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A Reliable and efficient data transfer on wireless sensor networks by using Optimization algorithms

A Reliable and efficient data transfer on wireless sensor ne...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： T. Saravanan G. Govinda Rajulu Department of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Department of CSE St. Martin’s Engineering College Secunderabad India

Wireless Sensor Network technologies that require collecting all node-generated data without any losses like structural monitoring. Since wireless networks and resource constraints present additional challenges, point-to-point transmission, which is employed in the network for sustainable communication, becomes extremely ineffective in Wireless Sensor Networks. We investigate elements that determine reliability and explore effective combinations of possible solutions. You can implement information redundancy techniques like rebroadcasting and destruction of data techniques. Additionally, route maintenance, which explores an alternate hop and several faults, minimizes packet drops. In this paper, we evaluated several machine learning optimization algorithms for efficient and reliable data transfer in wireless networks. Our simulation results prove that each opportunity overcomes different types of flaws that occurred by the existing techniques. The Arithmetic Optimization Algorithm, Particle Swarm Optimization, and Shuffled Complex Evolution Algorithm is efficient in obtaining reliability, achieving very high reliability by enduring packet losses, and it responds very instantly to link failures. That’s the proposed combination of optimization algorithms that can prove more than 90% memory consumption with Time, High Speed, and Accuracy by providing an alternative to point-to-point communication over multiple nodes in the network.

关键词： Wireless sensor networks Machine learning algorithms Wireless networks Memory management Packet loss data transfer Reliability

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Drones and Networks: Ensuring safe and secure operations using 5g mobile network

Drones and Networks: Ensuring safe and secure operations usi...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Meenu Gupta Rakesh Kumar Manas Maheshwari Roushan Kumar Department of Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Unmanned Aerial Vehicle (UAV) i.e., drones have become increasingly popular in recent years for various applications, including surveying, photography, delivery, and even military operations. However, their widespread use raises concerns about safety and security, as drones can invade privacy, and even be used for malicious purposes. To ensure safe and secure drone operations, it is crucial to establish robust drone networks that can effectively monitor, control, and manage drone traffic. An important aspect of ensuring safe and secure drone operations is to establish clear regulations and guidelines for drone use. To accomplish safety and security there is a need of establishing no-fly zones, setting altitude limits, and requiring operators to obtain licenses and certifications. Additionally, drone operators must be trained to operate drones safely, and drones must be equipped with safety features such as collision avoidance systems and emergency landing capabilities. Overall, the safe and secure operation of drones depends on the effective integration of drone networks, regulations, and technologies. By developing and implementing robust drone networks and regulations, it can promote the responsible and beneficial use of drones while minimizing their risks and negative impacts.

关键词： 5G mobile communication Streaming media Autonomous aerial vehicles Regulation Real-time systems Security data communication

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Explainable AI and Deep Learning techniques for Colon Cancer Detection

Explainable AI and Deep Learning techniques for Colon Cancer...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Prashant Narayan Sholapur Indiramma M Department of Computer Science & Engineering B.M.S. College of Engineering Bengaluru India

Cancer is the most common disease in the world. Cancer is a disease in which some somatic cells grow out of control and spread to other parts of the body. Of the types of cancer, colon cancer is the third most frequent cancer in the world. Artificial Intelligence is being used to diagnose cancer and deep learning techniques are used for prediction. The motive of this paper is to propose a model to implement a deep learning-based Convolution Neural Network (CNN), InceptionResNetV2 Network, and InceptionV3 transfer learning model to predict cancer and non-cancerous. The model helps in the classification and prediction of two types of colon cancer: colon adenocarcinoma and colon benign cancer. We experimented with the histopathological image data set. Along with the deep learning model, Explainable AI (XAI) technique is used for the representation of the black-box model of deep learning.

关键词： Deep learning Smoothing methods Computational modeling Neural networks Transfer learning Predictive models data models

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A Systematic Survey of Happiness from an Analytical Perspective

A Systematic Survey of Happiness from an Analytical Perspect...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Aditi Jedhe Sobin Varghese Jibrael Jos Data Science CHRIST (Deemed to be University) Pune India

The paper is a survey paper that talks about studies around happiness. We have surveyed papers about the scales of measuring happiness, in which the scales are proposed, demonstrated and examined. Happiness is affected by various factors, which can be called indicators of happiness. Some of the papers we reviewed validate the significance of such indicators with applications. The indicators include inflation, unemployment, health, loneliness, and sports. Modern technology helps researchers estimate and forecast happiness and effectively find the relation between factors affecting happiness. Researchers use different methodologies to study happiness. The data used in the papers were retrieved from surveys and existing Happiness Report, designed surveys appropriate for the study. Models were proposed for forecasting happiness using Machine Learning and Neural Networks. From the reviews, we identify research gaps in the area for future work. This paper gives an overview of the studies around the area of happiness from an analytical approach.

关键词： Technological innovation Systematics Biological system modeling Neural networks Predictive models Reliability Indexes

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The Impact of Adversarial Attacks on Medical Imaging AI Systems

The Impact of Adversarial Attacks on Medical Imaging AI Syst...

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international conference on Communication Systems and Network technologies (CSNT)

作者： Alok Jain K Sangeeta Swathi B Mohd. Sadim Shashi Prakash Dwivedi Ali Albawi Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal M. P. India Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Bad actors threaten medical imaging AI systems' dependability and safety, affecting patient care and diagnosis accuracy. A novel defense against this expanding threat is adversarial defense via ensemble integration. Explainable Feature-Based Defense (XFBD), Adversarial Training with Transfer Learning (ATTL), and Robust Classifier Augmentation (RCA) are three innovative techniques that have never been combined. RCA enhances training with controlled aggression. This allows the model to distinguish authentic medical imaging data from altered sources. ATTL makes transfer-learning-taught models more resistant to topic-specific hostile approaches. XFBD simplifies the defensive process, helping us comprehend how the model picks and fights different methods. A comparison indicates that ADEI always outperforms tried-and-true approaches. ADEI outperforms typical approaches in accuracy, sensitivity, precision, stability, interpretability, private protection, and computing cost. Strong safeguards are needed as AI-powered healthcare research becomes increasingly popular. As a lighthouse, ADEI protects everything from attack. The math behind each section and how they work together to strengthen the system make it valuable. ADEI advances AI-assisted healthcare's future. Testing instruments must be accurate and dependable in this industry.

关键词： Training Sensitivity Computational modeling Transfer learning Stability analysis Security Artificial intelligence

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Using Machine Learning Techniques for Outlier Detection Application

Using Machine Learning Techniques for Outlier Detection Appl...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ravindra Kumar Chahar Ajay Shanker Singh School of Computing Science & Engineering Galgotias University Greater Noida India

The aim of designing models that have the ability for adapting to their surroundings and have capability for learning from past data has gained attention from numerous spheres, comprising computing engineering, artificial intelligence, statistics, and mathematics. Numerous machine learning methods have evolved from research in the field. Supervised, unsupervised and reinforcement techniques have been applied for a variety of scientific and industrial applications. This paper introduces a description of a scheme for outlier detection application that is based on Isolation Forest machine learning technique. The scheme is an unsupervised learning method. The scheme also uses Random Forest machine learning technique for the application. This method is a supervised learning method. The learning techniques are tested on a sample system. A comparison of Isolation Forest technique with other unsupervised learning algorithms is presented, along with its comparison to supervised Random Forest Classifier algorithm.

关键词： Support vector machines Machine learning algorithms Supervised learning Forestry Mathematical models Classification algorithms Decision trees

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Reinforcement Learning of Self-enhancing Camera Image and Signal Processing

Reinforcement Learning of Self-enhancing Camera Image and ...

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international conference on advances in data-driven computing and Intelligent Systems, ADCIS 2022

作者： Bajaj, Chandrajit Yang, Yunhao Wang, Yi Department of Computer Science and Oden Institute of Engineering and Sciences University of Texas AustinTX United States Oden Institute of Engineering and Sciences University of Texas AustinTX United States

ISBN: (纸本)9789819909803

Current camera image and signal processing pipelines (ISPs), including deep-trained versions, tend to apply a single filter that is uniformly applied to the entire image. This is despite the fact that most acquired camera images have spatially heterogeneous artifacts. This spatial heterogeneity manifests itself across the image space as varied Moire ringing, motion blur, color bleaching, or lens-based projection distortions. Moreover, combinations of these image artifacts can be present in small or large-pixel neighborhoods within an acquired image. Here, we present a deep reinforcement learning model that works in learned latent subspaces and recursively improves camera image quality through patch-based spatially adaptive artifact filtering and image enhancement. Our Recursive Self-Enhancement Reinforcement Learning(RSE-RL) model views the identification and correction of artifacts as a recursive self-learning and self-improvement exercise and consists of two major sub-modules: (i) The latent feature subspace clustering/grouping obtained through variational auto-encoders enabling rapid identification of the correspondence and discrepancy between noisy and clean image patches. (ii) The adaptive learned transformation is controlled by a soft actor-critic agent that progressively filters and enhances the noisy patches using its closest feature distance neighbors of clean patches. Artificial artifacts that may be introduced in a patch-based ISP are also removed through a reward-based deblocking recovery and image enhancement. We demonstrate the self-improvement feature of our model by recursively training and testing on images, wherein the enhanced images resulting from each epoch provide a natural data augmentation and robustness to the RSE-RL training-filtering pipeline. Our code is publicly available at https://***/CVC-Lab/rse_***. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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A Novel Approach for Multi-Document Summarization using Jaccard and Cosine Similarity

A Novel Approach for Multi-Document Summarization using Jacc...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Sumathi Pawar Manjula GuruRaj Rao Niranjan N Chiplunkar Information Science & Engg Nitte(DU) NMAMIT Karkala Karnataka India Computer Science & Engineering Nitte (DU) NMAMIT Karkala Karnataka India

ISBN: (纸本)9781665493970

Text summarization is process of getting summaries of multiple documents. Text summarization is very much essential for saving time and resources. Through text summarization knowledge can be grasped quickly. Getting summery is challenging process because relevant information needs to be retrieved. Text summaries essential to resolve the issues of information overload which demands access to reliable and properly crafted summaries. Users can quickly find the information they need using data minimization. Saving the time and effort from having to browse through the entire collection of documents is main advantage of text summarization. In this paper it is focussed on an extractive technique of text summarization using a sentence clustering approach. The hybrid approach deals with the novel method for comprising of document and sentence clustering using Jaccard and Cosine similarity method.

关键词： Minimization Reliability

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Cropland Mapping Using Single Date Sentinel-2 Imagery Using Machine Learning Classifiers

Cropland Mapping Using Single Date Sentinel-2 Imagery Using ...

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advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Shivam Rawat Rashmi Saini Department of Computer Science and Engineering G.B Pant Institute of Engineering and Technology Pauri-Garhwal India

ISBN: (纸本)9781665474405

Crop classification is an important task that finds application in many fields, such as crop health management, crop yield estimation, and crop monitoring. Sentinel-2 images, which are readily available and have a good temporal and spatial resolution, have a lot of potential for crop classification. The primary objective of this study is to map the cropland areas using spectral signatures derived from single-date Sentinel-2 imagery. The post-classification accuracy metrics (overall accuracy (OA), and kappa score (k)) were assessed using the confusion matrix derived from the testing data samples. For Random Forest (RF) and Support vector machine (SVM) models, the Accuracy Metrics achieved are (OA = 87.33%, k = 0.851) and (OA = 88.6%, k = 0.866) respectively. The results of RF and SVM classified data show that mapped cropland areas are 1560.96 km2 and 1594.89 km2, respectively.

关键词： Support vector machines Measurement Time series analysis Crops Sensors Yield estimation Task analysis

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Multi-Grade Brain Tumor Classification

Multi-Grade Brain Tumor Classification

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Manish Kumar Sharma Sanyay Kumar Arish Pal SinghSethi Rithik Choudhary Department of Computer Science and Engineering Galgotia’s College of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Brain tumors pose a significant challenge to radiologists engaged in automated diagnosis and health monitoring due to the intricate nature of tumor grading. The literature has suggested a number of machine learning-based strategies to help radiologists in achieving more precise and dependable diagnostic assessments. This paper leverages convolutional neural networks (CNN) in conjunction with MRI detection technology to grow a particular model for the finding of brain tumor’s, facilitating the integration of computer-aided detection technologies in the medical domain. The research introduces an innovative DCNN architecture and delineates a three step preprocessing methodology designed to enhance MRI image quality and enable efficient brain tumor diagnosis. Key contributions of this work include addressing the challenge of limited datasets through the implementation of the Augmentation Technique. Advanced signal processing and image processing techniques are employed for improved feature extraction and selection. Additionally, the utilization of Multigrain offers a novel method to assess the aggressiveness of brain tumors. This research thus presents a comprehensive framework that not only enhances the correctness of brain tumor detection but also addresses data scarcity issues and incorporates state of the art systems for robust feature analysis and tumor characterization.

关键词： Image segmentation Convolution Magnetic resonance imaging Noise Feature extraction Convolutional neural networks Biological neural networks

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