Dr Gopal Jamnal

In this paper, we proposed a new type of decision making system to achieve the intelligent goal for automated smart environments. The artificial intelligence techniques, used as building blocks to understand inhabitant activity patterns. The collected information fused to a central inference engine based on Hidden Markov model and Fuzzy rules for taking appropriate actions to communicate and control various home appliances. We proposed a novel CASH (cognitive automated smart home) architecture, based on the Hidden Markov Model and the fuzzy rule based system. The Hidden Markov Model and fuzzy rules are well equipped to address the spatio-temporal activity pattern recognition problem and to trigger appropriate task execution rules.

In today’s world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions. Cognitive IoE is a new state-of-art computing paradigm for interconnecting and controlling network objects in context-aware perception-action cycle for our cognitive needs. The interconnected objects (sensors, RFID, network objects etc.) behave as agents to learn, think and adapt situations according to dynamic contextual environment with no or minimum human intervention. One most important recent research problem is “how to recognize inhabitant activity patterns from the observed sensors data”. In this paper, we proposed a two level classification model named as ACM (Ambient Cognition Model) for inhabitant’s activities pattern recognition, using Hidden Markov Model based probabilistic model and sub tractive clustering classification method. While subtractive clustering separates similar activity states from non-similar activity state, a HMM works as the top layer to train systems for temporal-sequential activities to learn and predict inhabitant activity pattern proactively. The proposed ACM framework play, a significant role to identify user activity intention in more proactive manner such as routine, location, social activity intentions in smart home scenario. The experimental results have been performed on Matlab simulation to evaluate the efficiency and accuracy of proposed ACM model.

This paper addresses the interpretability and transparency behind the random forest model predictions. Random forest is an ensemble of bootstrapped independent decision trees that are trained on subsets of input data to make predictions. Although random forest is a robust model that can overcome bias, its inherent complexity, and poor interpretability can make it challenging to apply in many application domains that require transparency and explainability in the model's predictions. This lack of transparency in the decision-making process can prevent users from analyzing what makes the model arrive at a specific prediction. The paper presents a visual analytic application to overcome transparency challenges by providing a clear structure of individual decision trees and hierarchical relationships between features. This allows users to analyze latent information and instils trust in the random forest model. Additionally, statistical analysis of feature ranking agreement and prediction popularity reduces mental burden of the user. The paper includes two case studies to evaluate model's uncertainty, bias, and variances in predictions with explainability at local and global scales of decision paths. The visual analytics application provides a coordinated multiple-view system to instil trust in random forest models.

In today’s world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users’ instructions (Wanglei, 2015). The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher’s work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.

In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions (Wanglei, 2015). The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher's work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.

© 2014 The Science and Information (SAI) Organization. Software Development with reuse and for reuse is the foundation of CBSE (Component based software engineering) which allow faster development at lower cost and better usability. A reusable software component works as a plug and play device, which abstract the software complexity and increase performance. Software reuse guidelines have been addressing the issue of capturing best practices, for a long while software industry has collected the enormous wealth of knowledge, experience, domain expertise, design principals & heuristics, hypothesis, algorithms, and experimental results. However, there is no rock solid and mature software component development guidelines defined for the current technologies such as.NET. This paper presents reuse guidelines based framework (known as.NET Reuse Guider) for guidelines based component development for reuse in.NET family. We have demonstrated our approach by designing a binary component as part of development for reuse based on our own.NET Reuse Guider framework. This paper also provides a number reuse analysis and metrics and a prototype component guider tool which sits on top of the.NET architecture with built-in software development & reuse knowledge.