some fluctuation in the TPS values, the magnitude is small, indicating potential for further enhancing system performance. Enabling or disabling PIE does not significantly affect the overall performance stability of the Linux 64-bit system.

关键词： Fluctuations Linux System performance Scalability Stability analysis Safety Security Intelligent systems Network systems

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Unleashing the Power of Big Data: A Comprehensive Analysis and Future Directions

Unleashing the Power of Big Data: A Comprehensive Analysis a...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Annu Sharma Gaurav Kumar Mansoor Farooq Manish Kumar Gupta Saranya Raj Ashok Kumar Rai Department of Computer Science & Application RajaRajeshwari College of Engineering Bengaluru Karnataka India Department of Computer Science & Engineering Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Department of Management Studies University of Kashmir Jammu & Kashmir India Department of Information Technology & Computer Application Madan Mohan Malaviya University of Technology Gorakhpure UP India Department of Data Science & Engineering Dronacharya Group of Institutions Greater Noida Uttar Pradesh India Department of Computer Science & Engineering Madan Mohan Malaviya University of Technology Gorakhpure UP India

Big Data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive analysis of the current state of BD, explores its potential applications across industries, and outlines future directions for maximizing its benefits. This paper examines the key components of BD, including data collection, storage, processing, and analytics. It investigates the technologies and methodologies that enable efficient handling of large volumes of structured and unstructured data. Additionally, it explores the impact of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) on the growth and utilization of BD. Overall, this research paper provides a comprehensive analysis of BD, its current applications, challenges, and future directions. It serves as a valuable resource for researchers, practitioners, and policymakers seeking to unlock the full power of BD and harness its benefits across diverse domains.

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Based on Internet of Things Tracking of Hand Hygiene and Practices 2nd

Based on Internet of Things Tracking of Hand Hygiene and Pra...

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2nd International Conference on Mechanical and Energy technologies , ICMET 2021

作者： Jayadeva, Sujay Mugaloremutt Singh, Krishanveer Karnan, Lokanayaki Franklin Telfer, L. Bharti, Anupama Dhiman, Viney Department of Health System Management Studies JSS Academy of Higher Education and Research Karnataka Mysuru India GLA University Mathura India Department of Computer Science St. Francis de Sales College Bengaluru India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Sociology and Social Work Himachal Pradesh University Himachal Pradesh Shimla India Department of Cardiology PGIMER Chandigarh India

ISBN: (纸本)9789811901072

The study explores the tracking and practices essentiality in hospitals and health facilities, because related infections, such as higher risks and acceleration of illness, can have significant effects on health care. Human observers tracking and Practices have problems of their own, such as loss of observation concentration, accumulation of human mistake and expert deformation. In this investigation, the results of two internet-based techniques for controlling the hand hygiene of medical personnel during patient visits were carried out and evaluated. As base stations and smart phones, we employed ESP modules as mobile nodes and calculated the range to be located in the patient's room using Bluetooth RSSI values. We analyzed the RSSI in the proximity solution from a sensor host measured on different ESP nodes/module and used the premise that the sensor host is nearest to the ESP node which offers the maximum RSSI values. We utilized the RSSI value to measure the location between mobile nodes and every ESP node in the trilateration method and we applied the trilateration technique to identify the mobile node inside the room. Our tests have shown that 20% of the solution based on proximity was erroneous, whilst the 8% of the solution based on trilateration was erroneous. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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A Novel Approach for Enhancing the Security and Performance of Wireless Sensor Networks With a Single Cluster with Multi-hop Communication 1st

A Novel Approach for Enhancing the Security and Performance ...

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1st International Conference on Artificial Intelligence and Digital Twin Technology, IconAIDTT 2023

作者： Gopalakrishnan, S. Hemanand, D. Soundari, A. Gnana Hemanth, S.V. Aljanabi, Mohammad Hephzipah, J. Jasmine Department of Electronics and Communication Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Science and Engineering S.A. Engineering College Tamil Nadu Thiruverkadu600077 India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Thandalam Chennai602105 India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Telangana Medchal-Malkajgiri501401 India Department of Computer College of Education Alirqia University Baghdad Iraq Department of Electronics and Communication Engineering R.M.K Engineering College RSM Nagar Tamil Nadu Kavaraipettai601206 India

ISBN: (纸本)9783031777981

It is common practice to employ wireless sensor networks (WSNs) for intercontinental conversations. Quality of service (QoS), which may be quantified in terms of end-to-end latency, energy efficiency, and packet delivery ratio, is crucial for these wireless networks. Using a routing protocol with distinguishing features is advised to enhance service quality. Due to the absence of an alternate way in the event of a path failure, the single-path routing mechanism in WSN has significant difficulties in providing a continuing communication channel. This issue is resolved by implementing a multipath routing method using a clever protocol. WSNs are dispersed throughout space and are autonomous. WSNs are susceptible to security concerns since they are deployed sporadically and lack a central authority. Using the attacker identification Protocol, a more effective security methodology has been created based on the approach that has been provided (AIP). In this study, we also suggest the SCMC protocol, which increases bandwidth, packet delivery ratio, and end-to-end delay in order to enhance QoS. The single cluster head Multi-hop Communication Protocol stabilizes the energy level to achieve excellent energy conservation and a longer lifespan. For multi-hop communications, a single cluster may produce several dynamic multi-paths among its members. Testing of the proposed work against the ECMP and SPEED protocols is done in Ns-2. According to the findings, the suggested protocol performs better than the other two protocols regarding shortest latency, minimal energy consumption, and improved delivery ratio. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wireless sensor networks

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