With the widespread deployment of large language models (LLMs) such as GPT4, BART, and LLaMA, the need for a system that can intelligently select the most suitable model for specific tasks while balancing cost, latency, accuracy, and ethical considerations has become increasingly important. Recognizing that not all tasks necessitate models with over 100 billion parameters, we introduce OptiRoute, an advanced model routing engine designed to dynamically select and route tasks to the optimal LLM based on detailed user-defined requirements. OptiRoute captures both functional (e.g., accuracy, speed, cost) and non-functional (e.g., helpfulness, harmlessness, honesty) criteria, leveraging lightweight task analysis and complexity estimation to efficiently match tasks with the best-fit models from a diverse array of LLMs. By employing a hybrid approach combining k-nearest neighbors (kNN) search and hierarchical filtering, OptiRoute optimizes for user priorities while minimizing computational overhead. This makes it ideal for real-time applications in cloud-based ML platforms, personalized AI services, and regulated industries.

The e-commerce systems are being tackled from commerce behavior and internet technologies. Therefore, trust aspect between buyer-seller transactions is a potential element which needs to be addressed in competitive e-commerce industry. The e-commerce industry is currently handling two different trust approaches. First approach consists on centralized mechanism where digital credentials/set of rules assembled, called Policy based trust mechanisms . Second approach consists on decentralized trust mechanisms where reputation, points assembled and shared, called Reputation based trust mechanisms. The difference between reputation and policy based trust mechanism will be analyzed and recommendations would be proposed to increase trust between buyer and seller in e-commerce industry. The integration of trust mechanism is proposed through mapping process, strength of one mechanism with the weakness of other. The proposed model for integrated mechanism will be presented and illustrated how the proposed model will be used in real world e-commerce industry.

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.

Any company's human resources department faces the challenge of predicting whether an applicant will search for a new job or stay with the company. In this paper, we discuss how machine learning (ML) is used to predict who will move to a new job. First, the data is pre-processed into a suitable format for ML models. To deal with categorical features, data encoding is applied and several MLA (ML Algorithms) are performed including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and eXtreme Gradient Boosting (XGBoost). To improve the performance of ML models, the synthetic minority oversampling technique (SMOTE) is used to retain them. Models are assessed using decision support metrics such as precision, recall, F1-Score, and accuracy.

Create, modify, and test code, forms, and scripts that allow computer applications to run. Work from specifications drawn up by software developers. Assist software developers by analyzing user needs and designing software solutions. Develop and write computer programs to store, locate, and retrieve specific documents, data, and information. Perform Hibernate optimization using projection in criteria object to query minimum columns to display data on dashboards.

If you have a passion for computers and coding, perhaps you've wondered how to become a software engineer. The BLS reports that software developers, quality assurance analysts, and testers positions will see 22% employment growth from 2020-30. Even more good news: This high-responsibility role is more attainable than you may think. A software engineer designs, builds, and maintains computer applications. They may oversee a team of software developers writing code.

Traditionally Genetic Algorithm has been used for optimization of unimodal and multimodal functions. Earlier researchers worked with constant probabilities of GA control operators like crossover, mutation etc. for tuning the optimization in specific domains. Recent advancements in this field witnessed adaptive approach in probability determination. In Adaptive mutation primarily poor individuals are utilized to explore state space, so mutation probability is usually generated proportionally to the difference between fitness of best chromosome and itself (fMAX - f). However, this approach is susceptible to nature of fitness distribution during optimization. This paper presents an alternate approach of mutation probability generation using chromosome rank to avoid any susceptibility to fitness distribution. Experiments are done to compare results of simple genetic algorithm (SGA) with constant mutation probability and adaptive approaches within a limited resource constraint for unimodal, multimodal functions and Travelling Salesman Problem (TSP). Measurements are done for average best fitness, number of generations evolved and percentage of global optimum achievements out of several trials. The results demonstrate that the rank-based adaptive mutation approach is superior to fitness-based adaptive approach as well as SGA in a multimodal problem space.

The need for analysis of toxicity in new drug candidates and the requirement of doing it fast have asked the consideration of scientists towards the use of artificial intelligence tools to examine toxicity levels and to develop models to a degree where they can be used commercially to measure toxicity levels efficiently in upcoming drugs. Artificial Intelligence based models can be used to predict the toxic nature of a chemical using Quantitative Structure Activity Relationship techniques. Convolutional Neural Network models have demonstrated great outcomes in predicting the qualitative analysis of chemicals in order to determine the toxicity. This paper goes for the study of Simplified Molecular Input Line-Entry System (SMILES) as a parameter to develop Long short term memory (LSTM) based models in order to examine the toxicity of a molecule and the degree to which the need can be fulfilled for practical use alongside its future outlooks for the purpose of real world applications.

Conversational interfaces are powered primarily by natural language processing (NLP), and a key subset of NLP is natural language understanding (NLU). While NLU is a subset of NLP, NLP doesn't always involve NLU. The terms NLP and NLU are often used interchangeably, but they have slightly different meanings. Developers need to understand the difference between natural language processing and natural language understanding so they can build successful conversational applications. Natural language processing is a subset of AI, and it involves programming computers to process massive volumes of language data.