Mobile agent is a piece of computer code that organically goes from one host to the another in a consistent or inconsistent environment to distribute data among users. An autonomous mobile agent is an operational programme that may migrate from one computer to machine in different networks under its own direction. Numerous health care procedures use the mobile agent concept. An agent can choose to either follow a predetermined course on the network or determine its own path using information gathered from the network. Security concerns are the main issue with mobile agents. Agent servers that provide the agents with a setting for prosecution are vulnerable to attack by cunning agents. In the same way agent could be carrying sensitive information like credit card details, national level security message, passwords and attackers can access these files by acting as a middle man. In this paper, optimized approach is provided to encrypt the data carried by mobile agent with Advanced Encryption Standard (AES) algorithm and secure key to be utilized by the AES Encryption algorithm is generated with the help of Hopfield Neural Network (HNN). To validate our approach, the comparison is done and found that the time taken to generate the key using HNN is 1101ms for 1000 iterations which is lesser than the existing models that are Recurrent Neural Networks and Multilayer Perceptron Network models. To add an additional level of security, data is encoded using hash maps which make the data not easily readable even after decrypting the information. In this way it is ensured that, when the confidential data is transmitted between the sender and the receiver, no one can regenerate the message as there is no exchange of key involved in the process.

A mobile agent is a small piece of software which works on direction of its source platform on a regular basis. Because mobile agents roam around wide area networks autonomously, the protection of the agents and platforms is a serious worry. The number of mobile agents-based software applications has increased dramatically over the past year. It has also enhanced the security risks associated with such applications. Most of the security mechanisms in the mobile agent architecture focus solely on platform security, leaving mobile agent safety to be a significant challenge. An efficient authentication scheme is proposed in this article to address the situation of protection and authentication of mobile agent at the hour of migration of across multiple platforms in malicious environment. An authentication mechanism for the mobile agent based on the Hopfield neural network proposed. The mobile agent's identity and password are authenticate using the specified mechanism at the moment of execution of assigned operation. An evaluative assessment has been offered, along with their complex character, in comparison to numerous agent authentication approaches. The proposed method has been put into practice, and its different aspects have been put to the test. In contrasted to typical client-server and code-on-demand approaches, the analysis shows that computation here is often more safe and simpler.

Aspect Based Sentiment Analysis (ABSA) aims to determine sentiment with respect to specific aspects of a text, providing more detailed insights than conventional sentiment analysis which assigns a single polarity to the whole text. However conventional techniques often fail to capture fine grained aspect level sentiment limiting their effectiveness in real world applications such as product reviews and customer feedback analysis. In this article proposes an innovative ABSA framework that synchronizes enhanced feature engineering with a lightweight hybrid deep learning architecture. In the proposed method Text preprocessing is done using a BERT tokenizer followed by feature extraction with an improved TF-IDF approach and Aspect Term Extraction (ATE) allowing the model to capture both global context and aspect level information. A hybrid classifier synchronizes Link-Net and SqueezeNet for fast and accurate sentiment classification. Experiment was conducted on data for Restaurant Reviews containing 10,000 reviews. The dataset splitting in training, validation and testing set 70:15:15 respectively. Fiive-fold cross-validation was applied to ensure robustness of proposed framework. Outcome of analysis demonstrate that the proposed farmwork gained a high accuracy of 97.7%, sensitivity 96.4% and F-1 Score 91.0% as compare to several traditional framework. The Proposed technique suggests a balanced tradeoff between performance and computational cost making it suitable for real time ABSA applications.

The process by which customers search for suitable products to finish purchasing or other associated chores in a virtual shopping environment is examined in this study. All these factors come together in the payment decision, which is what a person call the integration and unity of the payment decision. This may discover probable links between a series of customer behaviors by using analysis, prediction, and forecasting analysis during online shopping. With the deployment of this technique, consumer groups could be quickly identified and segmented, allowing for the mapping of differences between them as well as a comparison of customer buying behavior across various market segments. This strategy worked quickly and effectively. The analysis, prediction and forecasting the user behavior during the online shopping. This paper includes one example of Kaggle which is based on the buying behaviors of certain products. A consumer segmentation analysis found that most customers were inactive or made infrequent purchases. There is a huge gulf between top customers and lost low-cost consumers in terms of recency, frequency, and revenue. The company should have a different approach for each section of the market. So, analysis, prediction and forecasting of User Behavior during Online Shopping must investigate with categorical approach with modified A-Priori algorithm for better explanation of the user data