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Design and Implementation of intelligent Medical Diagnosis Platform based on intelligent Evaluation Algorithm 3

Design and Implementation of Intelligent Medical Diagnosis P...

引用

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

作者： Li, Zhi Anhui University of Chinese Medicine School of Medicine and Information Engineering Hefei230012 China

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Design and implementation of intelligent medical diagnosis platform based on intelligent evaluation algorithm is presented in the paper. When conducting remote consultation, medical data mainly comes from the clinical information systems of various hospitals. The key to effectively integrating medical data in various hospitals is to solve the information integration of electronic medical records. The standardization and structuring of medical record information determines the application scope and level of electronic medical records. Adaptive computing can be combined with wireless sensor network technology, so that medical staff can obtain simplified, accurate and reliable medical information and reduce the difference between medical service levels. With these theoretical information, the designed model is then integrated wit the medical imaging analysis. Through the testing, the performance is validated. © 2022 ieee.

关键词： Diagnosis

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Design of Multi-Layer Information Security Protection Scheme for Vehicle-to-Grid System 3

Design of Multi-Layer Information Security Protection Scheme...

引用

3rd International Conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2023

作者： He, Shan Lin, Fafu Zhao, Yuming Xiao, Yong Tang, Wenjun Zhang, Fan Lin, Xiaoming Shenzhen Power Supply Bureau Co. Ltd Guangdong Province Shenzhen China Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangdong Province Guangzhou China

ISBN: (纸本)9798350372052

Vehicle-to-grid(V2G) is an important tool for enhancing the flexibility of new power systems and ensuring real-time power balance. The V2G system has the characteristics of multiple levels of entities and strong openness, making it easy to become the main target and springboard for attacking the power grid. In this context, the article analyzes the communication architecture and potential information security risks of the V2G system. Based on the domestic password algorithm system, a multi-layer and full link V2G system information security protection architecture is constructed, which ensures the safe and reliable transmission of V2G system information through four key technologies: identity authentication, encryption protection, situational awareness, and data sharing. Through testing, it has been shown that in order to ensure the success rate of data encryption and decryption in the V2G system, the system must have a concurrency of less than 1000 times. The encryption and decryption time, throughput, and anomaly rate indicators of the system are excellent. This data scheme can meet the interactive business needs of the V2G and provide important guidance for the information security construction of V2G networks. © 2023 ieee.

关键词： Cryptography

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Basal Cell Carcinoma Prediction in Pigmented Skin Infection using intelligent Techniques 3

Basal Cell Carcinoma Prediction in Pigmented Skin Infection ...

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Siva Prasad Reddy, K.V. Archana, K.S. Department of Computer Science and Engineering Pallavaram Chennai India

ISBN: (纸本)9781665462167

Melanoma is considered as a most lethal form of cancer. Design and development of computer-aided intelligent algorithms for early detection of skin cancer is the emerging research area. Despite many conventional mechanisms, a new type of cancer caused by unrepaired Deoxyribonucleic acid (DNA) within the skin cells. Due to its nature of rapid genetic mutations on the skin, it widely affects other body parts if not treated at early stages of intelligent computing evidenced the development of automated medical diagnosis and recommendation systems. It is possible to identify between melanoma and other classification of skin cancer based on the symmetry, color, size, form, and other characteristics of lesions. Numerous efforts are made by many researchers to develop various deep learning and machine learning inspired classification and segmentation algorithms to analyses skin lesion images. In existing the algorithm used for this research was naïve bayes, support vector machine etc. Here, after several methods such as data pre-processing, image segmentation, feature extraction and the feature extraction and the proposed algorithm of adaboost method, which is used to tune the algorithm to predict the skin infection. Finally, the proposed model has achieved 92.5% accuracy when compared with existing work. © 2023 ieee.

关键词： Feature extraction

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Blockchain with Sand Cat Swarm Optimized Semantic Attentive Mobilenetv3 Model for Smart IoHT System 3

Blockchain with Sand Cat Swarm Optimized Semantic Attentive ...

引用

3rd ieee International Conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nalluri, Mounika Mupparaju, Chinna Babu Rongali, Aruna Sri Bhuvanesh, A. Murray State University Department of Information Technology Murray United States University of Central Missouri Department of Information Technology MO United States University of the Cumberlands Department of Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350361186

Nowadays, the healthcare (HC) system based on blockchain (BC) assisted internet of things (IoT) has grown a lot in which the medical services are readily accessible to each user via internet sources. However, the HC IoT systems are highly vulnerable to security threats to render the patient's personal health data. Hence, this study put forth innovative optimized Deep learning (DL) model for BC-assisted secure IoT enabled HC system. The developed study undergoes three major stages namely, medical image encryption, optimal key selection, decryption, and disease classification. Initially, the medical image is encrypted and followed by this, optimal key selection process is performed using a novel sand cat swarm optimizer (SCSO). The encrypted medical data is then stored in the BC and decrypted for further process. Finally, semantic attentive mobileNetV3 (SAtt-MNetV3) model is introduced to extract the features and to classify the decrypted health data to the patients. The simulation is carried out on the Python platform and a publicly available ISIC-2017 database is considered for training process. Various existing studies are also compared with the developed model by computing several performance measures like accuracy, computation time, peak to signal noise ratio (PSNR), recall, sensitivity and specificity metrics. The developed methodology obtained the overall accuracy of 99.2%, recall of 98.9%, specificity of 99.5%, PSNR of 60.7 dB and computation time of 0.13 s. The simulation performances outperform well compared to conventional studies. © 2024 ieee.

关键词： Cryptography

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Machine Learning-based Multilabel Toxic Comment Classification 3

Machine Learning-based Multilabel Toxic Comment Classificati...

引用

3rd ieee International Conference on computing, Communication, and intelligent systems, ICCCIS 2022

作者： Singh, Nitin Kumar Chand, Satish Jawaharlal Nehru University School of Computer and Systems Sciences New Delhi India

ISBN: (纸本)9781665462006

The emergence of social media marked the beginning of a revolution not just in the realm of digitalization but also in that of communication. In spite of the fact that social media platforms make it possible for anyone located anywhere in the world to express their viewpoints and interact with a large audience, social media has also evolved into a venue for cruel behaviour, offensive language, cyberbullying, personal assaults, and the use of profane language. To tackle this challenge, we are detecting the toxicity level in the Jigsaw dataset by Google, which consists of six different classes, including toxic, severe_toxic, obscene, threat, insult, and identity_hate. This is a multilabel classification in which one comment can fall under more than one class. We study the impact of Multinomial NB, Logistic Regression, and Support Vector Machine with TF-IDF on identifying toxicity in text. These models were trained using the training data and after training were tested on the test data provided in the dataset. Experimental results show that Logistic Regression trumps the other models in terms of accuracy and hamming loss. © 2022 ieee.

关键词： Toxicity

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Ensemble Learning on a Weak Correlated Android Malware data using Stratified K-Fold 3

Ensemble Learning on a Weak Correlated Android Malware data ...

引用

3rd ieee International Conference on computing, Communication, and intelligent systems, ICCCIS 2022

作者： Soundrapandian, Pradeepkumar Duraisamy Geetha, S. VIT University Chennai India

ISBN: (纸本)9781665462006

In Android apps communicates with other apps by using Intent or PendingIntent. An Intent enables Android applications to share information between apps (like data, action, etc.,), and the PendingIntent delegate's authority to other apps to perform the required action in the future. Android supports apps to collaborate with any $3^{\mathrm{rd}}$ party apps using a flexible communication model called Implicit Intent-based Communication. Though this communication channel is effective in collaboration it is unprotected and unsafe by default. Any application (even malware) can register to this implicit channel, and thereby can sniff the intents exchanged through the channel, making it vulnerable to malware attacks. In case, if an app is exchanging its sensitive data like GPS location or exchanging PendingIntent using implicit intents, in turn, this leads to unauthorized access and privilege escalation attacks. In this paper, we leverage the machine-learning techniques for security predictions in order to identify such possible threats from the apps' binary inspection, and thereby our research can assist cyber forensic tools to identify the vulnerabilities caused by dynamic characteristics present in an application before executing the application itself. This paper presents a statistical model to analyze the malware nature of a mobile application: (1) based on the PendingIntent Flag usages, and (2) based on the type of Broadcast across apps. Our app classification achieved an F-score of 78.7%. © 2022 ieee.

关键词： Learning systems

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Accident Factors Detection and Prediction for Indian states and district roads 3

Accident Factors Detection and Prediction for Indian states ...

引用

3rd ieee International Conference on computing, Communication, and intelligent systems, ICCCIS 2022

作者： Jagadamba, G. Chayashree, G. Kumar, A.V. Senthil Shrinivasacharya, Purohit Siddaganag Institute of Technology Karnataka Tumakuru India Vidyavardhaka Institute of Technology Karnataka Mysuru India Hindusthan College of Arts and Science Tamilnadu Coimbatore India

ISBN: (纸本)9781665462006

The transportation systems are increasing day by day. The increased number of vehicles on the road leads to more accidents. In this situation, priority to road safety is must, and the government has made many attempts to predict and pinpoint accidents zone, roads etc. The usual cause for road accidents is rash driving;however, the accidents are increasing day by day due to other factors. This paper proposes the detection of factors for accidents using the prediction system for Indian states and districts of Karnataka. The proposed system, detected many factors such as seasons, classes of road and road types as the major cause for road accidents using machine learning algorithms apart from the regular factors. The results from the experiments were appreciable for the dataset collected from nearby police stations and hospitals of Bangalore and Tumakuru road and kaggle dataset. The testing results were found to be near to the manual analysis. © 2022 ieee.

关键词： data mining

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Multimodal data Analysis Using Soft computing Techniques For The Detection and Classification of Lung Cancer 3

Multimodal Data Analysis Using Soft Computing Techniques For...

引用

3rd International Conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Vinod, C. Menaka, D. Noorul Islam Centre for Higher Education Department of Electronics and Communication Engineering Kanyakumari India Noorul Islam Centre for Higher Education Department of Electronics and Instrumentation Engineering Kanyakumari India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Examination of medical images is a tedious task as for as doctors and experts concerned. In order to analyse these images various computing techniques are also employed. data analyses using these computing techniques are proved to be efficient in many applications. In the proposed system a similar technique is employed to detect the presence of lung nodules and classify the same in to different categories. data collected from different sources can be used for the proposed system. Clinical information and various imaging methods can be used as the source of information to this system. Based on the input the system classifies the images into various categories. The input image is processed and with the help of auto encoder segmentation is done. Lung nodule segmentation helps to identify the location and size of the lobe. © 2022 ieee.

关键词： Biological organs

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Transformative Storage Framework for 6G Smart City Deployments 3

Transformative Storage Framework for 6G Smart City Deploymen...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Maheswaran, N. Bose, S. Shruthi, T. Logeswari, G. Anitha, T. Prabhu, D. Anna University Department of Cse Ceg Chennai India Ramco Institute of Technology Department of Cse Rajapalayam India VIT-Chennai School of Computer Science Engineering India Sathyabama Institute of Science Technology Department of Cse Chennai India Anna University Regional Campus Department of Cse Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

The advent of 6G networks is poised to revolutionize the digital landscape by offering unprecedented data transfer speeds, ultra-low latency, and enhanced connectivity capabilities. This paradigm shift necessitates the development of robust and efficient storage solutions to handle the massive influx of data generated by ubiquitous IoT devices, immersive applications, and intelligent systems. In this context, we propose a multilevel storage Algorithm (LRU + OPTIMAL) designed specifically for the 6G networking environment in smart cities. Our algorithm synergizes the Last Recently Used (LRU) and Optimal algorithms to dynamically manage storage resources, thereby ensuring optimal data retrieval and minimizing latency. The deployment of 6G networks introduces complex data storage challenges, driven by the exponential growth of data traffic and the real-time processing is required. By integrating LRU and Optimal algorithms, our multi-level storage framework can effectively prioritize data caching and retrieval operations, thereby enhancing system efficiency and responsiveness. This approach leverages the predictive capabilities of the optimal algorithm to anticipate future data needs, while the LRU algorithm ensures that frequently accessed data remains readily available. Compared to 5G networks, 6G offers significantly higher bandwidth, reduced latency, and increased device density, all of which exacerbate storage demands. Our algorithm addresses these challenges by optimizing storage utilization and ensuring seamless data flow across distributed network nodes. The comparative analysis underscores the superiority of 6G in handling complex, data-intensive applications, thus necessitating advanced storage solutions such as our proposed algorithm. In the Multi-Level Storage Algorithm (LRU + OPTIMAL), a scalable and efficient framework for managing optimal storage space in 6G networks, ensuring that the storage infrastructure can keep pace with the rapid advancements in 6G

关键词： 5G mobile communication systems

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 ieee.

关键词： Discrete cosine transforms

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