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Detection and Classification of Skin Cancer by Using a Parallel Deconvolutional Network Model 1

Detection and Classification of Skin Cancer by Using a Paral...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Sangeetha, M. Karthikeyini, C. Vasundhara, S. Saravanan, D. Arunkumar, G. School of Computing SRMIST Department of Data Science and Business Systems Kattankulathur C India Excel Engineering College Department of ECE Tamil Nadu Komarapalayam India G.Narayanamma Institute of Technology &science Department of Humanities & Mathematics JNTUH Hyderabad India IFET College of Engineering Department of CSE Tamil Nadu Villupuram India Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh Madanapalle India

ISBN: (纸本)9781665456876

Skin Cancer is one of the most common cancer forms in many countries, it is considered to be one of the dangerous types in the sense that it is lethal and its occurrence over time has been dramatically high. It is one of the deadliest cancers among all diseases and has a large rate of mortality. The efficiency of the earlier approaches to assess one of the most hazardous melanoma diagnosis in dermoscopic criteria are not up to the mark. Therefore, in this research, the work has been carried out in three stages in order to detect melanoma in an efficient manner. In the first stage, prior to the implementation of the image segmentation technique, noise elimination and pre-processing steps are carried out to remove the noise and to achieve better execution results. This segmentation model focuses on the separation of the interesting portions from the background and collects the necessary information from the neighboring pixels of the same category. Gaussian analytical patterns are used to handle the heterogeneous regions/sections of dermoscopy images whose mean and variance can be dynamic. In addition, the third stage of work is based on the texture classification, which is proposed as the CSTC-Mel Identification Model. © 2022 IEEE.

关键词： Machine learning

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Enhancing Wireless Sensor Network Routing Strategies with Machine Learning Protocols

Enhancing Wireless Sensor Network Routing Strategies with Ma...

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Networking and Communications (ICNWC), International Conference on

作者： Veeranjaneyulu K Lakshmi M Sri Venkateswara Swamy Sirisha K Nagarjuna N Anupkant S CVR College of Engineering Rangareddy Telangana India Data Science and Business Systems College of Engineering & Technology SRMIST Chennai India Vishnu Institute of Technology (A) Bhimavaram Andhra Pradesh India Department of CSE Siddharth Institute of Engineering & Technology Puttur A.P India Department of CSIT CVR College of Engineering Rangareddy Telangana India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Woth the realm of Internet supportive Things (IoT), where numerous wireless components constantly communicate, a unique challenge arises. This challenge includes issues such as handling diverse devices, ensuring data broadcasting and managing scalability. To handle these issues, a new routing protocol has been proposed in this paper. This protocol is intelligent, energy-conscious, and employs a multi-objective approach, based on the distribution type Reinforcment based Learn -Driven (RL) mechanism integrated Meta-Learning termed as data synchronized -Machine learning (DS-ML). The core goal of proposed data-route mechanism, called DS-ML, will optimized power consumption on IoT connected networks, a critical issue given the defined power resources of wireless connected IoT Components. Additionally, mechanism focuses on adapting to network changes seamlessly by ensuring smooth adjustments to alterations and minimizing disruptions to enhances entire network Efficacy. The algorithm incorporates correlates issues in rewards designed in acceleration of the learn cycle. Through various simulation parameters, the DS-ML routing protocol demonstrated significant improvements in power efficacy and rapid adapt in unexpected network happenings. This was evidenced by enhancements in delivered ratio of data sets and reductions in data-sets delivery latency when compares in conventional data-routing Techniques. In general, proposed DS-ML system represents a solution for enhancing the performances and sustainability of IoT network infrastructure.

关键词： Wireless communication Metalearning Wireless sensor networks Energy consumption Scalability Routing Routing protocols

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Prediction of Bankruptcy of a company using machine learning techniques 3

Prediction of Bankruptcy of a company using machine learning...

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3rd International Conference on Electronics and Sustainable Communication systems, ICESC 2022

作者： Kothuru, Vivek Jha, Raman Kumar Ranjit, Sasanapuri Kumar Reddy, Bommireddy Vijay Roy, Sagnik Sudheer, Sachin Niit University Department of Computer Science Engineering Rajasthan India D y Patil College of Engineering Department of Computer Engineering Pune India Pes University Department of Electronics and Communication Engineering Bangalore India National Institute of Technology Srinagar Department of Civil Engineering Jammu and Kashmir India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Amrita Vishwa Vidyapeetham Kollam Amrita School of Engineering Department of Computer Science and Engineering Kerala India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Bankruptcy prediction is an important modern technique because it helps not only the policy makers but the investors and all concerned people so they can take all necessary steps to prevent or to reduce the after effects of bankruptcy. Companies take a large quantity of loan and when they become bankrupt the entire system of the bank is shattered as it is really tough to get the given money bank. Hence, it is must needed to develop a system that could predict it the company's bankruptcy and stop the loan. It is observed that the newer machine learning prediction model is outperforming the models of past thirteen years, using relatively small data but this data is still very big and balanced. Some of the machine learning models or techniques are Random Forest, XGBoost, Vector Machines. The main motive of the paper aims to compare the machine learning models which are support vector machine, random forest and XGBoost and give which one performs the best amongst them and give a novel accuracy. © 2022 IEEE.

关键词： Support vector machines

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Exploring blockchain solutions: Integrating cryptocurrency with IoT for enhanced security

Exploring blockchain solutions: Integrating cryptocurrency w...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： M Shravan Shital Kakad Umar M Mulani Geeti Sharma Mohit Tiwari Mary Shiba C School of Business SR University Warangal Telangana India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune Maharashtra KJ College of Engineering and Management Research Pune Faculty of Management Studies (CMS Business School) Jain Deemed to be University Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Delhi Department of Computer Science and Business Systems R.M.K Engineering College Thiruvallur

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Blockchain, initially developed as the underlying technology for Bitcoin, has garnered significant attention for its applications beyond cryptocurrency, particularly in complex non-monetary domains. Utilizing cryptographic methods like hashing and asymmetric encryption, combined with a distributed consensus model, Blockchain-based ledgers achieve high levels of security and immutability, effectively removing the need for third-party intermediaries. Meanwhile, the rapid growth in Internet of Things (IoT) devices being connected to networks raises substantial concerns around security and privacy. Addressing these security challenges is essential for the expanding IoT landscape. This paper explores the blockchain solution by integrating cryptocurrency with IoT for enhanced security, reviewing recent studies and applications to evaluate Blockchain's role in IoT security, pinpoint challenges, and propose Blockchain-driven solutions to improve security in the IoT ecosystem.

关键词： Privacy Ecosystems Focusing Bitcoin Reliability engineering Hardware Blockchains Safety Internet of Things Security

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Leveraging SDN: Empirical Evaluation of Privacy Enriched data Communication over VANET Using Software Defined Network Assistance

Leveraging SDN: Empirical Evaluation of Privacy Enriched Dat...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna G. Saranya Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In the realm of modern transportation systems, Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling efficient communication among vehicles and infrastructure elements for enhanced road safety and traffic management. However, the inherent vulnerabilities in VANETs, such as privacy breaches and security threats, necessitate robust frameworks to mitigate risks and ensure secure data transmission. This paper presents a comprehensive architecture for Privacy-Enriched data Communication over VANETs using Software-Defined Network (SDN) assistance. The proposed framework integrates key components including vehicles, Roadside Units (RSUs), and SDN controllers to facilitate secure and privacy-aware communication. Vehicles act as data sources, employing pseudonyms and encryption mechanisms to protect user privacy. RSUs serve as access points, collecting and relaying data while enforcing access control policies. SDN controllers provide centralized management, dynamically allocating resources and enforcing privacy policies through encryption, pseudonymization, and access control mechanisms. Communication protocols such as IEEE 802.11p and LTE enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, while the OpenFlow protocol facilitates communication between SDN controllers and network devices. Additionally, trust management mechanisms evaluate the trustworthiness of network entities based on behavioral analysis, reliability metrics, and security considerations, ensuring the integrity and resilience of the network. By amalgamating SDN-based management with privacy enhancement mechanisms and trust management protocols, the proposed framework offers a robust solution for addressing privacy and security challenges in VANETs, thereby fostering safer and more secure transportation ecosystems.

关键词： Access control data privacy Protocols Vehicle-to-infrastructure Vehicular ad hoc networks Transportation Encryption

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light Controlling and Monitoring system using Intelligent Sensors

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： G. Saranya R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring system designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability.

关键词： system performance Urban areas Control systems Real-time systems Sensor systems Safety Internet of Things

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An Improved Driver Safety system based on Drunk and Drive Analyzing Architecture using Internet of Things Assisted Alcohol Identification Sensor

An Improved Driver Safety System based on Drunk and Drive An...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Swarna G. Saranya A. Vanathi R. Ashwini B. Arunsundar Department of ECE Dr. M.G.R. Educational And Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The recently survey government is said 80 percentage of road accident is done by drunk and drive and the small cities accident range is 50 percentage to 80 percentage on this survey government declare overall of 100 accident and 30 to 35 deaths occurs on the road due to not wearing the seat belt. The Alcohol Sensor we are implement on the steering and this paper is totally depends on embedded system. This research is IoT and Arduino based system. There has been a dramatic rise in the likelihood of traffic accidents caused by the proliferation of automobiles on the road. Drunk driving is a major contributor to traffic accidents worldwide. The major goal of this research is to develop a system that can detect how much alcohol a driver has consumed. The goal of the proposed system is to decrease the frequency of drunken driving accidents by restricting users from operating motor vehicles while under the influence of alcohol or drugs. A NodeMCU ESP8266 WiFi Modules, an Arduino Controller, an Eye Blink Sensor, and a MQ3 Alcohol Detection Sensors are the main components of the suggested model. To ensure everyone’s safety, when the blood alcohol concentration (BAC) rises beyond a certain threshold, the vehicle’s ignition system will be disabled and the appropriate authorities will be notified via the IoT module; this process has previously been carried out using the GSM module. The proposed work is termed as Improved IoT based Driver Safety Model (IIoTDSM), in which it is cross-validated with the conventional model called GSM based Driver Safety Model (GSMDSM) to evaluate the efficiency of the proposed work.

关键词： Surveys GSM Roads Government Urban areas Safety Automobiles

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Machine Learning-based Alzheimer's Disease Prediction using Personalized Methods 3

Machine Learning-based Alzheimer's Disease Prediction using ...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： Machine learning

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Singe Image Dehazing using Deep Neural Networks

Singe Image Dehazing using Deep Neural Networks

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Nisarg Doshi Sagar Bhavsar Rajeswari D Srinivasan R Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values.

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm 1

An Efficient Algorithm to Classify the Mitotic Cell using An...

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1st International Conference on Computer, Power and Communications, ICCPC 2022

作者： Vidhya, R.G. Seetha, J. Ramadass, Sudhir Dilipkumar, S. Sundaram, Ajith Saritha, G. Hkbk College of Engineering Department of Ece Bangalore India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Data Analyst Sterck Systems Chennai India Vit Department of Analytics Scope Vellore India School of Business Selection Grade Kochi India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India

ISBN: (纸本)9798350397840

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer;however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful. © 2022 IEEE.

关键词： Deposits

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