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Software agent is the one of the most recent contribution in the field of Information Technology. The field of software agents is a broad and rapidly developing area of research, which encompasses a diverse range of topics and interests. In order to study the various methodologies for agent design, implementation, commercial use of it, a sample survey is required.This paper gives an overview of recent research on the software agents, agent communication languages (ACL), the different ontology use for software agent, also the summary of ACL and different tool kits for developing a software agent.

During rainy times, the impact of outdoor vision systems gets considerably decreased owing to the visibility barrier, distortion, and blurring instigated by raindrops. So, it is essential to eradicate it from the rainy images for ensuring the reliability of outdoor vision system. To achieve this, several rain removal studies have been performed in recent days. In this view, this paper presents a new Faster Region Convolutional Neural Network (Faster RCNN) with Optimal Densely Connected Networks (DenseNet)-based rain removal technique called FRCNN-ODN. The presented involves weighted mean filtering (WMF) is applied as a denoising technique, which helps to boost the quality of the input image. In addition, Faster RCNN technique is used for rain detection that comprises region proposal network (RPN) and Fast RCNN model. The RPN generates high quality region proposals that are exploited by the Faster RCNN to detect rain drops. Also, the DenseNet model is utilized as a baseline network to generate the feature map. Moreover, sparrow search optimization algorithm (SSOA) is applied to choose the hyperparameters of the DenseNet model namely learning rate, batch size, momentum, and weight decay. An extensive experimental validation process is performed to highlight the effectual outcome of the FRCNN-ODN model and investigated the results with respect to several dimensions. The FRCNN-ODN method produced a higher UIQI of 0.981 for the applied image 1. Furthermore, on the applied image 2, the FRCNN-ODN model achieved a maximum UIQI of 0.982. Furthermore, the FRCNN-ODN algorithm produced a higher UIQI of 0.998 on the applied image 3. The simulation outcome showcased the superior outcome of the FRCNN-ODN (Optimal Densely Connected Networks) model with existing methods in terms of distinct measures.

Private Banks in India, are owned by individuals or group of limited individual and not by government. Here in India, private banks represent that most of the part of equity or shares are hold by private shares holders and not by government. The locus of control is reflected in terms of the particular degree upto which the private shares holders or the bank employee’s belief that they have power over their private banking events. This research study aims to find out locus of control among bank employees with special reference to private banks based on gender difference as well as two dimensions of locus of control viz. external and internal. Survey method was adopted for this study which covers four private banks within the Nagpur city. Sample size of 200 bank employees consists of bank managers, cashiers, clerks accountant were provided with an online Google form questionnaire. The questionnaire consists of seven questions related to their work, job satisfaction, colleagues clients etc. Initially Cronbach’s alpha test using pilot survey on 50 pre samples of bank employees was used to determine the internal consistency of data model. As second step Explorative statistics gave the trends among the bank employees towards the seven questions. The Descriptive statistics then quantitatively describes the features from a collection of information that can reveal the influence towards the three hypotheses under consideration. Lastly the Inferential statistics based two groups of genders interpret the facts as to accept or reject the three hypotheses under consideration. The study concludes the pro and cons and the prime factors which leads towards the external and internal locus of control among the employees.

As Agriculture is the pivotal point of survival, rainfall is the important source of its cultivation. Rainfall prophecy has always been a major problem as a prophecy of downfall gives awareness to people and  to know in advance about rain to take necessary precautions to cover their crops from rain. A particular dataset is taken from the Kaggle community and this design predicts whether it will rain henceforth or not by using the rainfall in the dataset. Cat Boost model is executed in this design as it’s an open-sourced machine knowledge algorithm, and features great quality without parameter tuning, categorical point support, bettered delicacy, and fast prophecy. Cat Boost model is a Grade boosting toolkit and two critical algorithms classical and innovative are introduced to produce a fight in prophecy shift present in presently being prosecutions of grade boosting algorithms. Cat Boostperformed truly well giving an AUC (Area under wind) score0.8 and a ROC (Receiver operating characteristic wind) score of 89. ROC is called an assessing wind whereas AUC presents a degree or measure of separability as the model is professed enough to distinguish between classes. An Exploratory data analysis is done to examine data distribution, and outliers and provides tools for imaging and understanding the data through graphical representation.