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共 595 条记录，以下是161-170 订阅

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Development of machine learning Based Fraudulent Website Detection Scheme 5

Development of Machine Learning Based Fraudulent Website Det...

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5th IEEE International conference on Knowledge Innovation and Invention, ICKII 2022

作者： Chen, Shyh-Wei Chen, Po-Hsiang Tsai, Ching-Tsorng Liu, Chia-Hui Tunghai University Department of Computer Science Taichung City Taiwan Chinese Culture University Department of Applied Mathematics Taipei Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

the development of mobile computing and e-commerce has greatly changed traditional transactions and grown online shopping. People are buying and selling goods on websites or social platforms. However, there are many malicious and counterfeit products on fraudulent websites to deceive consumers and make high improper profits. Due to the obvious increase in the number of such fraudulent websites, it is difficult to identify and detect these websites by manual inspection. In order to solve this problem, we propose an intelligent detection mechanism by using a machine learning approach to classify fraudulent websites. We use data set containing 300 legitimate websites, 300 fraudulent websites, and 15 features for training. In two machine learning algorithms, Random Forest and Deep Neural Networks, we divided the training set and the test set in a ratio of 8:2. Finally, the prediction is compared with the previous research results. the experimental results show that the RF accuracy of the random forest algorithm is 99.3% which is better than other deep neural networks algorithms. © 2022 IEEE.

关键词： Deep neural networks

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Proceedings of the 5th International conference on Information Technology for Education and Development: Changing the Narratives through Building a Secure Society with Disruptive Technologies, ITED 2022

Proceedings of the 5th International Conference on Informati...

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5th International conference on Information Technology for Education and Development, ITED 2022

ISBN: (纸本)9781509064229

the proceedings contain 103 papers. the topics discussed include: improved XM-18 controller using petroleum liquid gas-based automatic heating alternative for egg incubation in developing countries;an automatic railway level crossing system with crack detection;classification and recommendation of food intake in West Africa for healthy diet using deep learning;a cognitive analysis of Covid-19 on the Africa economy using linear regression;a survey of digital watermarking techniques for data protection in cloud computing;performance and accessibility evaluation of university websites in Nigeria;cascade of deep neural network and support vector machine for credit risk prediction;comparative analysis of the performance of various support vector machine kernels;ring learning with error-based encryption scheme for the privacy of electronic health records management;quantitative approach to automated diagnosis of malaria from Giemsa-thin blood stain using support vector machine;a study of the effectiveness of monophasic electrical stimulation in enhancing neuromuscular tissue function;and biocryptosystems for template protection: a survey of fuzzy vault.

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A Review of Data-Driven Approaches with Emphasis on machine learning Base Intrusion Detection Algorithms 5

A Review of Data-Driven Approaches with Emphasis on Machine ...

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5th International conference on Information Technology for Education and Development, ITED 2022

作者： Musa, Maryam Abdullahi Gital, Abdulsalam Ya'U Ibrahim, Kabiru Musa Chiroma, Haruna Abdulrahman, Mustapha Lawal Umar, Ibrahim Muhammad Abubakar Tafawa Balewa University Department of Mathematical Science Bauchi Nigeria Abubakar Tafawa Balewa University Department of Management Information Technology Bauchi Nigeria Department University of Hafr Al Batin Saudi Arabia

ISBN: (纸本)9781509064229

the importance of the internet across the globe cannot be over-emphasized as such network security is essential to curb future attack occurrences. Cyber-attacks like DDoS and Ransomware yielded a lot of damage to connected devices by endangering and accessing them, notwithstanding these damages are air marked to be on the rise. To overcome these issues, machine learning has been used in different computing aspects such as cyber-Intrusion Detection. Recently, deep learning, extreme learning, and deep extreme learning networks have superseded machine learning in this context due to their iterative hidden layers that can manipulate complex features of cyber intrusion data. Hence, this research surveys the application of data-driven intelligent algorithms for cyber security attack detection in comparison to conventional machine learning techniques. the review focuses on the performance evaluation of several state-of-the-art intelligent algorithms and provides research gaps and future 2trends in the context of Data Security Attacks and Cyber Intrusion Detection. © 2022 IEEE.

关键词： Intrusion detection

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A Review: Disease Detection in Wheat Plant using Conventional and machine learning Algorithms 5

A Review: Disease Detection in Wheat Plant using Conventiona...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Paidipati, Sai Ram Pothuneedi, Sathvik Gandham, Vijaya Nagendra Jain, Lovish Kumar, Sandeep Jain, Arpit Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350398267

Decade by decade entire globe has been experiencing rapid population growth that leads to many problems in food management and security. Wheat is a crucial crop that is cultivated in the world because wheat provides 20% of calories and protein for human beings. Due to its heavy contribution to human beings and cattle nutrition, there is a rapid growth in production in the past few decades. the wheat production was 106.41 million metric tons from India in 2021-2022. As per our survey, the total wheat production in Uttar Pradesh is 35 million metric tons approximately. As the cultivation is high, there is a high chance of diseases. As per nature, wheat cultivation is affected by many conditions, which reduces production from time to time. In the year 2021-2022, the total production of wheat in entire India is 106.41 million metric tons out of this 30% (31 million metric tons), which is a severe threat to food bonds. In such cases, the early detection of diseases which affects the plant most in the growth stage by applying various crop disease detection techniques is necessary. In this study, an overall analysis of the different methods used for disease detection in plants using artificial intelligence based on machine learning and deep learning techniques has been analyzed. © 2022 IEEE.

关键词： Deep learning

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Novel Data-Based Model for Future Epidemiology 5

Novel Data-Based Model for Future Epidemiology

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5th International conference on Artificial Intelligence, Big Data, computing and Data Communication Systems (ICABCD)

作者： Mathebula, Dephney Univ South Africa Decis Sci Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. this goal is often realized using closed form expressions that significantly utilize systems of differential equations. these systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non - active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. this is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required the discussion in this research proposes a suitable solution in this regard the proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. the research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet.

关键词： Artificial Intelligence Goals Epidemiology Internet Crawlers machine learning computing Framework Conventional Epidemiological Cycle COVID-19 bi-directional communications

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A machine learning-Based Approach for Enhancing Student learning Experiences 5th

A Machine Learning-Based Approach for Enhancing Student Lear...

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5th International conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Bhaskar, T. Tribhuvan, R.R. Department of Computer Engineering Sanjivani College of Engineering Maharashtra Kopargaon India

ISBN: (纸本)9789811986680

the tried-and-true learning method of outcome-based learning (OBL) is based on a specified set of goals. there are three parts to OBL: educational objectives, program outcomes, and course outcomes (COs). At the end of each course, faculty members may take several ML-based recommended actions to enhance the quality of learning and instruction. Some students may be encouraged to engage in dangerous activities due to the sheer volume of classes and professors engaged. As a consequence of this research, multiple machine learning algorithms may be used to predict the correct course of action based on college course requirements and academic records. Different issue change strategies and versatile methodologies, for example, one-versus-all, parallel importance, naming power set, series arrangement, and custom order ML-KNN, might be utilized to a dataset to tackle the issue. At the Institute of Computer and Information Sciences, the suggested recommender ML-based framework is used as a contextual investigation to help academic staff improve learning quality and instructional approaches. According to the findings, the recommended recommendation method provides a greater number of options for enhancing students’ educational experiences. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Irrigation Prediction using machine learning in Precision Agriculture 5

Irrigation Prediction using Machine Learning in Precision Ag...

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5th IEEE International conference on Computer and Informatics Engineering, IC2IE 2022

作者： Puspaningrum, Alifia Sumarudin, A. Putra, Willly Permana Politeknik Negeri Indramayu Informatics Engineering Indramayu Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

Agriculture has contributed to the economy in every country. Currently, agriculture has coped with several challenges, such as irrigation in water management. Agriculture used traditional techniques to manage irrigation. So, it requires much cost and labour. Besides, machine learning has evolved in various sectors to increase quality within. In a previous study, Fuzzy was proposed to model the data in statics based on rules and had difficulty computing the data automatically. this paper proposed irrigation prediction by using a machine learning algorithm. Classification algorithms, namely Support Vector machine (SVM), K-Nearest Neighbour (KNN), Naive Bayes, Random Forest, and Decision Tree, are investigated to forecast accurate irrigation. this paper calculates accuracy, precision, recall, and Fl-score to evaluate the algorithm's performance. Experimental results show that Decision Tree outperforms other algorithms using its performance using the same agricultural data according to accuracy, recall, precision, and fl-measure score. In addition, the results of this experiment will be implemented in predicting automatic paddy irrigation using loT -based sensor data. © 2022 IEEE.

关键词： Irrigation

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Revisiting Metric Space Similarity Methods for High Performance Deep Embedding learning 5

Revisiting Metric Space Similarity Methods for High Performa...

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5th IEEE International conference on Cognitive machine Intelligence, CogMI 2023

作者： Yi, Chungheon Choi, Wonik Liu, Ling Inha University Dept. of Info. and Comm. Engineering Incheon Korea Republic of College of Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350323832

With the growing popularity of large language models (LLMs) and the few-shot learners, deep neural network based metric space learning has attracted considerable attention. Metric space learning aims at learning feature embeddings through training embedding models, followed by computing the metric space similarity of feature vectors in the learned embedding space for different types of downstream learning tasks, such as multi-modal embedding learning for cross-modal retrieval. However, existing methods on cosine-based metric space similarity suffer from several limitations. First, when applying the cosine similarity to high dimensional vectors, the shapes of the two vectors must be identical, Second, the metric space similarity on the concatenate layer may suffer from misalignment of the vector elements because the concatenate layer may not reflect the positions of the elements in the two vectors, leading to deteriorated performance with respect to the learning success rates. this paper will review the limitations of the metric space similarity methods, such as cosine similarity or concatenate layer, and provide our insight on some solution approaches we have developed to address these limitations. © 2023 IEEE.

关键词： Embeddings

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Novel machine learning-based Soil Characteristic Analysis 5

Novel Machine Learning-based Soil Characteristic Analysis

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Vivek, V. Sharma, Sachin Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Since the computer's invention, every subject of knowledge has been digitalized, allowing computer users to view all available information. Because of this, data in every industry is growing exponentially. this article explains why researchers study agriculture. We projected three new classification approaches to overawe these restrictions: Hybrid KNN classification methods produce and choose prototypes from an initial training set. these methods include training set reduction KNN, which uses prototype selection to reduce training sets, training set reduction, which creates training set prototypes utilizing either the Elbow or Silhouette technique, and hybrid classification approaches, which use both prototype generation & selection mechanisms. If any of these strategies are to succeed, the KNN classifier must finish its classification work faster and use less space. Utilizing a soil fitness card agricultural dataset, we tested our unique classification algorithms and found that they solve our concerns. © 2022 IEEE.

关键词： Nearest neighbor search

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Research on Efficient Landslide Prediction Approaches using machine learning Techniques 5

Research on Efficient Landslide Prediction Approaches using ...

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5th IEEE International conference on Advances in Science and Technology, ICAST 2022

作者： Varangaonkar, Payal Rode, S.V. K. J. Somaiya Institute of Engineering and Information Technology Computer Engineering Department Mumbai India Sipna College of Engineering and Technology Electronics and Telecommunication Department Amravati India

ISBN: (纸本)9781665492638

A landslide is a condition in which a huge amount of rock particles slide or break off down a slope, resulting in great natural and physical loss in addition to the lives of many people. In large parts of the world, massive damage is caused by landslides. the utility of remotely sensed images is used for landslide detection, mapping, prediction, and assessment round the world. this systematic analysis might also make contributions to better expertise the considerable use of remotely sensed records and spatial evaluation techniques to conduct landslide research at more than a few scales. the machine learning algorithms in particular ANN and SVM are used as soft computing techniques for landslide prediction. the accuracy obtained from SVM is 91.78% and with ANN 93.38%. In India landslide is famous phenomena of Himalayan location, Western Ghats and southern Nilgiris Mountains. Such losses must be avoided if right perception tool is available that would notify about the event in boost. With the use of proposed soft computing techniques this paper projects unique landslide prediction techniques with cognizance on western India. © 2022 IEEE.

关键词： Remote sensing

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