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Proceedings of the 2nd international conference on Ubiquitous information management and communication

作者： Jia, Weijia Bin, Dai Liao, Lin Deparment of Computer Science City Univeristy of Hong Kong Kowloon Hong Kong Department of Electronic and Information HuaZhong Univeristy of Science and Technology Wuhan China

ISBN: (纸本)9781595939937

The convenience of Wireless LAN and the deployment of 3G network make the cross platform/network services (CPS) are getting popular. The core reasons for vulnerabilities for CPS stems from the transform of data and control messages and protocol translation occurred in cross networks and platforms operations. During the translation/transcoding, new threats could be introduced as it may go beyond of data cryptography. We particularly investigate the threats facing in Internet-3G-WiFi and propose the architecture of the secure CPS (SCPS). We illustrate newly defined five types of attacks uniquely in CPS and analyze the traditional attacks (TA) and the cross platform attacks (CPA) which are conducted mode and propose the corresponding countermeasures to the attacks in the cross platform. © 2008 ACM.

关键词： Wi-Fi

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Machine Learning-Driven Approaches for Dermatological Disease Diagnosis 2

Machine Learning-Driven Approaches for Dermatological Diseas...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Saini, Dimple Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Sri Balaji University Pune India Parul Institute of Technology Parul University Computer Science and Engineering Engineering and Technology India Pune India Chennai Institute of Technology Deparment of Computer Science and Engineering India

ISBN: (纸本)9798350373110

Recent advancements in machine learning (ML) have significantly enhanced the accuracy and efficiency of diagnosing dermatological diseases, surpassing conventional methods. This paper presents a detailed exploration of ML-driven approaches, employing sophisticated algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Decision Trees to develop a predictive model for various skin conditions. Our methodology involved collecting and preprocessing a comprehensive dataset of dermatological images, followed by applying these algorithms to classify and predict skin diseases. The model was rigorously evaluated against traditional diagnostic methods using metrics such as accuracy, precision, recall, and F1 score. Results demonstrate that our ML-driven model, particularly the CNNs, achieved superior diagnostic performance with an accuracy of 94%, precision of 92%, recall of 93%, and an F1 score of 92%. These findings highlight the potential of ML to revolutionize dermatological diagnostics, offering a more accessible, efficient, and precise approach to identifying skin diseases. The study underscores the importance of integrating artificial intelligence in healthcare, paving the way for advancements in dermatological diagnostic tools and contributing to the broader discussion on AI in medical applications. This work not only enhances diagnostic capabilities but also opens avenues for more equitable healthcare solutions by reducing reliance on limited dermatological expertise. © 2024 IEEE.

关键词： Convolutional neural networks

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Recognition of Secure Data Transmission in Cloud Platform using Deep Learning 3

Recognition of Secure Data Transmission in Cloud Platform us...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhavya, R. Kumar, M. Guru Vimal Ramya, U.M. Janagi, R. Ganesan, M. Deepa, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Rangarajan Dr. SangunthalaR&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Deparment of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Hindusthan College of Engineering and Technology Deparment of Information Technology Coimbatore India Kongu Engineering College Perundurai Department of Computer Technology-PG Erode638060 India

ISBN: (纸本)9798350375190

The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness of the system is improved using a random forest algorithm. In secure data transmission, the deep learning algorithms are trained to adopt various patterns of communication with the identification of certain anomalies. They result in security threats. They involve suspicious user behaviour with unexpected information exchange. The overall accuracy and interpretability of the system are achieved using a random forest algorithm which combines multiple decision trees. They are used for handling the high-dimensional data and obtaining the relationship between complex patterns. They helps in the identification of various security threats in the data communication through cloud platforms. This helps in the automatic decision-making process during malicious behaviour. The obtained dataset contains both the secure and insecure datasets. The extracted features acts as an input for the random forest classifier. These features are analyzed by the random forest and provides information that helps to influence the decision-making process. The integrated approach improves the accuracy of the security threats with enhancing transparency in the automatic decision-making techniques. They provide trust in the security measures in the cloud platforms. The collaboration of deep learning with a random forest algorithm helps in obtaining secure data transmission. © 2024 IEEE.

关键词： Behavioral research

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Music composition using combination of genetic algorithms and recurrent neural networks

Music composition using combination of genetic algorithms an...

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8th International Conference on Hybrid Intelligent Systems, HIS 2008

作者： Sheikholharam, Peyman Teshnehlab, Mohammad Deparment of Computer Science and Research University of Tehran Iran Department of Control K.N.Toosi University of Technology Tehran Iran

ISBN: (纸本)9780769533261

Creativity has a fundamental role in music composition. One of the theories, which exist about creativity, is combination-theory. In this paper the suitability of genetic algorithms and recurrent neural networks for modeling this theory is considered. We discuss that two phases of combination occurs: one at the genetic algorithm level, and the other at the network level. One important challenge in automatic composition is the musical fitness. We trained a recurrent neural network on two pieces of music to learn the coarse and fine statistical regularities to define a fitness function. In our implementation, pitch and duration properties of notes are considered to generate new melodic passages. The paper concludes that this method can produce music with novel combinations of pre-existing ideas that sound faithful to the learned music. © 2008 IEEE.

关键词： Genetic algorithms

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Enhancing Medical Imaging Trust with an Efficient Deep Learning System for Forgery Detection 2

Enhancing Medical Imaging Trust with an Efficient Deep Learn...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Saini, Dimple Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media India Sri Balaji University Pune India Parul Institute of Technology Parul University Computer Science and Engineering Engineering and Technology India Pune India Chennai Institute of Technology Deparment of Computer Science and Engineering India

ISBN: (纸本)9798350373110

The integrity of medical images is critical to accurate diagnosis and treatment, but the proliferation of sophisticated editing tools has heightened the risk of forgery, compromising the trustworthiness of medical imaging. This paper introduces an efficient deep learning system designed to safeguard medical imaging by identifying alterations such as splicing, copy-move, and retouching forgeries. Our proposed solution utilizes a convolutional neural network (CNN) architecture enhanced with attention mechanisms, optimized to detect subtle changes in medical images. The development process involved meticulous data preparation, augmentation, and the implementation of advanced mathematical models to refine the system's ability to differentiate between authentic and manipulated images. Evaluation was conducted on a diverse dataset encompassing various imaging modalities like MRI, CT scans, and X-rays, with both genuine and tampered instances. The proposed system delivered superior performance, achieving an accuracy of 97.5%, a precision of 96.8%, a recall of 98.2%, and an F1 score of 97.5%, significantly outperforming existing methods. These results highlight the system's robustness in distinguishing real images from forged ones, thanks to its attention mechanism that allows the model to focus on key areas within the images. Ultimately, this system offers a highly accurate and efficient tool for enhancing the security of medical imaging, reinforcing the potential of deep learning in tackling healthcare challenges and ensuring reliable diagnostics and treatment planning. © 2024 IEEE.

关键词： Convolutional neural networks

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Management and computer science synergies: A theoretical framework for context sensitive simulation environment 19th

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19th International Conference on Transactions on Computational Collective Intelligence, TCCI 2015

作者： De Maio, Carmen Fenza, Giuseppe Loia, Vincenzo Tommasetti, Aurelio Troisi, Orlando Vesci, Massimiliano Deparment of Computer Science University of Salerno FiscianoSA Italy Department of Management and Information Technology University of Salerno FiscianoSA Italy

ISBN: (纸本)9783662490167

In the light of contemporary management trends and on the basis of the theory of "open innovation", derives the concept of "crossfertilization". Crossfertilization, i.e. profitable inter-group knowledge exchange facilitates the fusion of input from different disciplinary communities. In such a scenario, the study highlights the opportunities deriving from the cross-fertilization between management and computer science domains, and yields in terms of cognitive synergies results that exceeds by far the individual outputs of the parties involved. The approach we propose is a full mode generation of knowledge starting from the hypothetical assumptions relative to simulation using context data. A general workflow complementary Structural Equation Modeling (SEM) is defining being the most appropriate mathematical technique for testing causal relationships between latent variables with Fuzzy Data Analysis techniques in order to tailor Decision Support System (DSS) to the context of application. The main contribution of our study is the definition of a theoretical framework to address contextual decision making concerning relations between commitment, loyalty and customer satisfaction. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Decision support systems

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Enrichment of Antenna Gain of a Biconvex Patch with a Novel Superstrate

Enrichment of Antenna Gain of a Biconvex Patch with a Novel ...

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2020 International Conference on computer science, Engineering and Applications, ICCSEA 2020

作者： Panda, Ribhu Abhusan Krishna Senapaty, Murali Rath, Premansu Sekhar Mishra, Debasis GIET Univarsity Department of Computer Science Engineering Gunupur India Veer Surendra Sai University of Technology Deparment of ELTC Burla India

ISBN: (纸本)9781728158303

This paper illustrates the mathematical derivation for the biconvex shaped patch which can be used for the 5G application. The gain enhancement has been done by using the superstrate with metal blocks. A 40mm × 40 mm substrate has been taken in which FR4 Epoxy dielectric is used for the substrate with a height 1.6 mm. Air gap between the superstrate and the substrate is also 1.6 mm. The conformal rounded patch has been altered in such a way that it will be shaped as a biconvex lens. The parameters like S-Parameter, SWR, antenna gain etc have been found out and a comparison has been done taking the proposed antenna with and without superstrate. The effect of metal blocks that are used on the superstrate is considered for gain enrichment of the projected model. The return loss ( © 2020 IEEE.

关键词： Substrates

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Interactive Embodied Intelligence of Machines

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CAAI Artificial Intelligence Research 2024年第1期3卷 164-169页

作者： Deyi Li Deparment of Computer Science and Technology Tsinghua UniversityBeijing 100084China

This paper discusses how intelligent machines have replaced humans in tasks requiring,heavy,and repetitive labor,whilst being better suited to the requirements of these *** increased capacity for brute force computation has facilitated increased collaborative innovation between man and *** example,the intelligent farming machines have overcome the confines of computational power,algorithms,and data,and the next generation of intelligent farming machines is expected to interact,learn,and grow *** the future,in addition to self enhancement,humans are expected to teach machines to learn and *** and engineers will collaborate with machines to accomplish invention,discovery,and ***“embodied intelligence”in the farming machine context,we propose(1)deep learning should be performed iteratively via real-time interactions with the external world;(2)embodied control and self-regulation can ensure coordination between behaviors of machines and their environment;(3)intelligent farming machines are characterized by the ability to interact,learn,and grow autonomously.

关键词： embodied intelligence human-machine interaction machine intelligence

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Detection and Classification of Rheumatoid Nodule using Deep Learning Models

Detection and Classification of Rheumatoid Nodule using Deep...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Dulhare, Uma N. Mubeen, Azmath Department of Computer Science and Artificial Intelligence Muffakham Jah College of Engineering and Technology Hyderabad500034 India Deparment of Computer Science and Engineering University College of Engineering Osmania University Hyderabad500007 India

In the past few years there have been many cases in which the patient may get some form of nodule on the body and it becomes very difficult to identify the type of nodule and its relative disease. Sometimes the nodule may be a simple skin lesion or it may be cancerous cyst or it may be due to a severe condition of rheumatoid arthritis. This research work concentrates on classifying the nodules from a given data set of images and detecting the Rheumatoid Nodule (RN) and its risks. Many studies have been conducted for detection of different nodules but specific to rheumatoid nodule identification and its risks on other organs are minimum as per my cognizance. In imaging study, Deep Convolutional Neural Networks (DCNN) have shown efficient accomplishment, hence the model proposed uses dual three-dimensional Deep Convolutional Networks design for the detection of RN and an improved Res Net for classification, respectively. Rheumatoid Nodule detection was performed through Pure Deep Convolutional Networks (PDCNN) and then feature selection was done using Particle Swarm Optimization (PSO) and later classification was done using the Residual Network (Res-Net) on proficiently learned features from Particle Swarm Optimization. The Deep Learning (DL) model for Rheumatoid Nodule detection and classification, blended with medical aspects, aids in minimizing the errors and False Positive (FP) rate and help in early detection of rheumatoid nodule. The recommended system was assessed on dataset collected from different orthopaedic hospitals in the city and improved results were obtained in comparison to the prevailing approaches. © 2022 Elsevier B.V.. All rights reserved.

关键词： Classification (of information)

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Contact Tracing over Uncertain Indoor Positioning Data (Extended Abstract) 40

Contact Tracing over Uncertain Indoor Positioning Data (Exte...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Liu, Tiantian Li, Huan Lu, Hua Cheema, Muhammad Aamir Chan, Harry Kai-Ho Aalborg University Department of Computer Science Denmark College of Computer Science and Technology Zhejiang University China Roskilde University Deparment of People and Technology Denmark Monash University Faculty of Information Technology Australia Information School University of Sheffield United Kingdom

ISBN: (纸本)9798350317152

Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's indoor mobility. Given a query object o, e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with o for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals. © 2024 IEEE.

关键词： Viruses

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