Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction.

/pdf/attention-mechanism-and-depthwise-separable-convolution-3ex01tu9.pdf

Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

Study of deep learning techniques for medical image analysis: A review

A spectral–spatial 3D-convolutional capsule network for hyperspectral image classification with limited training samples

The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds tremendous potential across a wide array of fields. By leveraging AI, the processing and interpretation of the vast and complex data generated by HSI are significantly enhanced, allowing for more accurate, efficient, and insightful analysis. This powerful combination has the potential to revolutionize key areas such as agriculture, environmental monitoring, and medical diagnostics by providing precise, real-time insights that were previously unattainable. In agriculture, for instance, AI-driven HSI can enable more precise crop monitoring and disease detection, optimizing yields and reducing waste. In environmental monitoring, this technology can track changes in ecosystems with unprecedented detail, aiding in conservation efforts and disaster response. In medical diagnostics, AI-HSI could enable earlier and more accurate disease detection, improving patient outcomes. As AI algorithms advance, their integration with HSI is expected to drive innovations and enhance decision-making across various sectors. The continued development of these technologies is likely to open new frontiers in scientific research and practical applications, providing more powerful and accessible tools for a wider range of users. 

Synergy between Artificial Intelligence and Hyperspectral Imagining—A Review

Silica Coating of Carboxymethyl Tamarind Kernel Polysaccharide Beads to Modify the Release Characteristics.

The quality of software is dependent on testing as per user specifications and requirements. So it is quite challenging to design, prioritize and optimize test cases to achieve quality. Different testing tools can be used for software testing either manually or automatically. During the recent studies it is found that automated software testing is better than manual testing by using heuristic search. In this paper presents a survey on genetic algorithm approach for random generation of test cases in functional software testing

Genetic Algorithm Approach For Test Case Generation Randomly: A Review

The World Wide Web is a huge source of hyperlinked information contained in hypertext documents. Search engines use web crawlers to collect these web documents from web for storage and indexing. The prompt growth of the World Wide Web has posed incomparable challenges for the designers of search engines and web crawlers; that help users to retrieve web pages in a reasonable amount of time. In this paper, a review on need and working of a search engine, and role of a web crawler is being presented.

/pdf/design-issues-for-search-engines-and-web-crawlers-a-review-58hgfyyu3p.pdf

Design Issues for Search Engines and Web Crawlers: A Review

The amount of information on Deep Web is growing rapidly, as most of the organizations put their content online behind the search forms. The information behind the search forms is often of very high quality and can be extremely valuable to many users. Therefore there has been increased interest in retrieval of deep web data. The conventional crawlers retrieve only publicly indexable web ignoring the large amount of high quality information deep behind search forms hence there is a need to build a deep web crawler that can crawl to deep web. But the main challenge for a deep web crawler is to model a query interface and to generate meaningful queries for the search interfaces. In this paper authors have integrated the search interfaces on the basis of the semantic relationships to generate the Generalized Search Interface.

Integration of Search Interfaces in Deep Web Databases for Specific Domains

The number of web pages is increasing into millions and trillions around the world. To make searching much easier for users, web search engines came into existence. Web Search engines are used to find specific information on the World Wide Web. Without search engines, it would be almost impossible to locate anything on the Web unless or until a specific URL address is known. This information is provided to search by a web crawler which is a computer program or software. Web crawler is an essential component of search engines, data mining and other Internet applications. Scheduling Web pages to be downloaded is an important aspect of crawling. Previous research on Web crawl focused on optimizing either crawl speed or quality of the Web pages downloaded. While both metrics are important, scheduling using one of them alone is insufficient and can bias or hurt overall crawl process. This paper is all about design a new Web Crawler using VB.NET Technology.

/pdf/a-novel-interface-to-a-web-crawler-using-vb-net-technology-50p9r2vj6y.pdf

A Novel Interface to a Web Crawler using VB.NET Technology

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