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Multi-Class Diagnosis of Skin Cancer Using Residual Networks

Multi-Class Diagnosis of Skin Cancer Using Residual Networks

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

作者： Gagandeep Singh Vikas Jindal Computer Science Chandigarh University Mohali Punjab India

Diagnosis of Skin Cancer is a very challenging domain. Skin Cancer has mainly two types: one is Melanoma and the other one is Non-melanomas, Melanoma or Malignant melanoma is one of the most dangerous types of cancer. If we do not treat it at an early stage, in 75% of cases, the death of the patient is confirmed. Many people, due to lack of time, ignore this. They do not go to the doctor; when it is at the advanced stage the patient has little chance of survival so early treatment of this is needed so that precious human life could be saved. We all know that skin cancer is not visible to the naked eye. Doctors use dermoscopy of its images and tell the patient which type of cancer. But this is not easy and for this purpose, we need to have a highly accurate model. Many models are present that give reasonable accuracy but some models only detect two classes. The present model shows the prediction of seven classes of skin lesions so it can detect the problem more accurately and nearly. These are melanoma, Basal Cell Carcinoma, vascular lesion, melanocytic nevi, dermatofibroma ,benign keratosis, and Bowen's. For the detection of seven classes of skin cancer, in this paper, the HAM10000 data set is used along with the application of preprocessing techniques like data augmentation. In the end, ResNet34 is used for the training of the model with fine-tuning which is found to give 97.6% accuracy.

关键词： Training Computational modeling Melanoma Medical services Predictive models Skin Lesions

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Expression of Concern for: A Steadfast Approach to perform Face Detection using Feed Forward Neural Networks

Expression of Concern for: A Steadfast Approach to perform F...

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

作者： Gaurav Gupta Sushant N. Suresh Kumar K Anandhan School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9781665474368

ISBN: (纸本)9781665474375

Now Presently every day with the monstrous expansion in video and picture data sets there’s a superb requirement for programmed comprehension and assessment of data by canny frameworks as physically it’s becoming far off. Step by step face recognition is turning into a test on account of its expanding use in a few applications. the essential advance for face acknowledgment, face investigation, and location of different elements of the face. Detection of dark-coloured faces and poorly illuminated faces don't seem to be well studied within the literature thanks to several challenges. the foremost critical challenge is that there’s inadequate contrast among facial expressions. The face detection algorithms are supported by either grey-level template matching or the computation of geometric relationships among face expressions. The technology nowadays isn't capable enough to acknowledge the face with or without beards or some general changes to the identical. This research illustrates a strong algorithm for pre-processing the face image with and without the beard. This research is implemented on a secured application where the robustness of the face recognition algorithms is enhanced.

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An Agentless Private Cloud Maintenance System Based on Open Source Toolbox

An Agentless Private Cloud Maintenance System Based on Open ...

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IEEE international conference on Orange technologies (ICOT)

作者： Mingsheng Ling Wenjuan Wang Xiaohua Zou Shih-Pang Tseng School of Software and Big Data Changzhou College of Information Technology Changzhou China Academic Affairs Office Hohai University Changzhou Jiangsu China School of Information Science and Technology Sanda University Shanghai China School of Software and Big Data Changzhou College of Information Technology Changzhou China Changzhou Jiangsu China

ISBN: (纸本)9781665454209

The cloud computing provides the more elastic organization of computing resources, and more efficiency of computing works. However, the private cloud is still necessary to various companies and institutions because of the security requirements. Therefore, these companies and institutions must manage and maintain their private clouds by themselves. The low-cost and effective private cloud maintenance alternative is an important issue to the private cloud. This paper proposes an agentless maintenance system based on the open-souce tool, Ansible. The proposed approach integrate the Ansible with the Web platform. It is unnecessary to install any software on the client machines. The maintenance system can remotely control the client machine to complete asset management, performance monitoring, Risk reporting, and so on. The proposed approach can effectively reduce the maintenance cost for the private cloud.

关键词： Cloud computing Costs Companies Maintenance engineering Control systems Software Asset management

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An Intrusion Detection Model for CICIDS-2017 dataset Using Machine Learning Algorithms

An Intrusion Detection Model for CICIDS-2017 Dataset Using M...

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

作者： Shailesh Singh Panwar Y. P. Raiwani Lokesh Singh Panwar Dept. of Computer Science and Engg. H.N.B. Garhwal University Srinagar Garhwal Uttarakhand India Dept. of Electronics and Comm. Engg. Graphic Era University Dehradun Uttarakhand India

ISBN: (纸本)9781665474405

Due to excessive use of the internet, keeping the network secure and transferring data securely across networks has become a difficult task. To recognize distinct types of network (internet) attacks, need Machine learning strategies, to develop various types of the intrusion detection system. The main reason behind using different types of intrusion detection strategies is that the attackers constantly receiving information about the network traffic. Therefore, the intrusion detection system is used to easily identify the attacks and to thwart those attacks. In this paper, we have used the CICIDS-2017 dataset, which is a labeled dataset, for analyzing the result of eight different supervised classification techniques (GaussianNB (GNB), BernoulliNB (BNB), Decision Tree, KNN, Logistic Regression, SVM, Random Forest and SGD). The dataset is divided into five different days; each has different types of class attacks (DoS, PortScan, Botnet, Web Attacks, Infiltration, Heartbleed and DDoS). We have proposed a model with three stages. The first stage data-preprocessing, which includes several steps such as extracting independent and dependent variable, splitting dataset, finding missing value, feature scaling on the dataset and encoding categorical data, then in feature selection stage, finding out unique features using RFE (Recursive feature elimination) technique. Finally, test the result of eight different supervised classification techniques using cross-validation on the CICIDS-2017 dataset.

关键词： Support vector machines Machine learning algorithms Intrusion detection Gaussian processes Telecommunication traffic Feature extraction Encoding

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DB-YOLOv8 Model: Object Detection in Complex Scenarios 10

DB-YOLOv8 Model: Object Detection in Complex Scenarios

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10th international conference on Computer and Communications, ICCC 2024

作者： Yu, Bo Li, Shun Wang, Wei Meng, Yuanze Liu, Dongying Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

The improved DB-YOLOv8 model aims to address the issues of missed detection and false detection that are prone to occur in object detection tasks under complex scenarios, particu-larly in the case of overlapping objects and multiple identical ob-jects. It targets the limitations of the YOLO backbone network caused by fixed kernels that lead to restricted receptive fields and insufficient multi-scale feature learning capabilities due to the in-ability of the spatial and channel attention fusion mechanism to adapt to the input data feature distribution. The DB- YOLOv8 model introduces the deformable convolution C2f (D - C2f) module in the backbone network to achieve adaptive adjustment of the network's receptive field. Additionally, the BiFPN structure is in-troduced in the Neck network, allowing information to propagate bi-directionally between different resolution levels, thereby better fusing multi-scale information. Experimental results demonstrate the outstanding performance of the DB-YOLOv8 model on the VOC and COCO datasets. Validation confirms that its mAP50 and mAP50-95 metrics are improved by 3.3% and 4.7% compared to the YOLOv8 model, respectively. © 2024 IEEE.

关键词： Object detection

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Integration of AI with the Cybersecurity: A detailed Systematic review with the practical issues and challenges

Integration of AI with the Cybersecurity: A detailed Systema...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Melanie Lourens Amar Prakash Dabral Durgaprasad Gangodkar Navjot Rathour Cs Nagabhushanam Tida Anupama Chadha Faculty of Management Sciences Durban University of Technology South Africa Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Law Koneru Lakshmaiah Education Foundation. Guntur Andhra Pradesh India Computer Applications MRIIRS Faridabad

Not only have there been a lot more cyberattacks in recent years, but they have also gotten much more advanced. Therefore, developing a cyber-resilient strategy is of utmost significance. In the event of a cyberattack, traditional security measures are insufficient to prevent data leaks. Cybercriminals have mastered the use of cutting-edge methods and powerful tools for data intrusion, hacking, and assault. Fortunately, the use of artificial intelligence (AI) technologies has the creation of intelligent models for securing systems against attackers. AI technologies can quickly advance to meet complicated problems, making them useful as fundamental cybersecurity tools. AI-based solutions can provide effective and robust cyber defense capabilities to identify malware attacks, network intrusions, phishing and spam emails, data breaches, and alert security incidents when they occur. This paper provides a summary of existing research regarding the advantages of AI in cybersecurity and a review of the impact that AI has had on cybersecurity.

关键词： Systematics Machine learning algorithms Phishing Unsolicited e-mail Supervised learning Reinforcement learning Artificial intelligence

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Cross-Media Topic Detection: Approaches, Challenges, and Applications

Cross-Media Topic Detection: Approaches, Challenges, and Ap...

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international conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Rani, Seema Kumar, Mukesh Computer Science and Engineering Department University Institute of Engineering and Technology Panjab University Chandigarh160014 India

ISBN: (纸本)9789811978661

Developing technologies and new social media platforms (Facebook, YouTube, Flickr, and Twitter) are transforming how people communicate with each other and scrutinize information. Different social media users prefer different mode of information for expressing their views. For instance, Twitter users prefer short text and informally taken photos;YouTube viewers tend to discuss breaking news in the form of short videos with rough editing, and professional journalists usually produce standardized news stories with substantial text and well chosen images. Every day, a large number of videos, images, and text are shared on social media platforms. The cross-media data provide credible insights into public opinion, which are extremely beneficial to commercial firms, governments, and any organization concerned with societal opinions. As a result, assessing such voluminous data is beneficial. When cross-media information is combined, it can provide a more comprehensive and effective description of a given event or issue, as well as improve system performance. Also, in addition to being a significant source of information, cross-media information provided by various media is more robust, has a larger audience, and appears to be a more accurate reflection of real-world events. Consequently, robustly identifying topics from multimodal data from multiple media is a useful and sensible extension of cross-media topic identification. This paper presents a review of various cross-media topic detection approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social aspects

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A Filter Approach Based on Binary Integer Programming for Feature Selection

A Filter Approach Based on Binary Integer Programming for Fe...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Bui Thi Mai Anh Duong Viet Anh Bui Quoc Trung School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (纸本)9781665461672

Feature Selection (FS) aims to identify the most relevant information from the data in order to boost the overall performance of the learning model in terms of both accuracy and computational cost. Traditional FS methods rely on specific criteria to rank the features in order of their relevant scores and filter out low-ranking elements. However, the nature of these greedy approaches may suffer from the locally optimal choice. To tackle this problem, we propose a novel FS approach by reformulating the existing filter selection problem into an integer program which can be solved effectively by branch-and-cut algorithms to find the globally optimal solution (an optimal subset of features). Experiments on a wide range of benchmark datasets indicate that our proposed model outperforms existing state-of-the-art approaches in terms of considerably reducing the search space and computational resources.

关键词： Integer programming Maximum likelihood detection Computational modeling Nonlinear filters Filtering algorithms Feature extraction Information filters

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A Review on Machine Learning and Deep Learning Methods to Fortify Cyberspace

A Review on Machine Learning and Deep Learning Methods to Fo...

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international conference on Computational science and Technology (ICCST)

作者： N Junnu Babu J Bhargav Vanapalli Mounica Eedupalli Sai Kumar Department of Computer Science & Technology Madanapalle Institute of Technology & Science (A) Madanapalle Andhra Pradesh Department of Computer Science & Information Technology Chalapathi Institute of Engineering and Technology (A) India Department of Artificial Intelligence & Data Science Vasireddy Venkatadri Institute of Technology (A) India Department of CSE QIS College of Engineering and Technology (A) India

ISBN: (纸本)9781665476560

Things that have garnered and sparked a great deal of academic attention in the previous decade are Machine learning (ML) and Deep learning (DL). In most people's daily lives, online communities and social media have taken the lead recently, however, this trend comes with serious societal risks. Protecting sensitive data, data networks, and computers against malicious cyber attacks is a difficult task. Cyber security is essential for the safety of our data. Deep learning and machine learning, two relatively new technologies, are merged with cyber-attacks to give a solution to these issues. This article provides a summary of recent research into applying DL to the problem of cyber security, and it examines the many obstacles that must be overcome before this promising field can be fully implemented.

关键词： Deep learning Computers Social networking (online) Scientific computing Weapons Unsolicited e-mail Security

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Mining Frequent Weighted Utility Patterns in Dynamic Quantitative databases

Mining Frequent Weighted Utility Patterns in Dynamic Quantit...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Nguyen Le Huong Bui Minh Nguyen Ham Nguyen Tuong Le Faculty of Information Technology iSPACE Cybersecurity Vocational Training College Ho Chi Minh City Vietnam Faculty of Computing Fundamentals FPT University Ho Chi Minh City Vietnam Faculty of Mathematics Mechanics and Informatics VNU University of Science Hanoi Vietnam Faculty of Information Technology HUTECH University Ho Chi Minh City Vietnam Informetrics Research Group Ton Duc Thang University Ho Chi Minh City Vietnam

ISBN: (纸本)9781665461672

Mining frequent weighted utility patterns (FWUPs) is an important activity in data mining to discover frequent patterns from quantitative databases with considering the weight (mean the importance) of each item. In practical, the weight of each item in a database may varies with time. For example, the weights of the products in a store may change by each month, each quarter, or each year. Therefore, this paper will concern with that issue. At first, we introduce a new problem of mining FWUPs with the dynamic weighted items in the quantitative databases (called dynamic quantitative databases - dQDB). Then, we propose an algorithm named dFWUT using Tidset data structure to solve this problem. Finally, some experiments were conducted to compare the proposed algorithm with a modification of WUN-Miner (denoted by WUN-Miner-Mod), a state-of-the-art algorithm for mining FWUPs in dQDB, in terms of time and usage memory. The experimental results indicate that the dFWUT algorithm outperforms the WUN-Miner-Mod algorithms for mining FWUPs in dQDB.

关键词： databases Heuristic algorithms Memory management data structures Communications technology data mining

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