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A Novel Simulated Annealing-based Algorithm for data Replication Optimization in Mobile Edge computing

A Novel Simulated Annealing-based Algorithm for Data Replica...

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2024 ieee international conference on Recent Advances in Science and Engineering Technology, ICRASET 2024

作者： Srisainath, R. Jain, Romil Pallavi Tiwaskar, S.A. Gajendran, P. Marwaha, Pradeep Prince Shri Venkateshwara Padmavathy Engineering College Department of Mech Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Psychology Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350388602

New issues such network congestion, capacity limitation, and excessive latency have arisen due to the everincreasing need for efficiently processing complex data in MEC systems. Improving system performance, lowering latency, or increasing data availability are all possible outcomes of data replication, which entails making several copies of data at the edge nodes. data replication and placement in MEC systems is hardly a walk in the park, either, since mobile networks are always evolving. An article introducing a heuristic data Replication Algorithm (SA-DRA) based on Simulated Annealing that is particular to MEC is presented now. Therefore, SA-DRA's goal is to delay replication until it returns maximum benefit while simultaneously eliminating the use of available network resources. Multiple elements or measurements may be used for this purpose, such as user mobility, network design, and workload kinds. A new solution is first generated using a basic formula, and then a cost function depending on the network status in real mode is used. Sequential operation of surrounding states exploration in a cycle improves the position even farther than the initial result. To find out how much more efficient SA-DRA is at tackling latency and load balancing between edge nodes, we compared the simulated results to those of using heuristics and greed. © 2024 ieee.

关键词： Quality of service

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Exploring the Benefits of Deep Learning for data Science Practices 15

Exploring the Benefits of Deep Learning for Data Science Pra...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Mohit Kumar Kumar, Ritesh Sinha, Deepak Kumar Senthilkumar, K.K. Dhabliya, Dharmesh Ahluwalia, Gunveen Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

With the growing occurrence of records technology and analytics, Deep studying has become an effective device within the records technology field. Deep mastering is a subset of artificial Intelligence that uses algorithms to construct complex fashions for huge-scale records analysis. The models created through Deep studying are often extra correct than traditional analytic techniques, as they use a wide style of capabilities and labels to discover records. Deep studying models can remedy many facts and technological know-how problems, from herbal language processing to photo class to regression modeling. Deep mastering models are compelling in conditions in which a high degree of accuracy is needed and where the inputs are complicated and varied. In brief, the blessings of Deep studying for data technological know-how consist of improved accuracy, faster processing, and automated feature extraction. © 2024 ieee.

关键词： Economic and social effects

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 ieee.

关键词： Convolutional neural networks

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Comparative Analysis of LSTM and Random Forest Algorithms for Sentiment Classification in Movie Reviews 3

Comparative Analysis of LSTM and Random Forest Algorithms fo...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Purohit, Shardha Rajput, Aarfa Vats, Satvik Mudgal, Rusha Shah, Owais Ahmad Verma, Aditya Noida International University School of Journalism & Mass Communication Gautam Budh Nagar India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Amity University Amity School of Communication Manesar Gurugram India K. R. Mangalam University School of Engineering & Technology Gurugram India

ISBN: (纸本)9798350375190

This study conducts sentiment analysis on 50,000 movie reviews, comparing the traditional Random Forest classifier with a Long Short-Term Memory (LSTM) network. The Random Forest achieved robust metrics across accuracy, precision, recall, and F1-score (0.85), while the LSTM excelled in capturing the complex language patterns inherent in the reviews through its sequence-based processing. This study has refined the data by removing irrelevant characters and employing tokenization and lemmatization to enhance model training. Despite its higher computational requirements, the LSTM showed potential in detailed sentiment analysis. Future directions include exploring other neural architectures, enlarging the dataset, and practical applications to improve model efficacy. The study highlights key insights into sentiment analysis methodologies and their implications for both academic and practical realms. © 2024 ieee.

关键词： Long short-term memory

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Unleashing Simple Pendulum Dynamics with Posit Arithmetic 5th

Unleashing Simple Pendulum Dynamics with Posit Arithmetic

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5th international conference for Next Generation Arithmetic

作者： Aldhapati, Avinash Kumar, Ashwini Jaya Subramanian, Rajaraman Calligo Technol Pvt Ltd Bengaluru India

ISBN: (纸本)9783031727085;9783031727092

Simulations provide a powerful means to explore, analyze, and understand complex systems, allowing us to make informed decisions across various domains. Fields like genomics, physics, and climate science and many more heavily rely on simulations to produce valuable information but the Memory Wall, the gap between computation speed and data access, poses challenges, especially these days when massive amount of data is produced and quick access to it is of high priority. While solutions exist, they often require infrastructure changes or apply to specific algorithms only. Posit, a novel datatype, matches standard sizes but with higher precision. Analyzing systems like a pendulum with posits reveals lower errors compared to floats, enhancing accuracy within memory constraints. Posits excel in capturing system dynamics, surpassing competitors of similar sizes. They enable better predictions of complex simulations, advancing our understanding of natural phenomena while addressing memory limitations.

关键词： Posits ieee 754 Simple Pendulum Simulations high-performance computing Error Precision

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Safety Helmet Detection Algorithm for complex Scenarios Based on PConv-YOLOv8

Safety Helmet Detection Algorithm for Complex Scenarios Base...

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2023 international conference on cognitive computing and complex data, ICCD 2023

作者： Wang, Yuanyuan Jiang, Feilong Li, Yazhou Zhang, Haiyan Wang, Meifeng Yan, Shaofeng Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Detecting safety helmets in complex environments is challenging due to issues like occlusion and lighting variations. Addressing the issues of slow detection speed and low object detection accuracy in complex environments with the YOLOv8 model, this paper introduces a lightweight safety helmet detection model, called PConv-YOLOv8, that is suitable for real-time applications in complex environments. Our method incorporates the PConv (Partial Convolution) module into the YOLOv8 model, reducing the complexity of the feature extraction network while enhancing feature representation accuracy. It also incorporates SimAM attention to extract and enhance the most relevant features by evaluating their similarity. Additionally, it considers category imbalance and positional regression in the target detection task, enhancing the model's performance in target category identification and positional localization. Moreover, we propose the Wise-Distribution Focal Loss function to improve bounding box selection accuracy and enhance the model's robustness. This paper introduces the Wise-Distribution Focal Loss method, which enhances the performance of target category recognition and location localization by improving the accuracy of bounding box selection and increasing the robustness of the overall model. The experimental results demonstrate that the method proposed in this paper achieves a 125% improvement in detection speed and a 1.8% increase in mAP0.5 compared to the YOLOv8 model. © 2023 ieee.

关键词： Convolution

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Empowering data Defender's Comprehensive Security Measures for Robust Information Protection in Robust-Cloud Environments

Empowering Data Defender's Comprehensive Security Measures f...

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2024 international conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Keshattiwar, Prasad Lokulwar, Prasad Saraf, Pranay G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India G H Raisoni College of Engineering Department of Dic Nagpur India

ISBN: (纸本)9798350319019

Ensuring sensitive data security is critical in the ever changing world of cloud computing. This paper offers an overview of our all-encompassing security protocols, created to tackle the ever-changing demands of contemporary information ecosystems. Proposed approach starts with a strong encryption architecture that uses the most recent algorithms to protect data while it's in transit and at rest. We proactively identify and neutralise such attacks before they have a chance to jeopardise the integrity of the cloud environment by utilizing sophisticated AI-driven anomaly detection. A proactive approach to addressing new security threats is ensured by this real-time threat mitigation. A multi-layered authentication and access control system that customises rights depending on user roles and responsibilities is another feature of Empowering data Defender. By using this granular approach, sensitive material is protected from unauthorised access and only authorised workers can interact with it. Project approach includes regular security audits and compliance inspections to make sure that industry standard and laws are followed. In addition to strengthening the present security posture, this ongoing monitoring and assessment procedure allows for quick adaptability to changing compliance needs. Encouraging data Defender's all-encompassing security approach fortifies cloud-based data, fostering confidence and trust in the digital age. Through the integration of access control, intrusion prevention, encryption, and compliance measures, our methodology provides a holistic resolution to the complex problems presented by contemporary cloud systems © 2024 ieee.

关键词： Authentication

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Interactive influence of urban heat island and urban pollution island in two major cities of India (Bangalore and Delhi)

Interactive influence of urban heat island and urban polluti...

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2024 international conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Dasgupta, Anindita Kumar, Uttam International Institute of Information Technology Spatial Computing Laboratory Center For Data Sciences Bangalore India

ISBN: (纸本)9798350389678

Rapid urbanization with the changes in land use patterns has resulted in the human induced urban heat island (UHI). UHI is a well-known phenomenon in which urban environments retain more heat than nearby rural environments effecting the urban population. City-heat interacts with rising summer temperatures and heat-waves and emission produced placing additional pressure on infrastructure. This study investigates the impact of interactive influence of Urban Heat Island and Urban Pollution Island (UPI) in two major cities of India (Delhi and Bangalore). The seasonal study for summer was performed for the two cities and so that interactive influence of aerosol, Nitrogen dioxide (NO2) and Land Surface Temperature (LST) is measured when the effect of UHI is the most. The study is performed for five years (2019, 2020, 2021, 2022 and 2023) and it was observed that in Delhi the overall aerosol index is greater than Bangalore. There was a considerable difference in the concentration of pollutant in 2021(during lock down because of pandemic) and 2022 (post-pandemic). Study was carried at a ward level in order to find out the interaction of pollutant and LST. Regression analysis was done taking the spatial metrics such as mean patch size, patch density, edge density, and shape of built-up patches at ward level (micro scale) of the city to measure the effect of anthropogenic activities on LST. © 2024 ieee.

关键词： Atmospheric temperature

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A Novel Collaborative Framework for Cloud-Based cognitive Services 4

A Novel Collaborative Framework for Cloud-Based Cognitive Se...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sharma, Vinod Rathore, Rachna Maheshwari, Divya Pandey, Abhishek Juneja, Ashwani Kumar Chauhan, Yuvraj Singh Slp University Dept of Cse Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Vidyapeeth University Dept. of Journalism & Mc Lingayas Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

cognitive Radio (CR) is transforming wireless communications by optimizing spectrum usage. CR users can access the spectrum only when primary users are inactive, requiring reliable spectrum sensing. Cooperative spectrum sensing, where data from multiple nodes is shared with a central hub, has emerged as the most effective method, surpassing local detection in dealing with challenges like noise uncertainty and channel instability. This study explores two fusion algorithms for merging local spectrum data: hard decision fusion (HDF) and soft decision fusion (SDF). While SDF offers better detection capabilities, it demands excessive bandwidth. In spectrum allocation, a complex NP-hard problem, optimization methods, especially swarm intelligence inspired by honey bee behavior, have been researched. To address spectrum assignment issues, a novel hybrid Deep Recurrent Neural Network (DRNN) approach is proposed. This hybrid model, incorporating 2-opt and additional search techniques, enhances spectrum utilization by overcoming slow convergence and local optima challenges. Empirical results demonstrate its superiority in optimizing spectrum allocation. © 2024 ieee.

关键词： Swarm intelligence

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A Dynamic Configuration Design Method Based on Cloud Model and Deep Reinforcement Learning 29

A Dynamic Configuration Design Method Based on Cloud Model a...

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29th international conference on Automation and computing (ICAC)

作者： Li, Rong Zhang, Haizhu Qin, Shengfeng Yang, Hao Zhang, Yongjie Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China Northumbria Univ Sch Design Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350360882;9798350360899

Rapidly developing technology and changing market make customer requirement (CR) change rapidly. In the early stage of product development, timely access to CRs is crucial. However, the previous design methods cannot grasp the changes of customers requirement preferences in time and give a better design scheme. Therefore, a dynamic configuration design method based on cloud model dealing with requirement uncertainty and obtaining CR preference is proposed to quickly solve the design scheme. Firstly, the reverse cloud conversion algorithm named Multiple Backward Cloud Transformation based on Sampling with Replacement (MBCT-SR) is used to convert the CR data into a multi-level evaluation cloud model, and the CR preference is calculated. Secondly, the design structure matrix is constructed to map the CR preference to the configuration instance. Then, the Deep Q Network (DQN) algorithm model is established and trained, and the updated CR preference is input to quickly solve the product configuration scheme. Finally, the effectiveness of the proposed method is verified by an example analysis of high-speed train bogies.

关键词： complex product configuration design Cloud model Requirement preference DQN algorithm

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