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

Mobile Agents are a new type of computing that is replacing the client-server approach. Mobile agents are little pieces of code that function automatically on behalf of the owner. Many applications, such as e-commerce, parallel computing, network management, and health care, use mobile agents. The healthcare industry is one of the most growing fields in any country. As the population increases day by day the requirement of medical resources is proportionally increasing. Due to high patient demand and a severe lack of medical resources, a remote medical healthcare system is required. However, the deployment of remote healthcare systems over the Internet introduces a new set of challenges, including interoperability among heterogeneous networks and the need to navigate through multiple public systems dispersed over insecure networks. This paper explores how mobile agents can effectively tackle these challenges, especially in heterogeneous and potentially malicious environments. A key focus of this research is the development of a mathematical model for secure medical information retrieval. This model incorporates a variable threshold secret-sharing mechanism, employing the Chinese remainder theorem and multiplicative inverse with modular arithmetic at different levels. By integrating these cryptographic techniques, the proposed approach ensures the confidentiality and integrity of medical information during its retrieval, contributing to the overall safety and robustness of mobile agent computing in healthcare scenarios.

Aspect-based opinion mining (ABOM) has emerged as a crucial task in natural language processing (NLP) aimed at extracting fine-grained opinions about specific aspects of products, services or entities from textual data. With the exponential growth of user-generated content on the Internet, such as product reviews, social media posts and forum discussions, understanding the opinions expressed towards different aspects has become increasingly important for businesses, researchers and consumers alike. Traditional sentiment analysis approaches often treat the entire document or sentence as a unit, neglecting the fact that opinions are often expressed towards specific aspects or features. However, ABOM goes beyond overall sentiment analysis by identifying the aspects being discussed and extracting opinions related to each aspect individually. This granular analysis provides deeper insights into the strengths and weaknesses of products or services, enabling businesses to make informed decisions and enhancing user experiences. In natural language processing, aspect-based opinion mining is an essential task that seeks to analyse opinions stated in text documents about particular qualities or aspects of entities, products or services. Organisations and customers may make better decisions with the aid of the customer-based summary produced from the identified aspect words. The main goal of this work is to estimate the polarity of aspect terms in a given textual collection. Sentiment polarity estimation is needed for a large number of samples or reviews in the dataset. In this paper, the approach of deep memory network is utilised. When assessing the sentiment polarity of an aspect, the deep memory network technique explicitly takes into account the significance of each context word. Deep memory networks are able to store and make use of historical context, which facilitates a more sophisticated comprehension of the connections among various aspects and opinions in the reviews. They also enhance the recognition and categorisation of emotions linked to particular features. Experiments are conducted on laptop and restaurant datasets and the performance of the model is then evaluated with different classifiers.

Cloud computing is an aligned distributive architecture consisting of a network of associated, digitized machines that are dynamically provisioned as individual computing resources depending on SLAs (service-level agreements) between customers and vendor of services. It provides easy, on-demand access to shared hardware, applications, and data pools. This technology allows businesses and individuals to store and analyze data in third-party data centers. While various load balancing techniques for cloud computing have been introduced recently, they do not always achieve the intended outcomes. It offers simple, usage-based accessibility to shared hardware instruments apps, and data pools. Businesses and individuals can store and analyze data in independent data centers thanks to this technology. Numerous techniques to balance load in cloud settings have been presented recently, but they don’t always produce the intended outcomes. This work presents a hybrid optimization solution for load balancing that integrates Particle Swarm Optimization (PSO) with genetic algorithms. The efficiency level of the presented solution is assessed according to consumed energy, duration of response, and the overall migrations, comparing it to the ACO and PSO after implementation in MATLAB. The findings indicate that the new solution surpasses existent optimization solutions for every evaluated criterion.

In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models.

Understanding the differences of high accuracy and data driven customer behaviour is one of the essential components of success in the e-commerce industry because customer behaviour varies from person to person depending on their segmentations. Owners will be able to recognise their desired customers by comprehending customer behaviour. They will be able to better target their marketing efforts, boost sales, and control costs. Applications of artificial intelligence in this area have a significant positive effect on operations. The most attributes that can influence a customer’s behaviour can be found and predicted by business partners using a data mining prediction model. The current study therefore identifies better ways to improve business decision-making through the use of AI and data analytics, which will aid in comprehending customer behaviour. This research, brings forth ideas and concepts to make models more data driven and more accurate through a more reactive approach on model designing, through clever infrastructural designing. This paper proves that the proposed method can assure users to get better results through reactive-data methodologies. This paper proves it practically by taking a simple classification problem, in contract to customer-behaviour prediction.

A certain voice can be recognised using voice recognition technology. The cornerstone for speaker identification is voice signals. Voice targeting is applicable to a wide range of applications, including voice mail, database access, phone banking, and phone purchasing. The ability to enter one's voice for verification is among the most potent uses of voice recognition for security. The fundamental means of interpersonal communication is speech. The technique of translating speech sounds into appropriate text is known as speech recognition. Over the past few years, speech recognition technology has advanced significantly. Nonetheless, there are other significant study obstacles, such as variations in speaker and language, ambient sound, word size, etc. This study aims to give a comprehensive overview of speech acceptance by summarising the several approaches utilised in the standard speech system and describing the numerous processes involved.

Mining the sentiment target included in a sentence or text is the main aim of Aspect Based Sentiment Analysis (ABSA). This task's main challenge is to efficient extraction of a specific sentiment item's sentiment polarity. This work proposes a model namely Improved ABSA with Deep Belief Network-Recurrent Neural Network (DBN-RNN), which includes 3 working phases. Processes like stemming, stop word removal, lemmatization as well and tokenization are conducted in the initial pre-processing phase. Furthermore, in the aspect sentiment extraction phase, improved aspect term extraction (I-ATE) along with cosine similarity and word co-occurrence are used to extract the complex features from the pre-processed data. In the sentiment analysis phase, a hybrid classification model named DBN-RNN is utilized to effectively categorize the sentiments as neutral, positive, and negative polarities. The performance of proposed work is evaluated in terms of different performance measures.

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.

Mobile agent is a processing unit works on the behalf of host computer. Mobile agent with intelligence provides a new computing prototype that is totally different from conventional prototype. Mobile agents are automatically itinerating from one host Computer to another host computer and execute assigned task on the behalf of user in heterogeneous environment under own control. Because mobile agents roam around distributed networks automatically, the security of the agents and platforms is a major concern. 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 protection systems in the mobile agent paradigm focus on platform security and provide few guidelines for mobile agent security, which is still a challenging topic. There is a risk to information carries by mobile agents from the malicious mobile agents who can modify and steal the confidential information. In this paper proposed multilevel authentication framework of mobile agents and platform based on Lagrange interpolation and fast Fourier transformation (LIFFT). In this frame work ‘n’ number of mobile agent have two level of security first level key used authentication and second level of key used for execution of mobile agents.

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.

Opinion mining, natural language processing technique task is used for extracting information from unstructured data available on the internet. Now a days, users’ shared aspect-based online views have become a reliable source of information for customers making purchasing decisions and business purposes. In this paper, a new aspect/feature based opinion mining approach with four primary components: preprocessing, extraction of aspect sentiment, grouping of aspect term and Aspect Sentiment Classification are discussed. The preprocessing of the user’s reviews is performed by tokenization, lemmatization, stemming and removal of stop words. The extraction of aspect sentiment is carried out in two stages: POS tagging and Rule-based lexicon extraction. Based on the estimated semantic similarity score, the retrieved characteristics are divided into primary and secondary aspects. Both the primary and secondary aspects are then multiplied by the weight function. Furthermore, the suggested Particle with Levy Updated Sparrow Search Algorithm model optimizes the primary and secondary weighting elements to make the categorization more suitable in terms of opinion. The hybrid optimization model is created by combining the conventional Particle Swarm Optimization (PSO) with sparrow search algorithm (SSA). Following that, the retrieved attributes for final classification take place via Neural Network. The opinions of the reviews are characterized as a positive, neutral or negative.

Customer behavior prediction is an important task for any company to improve the sale of product. Retailer used Market Basket Analysis pattern of customer to identify the behavior of customers. On the basis of previous basket analysis one most important feature suggestion for customer for next purchasing. Market Basket Analysis was successfully applied to evaluate a large amount of data to understand customer behavior of purchasing patterns. To facilitate reordering and maintaining adequate product stock, this paper will explain how Instacart can use its consumer transaction history and concentrate on descriptive analysis of customer buying habits, objects frequently purchased around each other, and units purchased from of the shop. Additionally, to locate client subsets and cluster with similar purchasing habits and to visualize the data to make useful recommendations geared toward enhancing revenue and satisfaction using segment and forecasting framework. In this paper design a machine learning framework to forecast whichever previously purchased item will be in the customer future purchasing. Proposed model has high accuracy on the same data set is 74.16 percent.

- Here, a special feature-rich safe route data traversal algorithm research model has been developed for use in data transfer preparation tasks. The characteristics include the format for three-dimensional data (memory is only one dimension). However, the method expertly transforms it into a 3-D entity (cube string construction), which is essential for establishing a novel data traversal model that deviates from conventional wisdom and has multiple DRDs. In this paper, a method for decrypting, encrypting data that calls for multiple DRDs. The DRDs include the following: a string containing cube data is called a cube string. Delete List (a list that contains cube string moves), Character Position List (List of all character's initial positions in the cube string, including the plain string) because it requires multiple dependencies to crack or decode the data, these dependencies make it extremely difficult for hackers to predict the nature of data traversal.

A mobile agent is a software application that moves naturally among hosts in a uniform and non-uniform environment; it starts with one host and then moves onto the next in order to divide data between clients. The mobile paradigm is utilized in a wide assortment of medical care applications such as the medical information of a patient, the recovery of clinical information, the incorporation of information pertaining to their wellbeing, dynamic help, telemedicine, obtaining clinical data, patient administration, and so on. The accompanying security issues have grown in tandem with the complexity and improvements in mobile agent technologies. As mobile agents work in an insecure environment, their security is a top priority when communicating and exchanging data and information. Data integrity, data confidentiality and authentication, on-repudiation, denial of service, and access control, are all key security concerns with mobile agent migration. This paper proposes a Verifiable, Secure Mobile Agent Migration model, based on two polynomials (t, n), and an edge secret imparting plan with Blowfish encryption, to enable secure information transmission in clinical medical care.

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

The paper includes a researching model from an attacker simultaneously and repeatedly manages attacks. Under this, a model is proposed in which iterative learning system that allows the defender to identify optimal. When the increase number of attacks targeting information and knowledge stealing information and security component, it protects the data and denying right full access to information and evolved to develop numerous natural variety of protection to assist them to avoid and eliminate danger and adapt to surroundings for survival. An adaptive cyber defense system is employed here to boost our systems resilience by security levels and metrics with transparency and secret writing. Pseudo-code illustration converges to the foremost response with regard to the attacks once the wrongdoer diminishingly traverses the system.

Knowledge discovery is defined as the method used for discovering interesting, previously unknown and potentially useful patterns from a massive amount of data. It is an integrative area of research, including illustrative work from areas such as database technology, machine learning, and pattern recognition, extraction of valuable information, neural network, artificial intelligence, high-performance computing and data visualization. The process of finding knowledge from the data is also getting more important as the data is increasing every day. This paper discusses the process of knowledge discovery and also gives description about the challenges faced when knowledge is discovered. It also presents the work done in the related area and their comparative analysis.

For survival in smart cities, updating skillset and synchronization with latest technologies is a necessity. Such cities are required to work on advance practical turn of events and work on usefulness of metropolitan frameworks. Few frameworks came into existence and some cities will become future cities of intelligent network but security and safety issues are major issues to deal with. The paper gives an overview about smart sustainable city, defines the crime in an ordinary city and an analysis about expected crime in smart sustainable city is presented.

The main objective of the mission of smart cities in the country (India) is to provide its residents a good quality life to live, a clean and reliable environment, with applications of smart solutions for all. A smart city promotes the sustainability to the cities to provide adequate infrastructure. Smart people are required for smart cities mission, who can participate in reforms and governance, effectively. The involvement of citizen of the country is very much needed in the governance. The aim of this paper is to present a review on objectives, mission, selection process, features, vision and challenges of smart cities mission in the Nation.