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

Green marketing stems from the degrading of ecological environment and consumer's increasing sense of(environmental) protection.The essence of green marketing is to response actively to man's increasing concern for ecological(environment) and make choices and decisions in marketing by adhering to the environmental protection principle and the(ecological) principle and building up green technology,green market and green industry.Therefore,green marketing has profound ethical connotation.However,green marketing in present Chinese enterprises is actually in conflict with the logics of enterprise behavior.

On "Green Marketing"

Water quality prospective in Twenty First Century: Status of water quality in major river basins, contemporary strategies and impediments: A review.

Concerns have been expressed by manufacturers and customers about the environmental impact of products during recent decades. Consumers and manufacturers have directed their attention toward environment friendly products that are presumed to be “green” or environment friendly like low power consuming (energy-efficient) electrical appliances, organic foods, lead free paints, recyclable paper, and phosphate free detergents. Indian marketers are also realizing the importance of the Green Marketing Concept. Although a variety of research on green marketing has been conducted across the globe; little academic research on consumer perception and preferences has been carried out in India. This research provides a brief review of environmental issues and identifies the green values of the consumers, their level of awareness about environmental issues, green products and practices. This paper highlights the consumers’ perception and preferences towards green marketing practices and products with the help of a structured questionnaire. A study was conducted on 106 respondents. High level of awareness about green marketing practices and products was found among the consumers. Green values were also found to be high among the respondents. Research has given good insights for marketers of the green products and suggests the need of designing the marketing communication campaigns promoting green products due to high green value among the consumers. Results of regression analysis reveals the view that overall green values, awareness about green products and practices and the perception regarding seriousness of marketing companies towards green marketing had positive significant impact on consumer persuasion to buy and prefer green products over conventional products.

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Green Marketing: A Study of Consumer Perception and Preferences in India - eScholarship

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through these online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These fake news can be a propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting the fake news is described in this systematic literature review.

Detecting Fake News using Machine Learning: A Systematic Literature Review

Raw datasets collected for fake news detection usually contain some noise such as missing values. In order to improve the performance of machine learning based fake news detection, a novel data preprocessing method is proposed in this paper to process the missing values. Specifically, we have successfully handled the missing values problem by using data imputation for both categorical and numerical features. For categorical features, we imputed missing values with the most frequent value in the columns. For numerical features, the mean value of the column is used to impute numerical missing values. In addition, TF-IDF vectorization is applied in feature extraction to filter out irrelevant features. Experimental results show that Multi-Layer Perceptron (MLP) classifier with the proposed data preprocessing method outperforms baselines and improves the prediction accuracy by more than 15%.

Fake News Detection Enhancement with Data Imputation

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Machine Learning in Agriculture

Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when there is no systematic news verification process, which is the case for traditional media. Detecting false information, for example, detection of rumors is a non-trivial task, given the fast-paced social media environment. In this work, we proposed an ensemble model, which performs majority-voting scheme on a collection of predictions of neural networks using time-series vector representation of Twitter data for fast detection of rumors. Experimental results show that proposed neural network models outperformed classical machine learning models in terms of micro F1 score. When compared to our previous works the improvements are 12.5% and 7.9%, respectively.

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

Rumor detection becomes increasingly important in social media. The effects of rumor propagation are dreadful in case of time-critical events, for example, during natural disasters. In this paper, we proposed a multiple time-series data analysis model to detect rumors on Twitter. Instead of checking the contents of the tweets, the proposed method only uses temporal properties of the tweets. As a result, the computational complexity measured by the training time and prediction time has been reduced significantly, which allows quick detection of rumors. Experimental results show that the proposed model combined with Gaussian Naive Bayes classifier achieved a high precision score of 94%.

Multiple Time-Series Data Analysis for Rumor Detection on Social Media

False information has become a weapon in cyber-warfare. How to detect false information effectively and efficiently on social media is a challenging problem. In this study, a novel method of rumor detection on Twitter tweets is proposed as a proof-of-concept for fast detection of false information on social media. Specifically, the proposed method will use the propagation pattern of the tweets to detect false information rather than the contents. As a result, the proposed method is very effective in reducing the dimensionality of the input feature set, and it requires much less computational time compared to content-based methods. Extensive experiments on PHEME dataset, a collection of Twitter rumors and non-rumors posted during five breaking news, have been performed to demonstrate the effectiveness of the proposed method. We also observe that deep learning models such as recurrent neural networks outperform classical machine learning models in terms of micro-F score.

Chandra M. M. Kotteti

Papers

Fake News Detection Enhancement with Data Imputation

Machine Learning in Agriculture

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

Multiple Time-Series Data Analysis for Rumor Detection on Social Media

Rumor Detection on Time-Series of Tweets via Deep Learning