Artificial Bee Colony (ABC) Algorithm is an optimization algorithm used to find out the global optima. In ABC, each bee stores the information of feasible solution or candidate solution and stochastically modifies this over time, based on the information provided by neighboring bees, it speculative modifies over time and based on the best solution found by the bee itself.. In this proposed work, enhanced ABC algorithm with SPV for travelling salesman problem is used. In this modified bee colony algorithm, additional phase in the form of mutation is used and SPV rule is used in this work for improving local search. After modification, proposed algorithm is implemented on standard travelling salesman problem for checking the efficiency of proposed work. The experimental results are compared with ABC algorithm and ABC with SPV algorithm.

Artificial Bee Colony (ABC) Algorithm is an optimization algorithm used to find out the global optima. In ABC, each bee stores the information of feasible solution or candidate solution and stochastically modifies this over time, based on the information provided by neighboring bees, it speculative modifies over time and based on the best solution found by the bee itself.. In this proposed work, enhanced ABC algorithm with SPV for travelling salesman problem is used. In this modified bee colony algorithm, additional phase in the form of mutation is used after the scout bee phase and the SPV rule is used in this work for improving local search. After modification, proposed algorithm is implemented on standard travelling salesman problem for checking the efficiency of proposed work. The experimental results are compared with ABC algorithm and ABC with SPV algorithm.

The exponential growth of Web services and Web-based applications has led to an enormous volume of data, providing a rich source for mining valuable insights. Web mining differs from traditional data mining due to the unique nature of the data it handles. Web data exists in diverse forms, including web server logs, news pages, and hyperlinks. As the usage of the internet continues to surge, web mining has become essential to extract meaningful information and patterns from these varied data sources. Traditional data mining methods may not be directly applicable to web data due to its unstructured and heterogeneous nature. Web server logs contain valuable information about user interactions, click-streams, and user preferences, which can be mined to understand user behavior and improve website performance. News pages and other forms of web content are valuable sources for sentiment analysis, topic modeling, and information retrieval, helping businesses and researchers gain insights into public opinions and trends. Additionally, web structure mining deals with the analysis of hyperlinks, enabling the discovery of relationships between web pages and identifying authoritative sources. The continuous growth of web-based data necessitates the use of specialized methods in web mining to effectively extract knowledge and valuable patterns. Researchers and practitioners in this field are constantly exploring innovative techniques to make sense of the vast amount of data available on the World Wide Web. The paper provides web mining techniques on web data and presenting the latest advancements, researchers and practitioners can gain insights into the state of the field and identify potential areas for further exploration. This paper also reports the comparisons and summary of various methods of web data mining with applications, which gives the overview of development in research and some importantresearch issues.

Now a day, the latest digital technologies are involved in agriculture field i.e. Big Data. Big Data plays a crucial role in the advancement of smart farming by boosting the productivity of individual farms and removing the risk of a global food crisis by collection and analysis process of Big Data. With the increasing global population and the growing demand for sustainable food production, the agriculture industry leaders and policymakers faces numerous challenges. Fortunately, advancements in technology, particularly in the field of big data analytics, have paved the way for innovative solutions in agriculture, such as smart farming. Smart farming leverages big data to optimize agriculture farming practices i.e. irrigation, fertilization, pest management and crop selection, helps in making real time decisions, improve efficiency, improve operations, boost productivity and increase yields while minimizing resource consumption and environmental impact (such as weather, soil, diseases). Big Data’s help to farmers is by suggesting pesticides the quantity they could use. Hence there arises the need for advanced practical and systematic strategies to correlate the different factors driving the agriculture to derive valuable information out of it. The Big Data has power to develop technologies to achieve the aim of sustainable and smart agriculture with smart farming to enhanced precision farming, predictive analytics, and real time monitoring in agriculture. Smart farming involves the collection and sharing of sensitive information, ranging from crop yields and livestock health to financial data. Safeguarding this data from unauthorized access and maintaining privacy while still allowing for valuable analytics poses a complex ethical and legal dilemma. This digital revolution in agriculture is very promising and will enable the agriculture sector to move to the next level of farm productivity and profitability. This transformation process is not reversible and poised to revolutionize both agriculture and food sector.

Abstract-Association rules mining in large databases is a core topic of data mining. Discovering these associations is beneficial to the correct and appropriate decision made by decision makers. Discovering frequent item sets is the key process in association rule mining. One of the challenges in developing association rules mining algorithms is the extremely large number of rules generated which makes the algorithms inefficient and makes it difficult for the end users to comprehend the generated rules. In this paper we proposed efficient fuzzy association rule mining technique to find all co-occurrence relationships among data items. The proposed method which allows considerably reduced the search space with discover the frequent item set and finding fuzzy sets for quantitative attributes in a database and finally employs techniques for mining of Fuzzy Associate Rules Mining (FARM).