Blind navigation is an accessibility application that enables blind to use an android Smartphone in an easy way for indoor navigation with instructions in audio form. We have proposed a prototype which is an indoor navigation application for blinds that uses QR codes. It is developed for android Smart phones and does not require any additional hardware for navigation. It provides automatic navigational assistance on pre-defined paths for blind. QR codes are placed on the floor sections after specific distance that acts as an input for current location detection and navigation. Whenever a QR code is scanned it provides the user with the information of the current location and asks the user to select the destination and then offers optimal and shortest path using path finding algorithms. During navigation whenever the deviation from the proposed path is detected it prompts the user and guides back to the right path by comparing the current path with the generated path. All of the instructions throughout the application are provided in audio form to the user. The interface of the application is well built for blinds which makes the smart phones user-friendly and useable for blind people. The user interacts with the application through a specific set of user-friendly gestures for specific inputs and operations. At the end, we have performed comparison between different state of art approaches and concluded that our approach is more user friendly, cost effective and produced more accurate results.

Blockchain has emerged as a groundbreaking security technology, playing a vital role in various industries such as banking, the Internet of Things (IoT), healthcare, education, and voting. However, the widespread adoption of this technology has introduced certain vulnerabilities, particularly in the form of exploitation by malicious entities. While existing research primarily focuses on identifying anomalous actor behavior, there has been limited exploration of precisely identifying hostile actors within the Ethereum network. This study aims to uncover malevolent actors operating on the Ethereum network and categorize attacks based on their actions. To achieve this research goal, a new dataset was constructed by consolidating data on malicious actors involved in illicit Ethereum activities. Key features were extracted from this dataset using advanced feature selection techniques, including Principal Component Analysis (PCA), Information Gain, and Ridge Regression. Machine learning classifiers such as LGBM, XGBoost, Random Forest, Extra Tree, Bagging, and K-Nearest Neighbors were applied to identify and classify malicious actors effectively. The results, achieving an impressive accuracy rate of 98%, underscore the effectiveness of Information Gain when coupled with LGBM and XGBoost. Notably, XGBoost demonstrates efficiency by completing the analysis in a mere 13.72 seconds. In addition to identifying fraudulent activities, this research classifies them into distinct categories, enhancing blockchain security and addressing trust concerns. This study’s outcomes fortify the Ethereum network’s resilience and contribute to the broader discourse on bolstering reliability in blockchain systems.

This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources. We are particularly interested in a specific case where both the client model architectures and data distributions are diverse, which makes it nontrivial to adopt conventional approaches such as Federated Learning and network co-distillation. To this end, we propose a new decentralized learning method called Decentralized Learning via Adaptive Distillation (DLAD). Given a collection of client models and a large number of unlabeled distillation samples, the proposed DLAD 1) aggregates the outputs of the client models while adaptively emphasizing those with higher confidence in given distillation samples and 2) trains the global model to imitate the aggregated outputs. Our extensive experimental evaluation on multiple public datasets (MNIST, CIFAR-10, and CINIC-10) demonstrates the effectiveness of the proposed method.

University course timetabling problem (UCTP) is important issue forever. Scheduling problems are considered and proved NP-Complete by different researchers. It is due to continuous changing of constraints to be fulfilled on rapidly changing data. Different hybrid state of art techniques and their use for university course timetable problem is investigated in this study. This paper also analyze occurrence of constraints and there ratio of similarity in recent research trend on university course timetabling problem. Constraints highly vary from department to department. This level of variance of constraints makes timetabling problem difficult to solve and NP-complete. In recent years, concept of hybridization of different methods increased. This study analyzed use of hybrid heuristic and meta-heuristic methods for university course timetabling problem. In this study, we categorize this hybridization into two main categories: local search hybridization with local search based approaches and population based hybridization with local search based approaches. It is observed that Population based methods (Genetic Algorithm (GA), Particle Swarm optimization (PSO) and Artificial Bee colony (ABC) etc.) are preferred in combination with local search (LS) based methods for university timetabling problem. Concept of hybridization of population based methods with local and other population based methods is adopted to eliminate demerits of both methods. Although, hybrid methods are difficult and also require more computational cost. Still hybridization is useful for finding better solutions.

New technologies are being adapted to made progress in healthcare especially for independent livings. Medication at distance is leading to integrate technologies with medical. Machine learning methods in collaboration with wearable sensor network technology are used to find hidden patterns in data, detect patient movements, observe habits of patient, analyze clinical data of patient, find intention of patients and make decision on the bases of gathered data. This research performs comparative study on non-clinical systems in healthcare for independent livings. In this study, these systems are sub-divided w.r.t their working into two types: single purpose systems and multi-purpose systems. Systems that are built for single specific purpose (e.g. detect fall, detect emergent state of chronic disease patient) and cannot support healthcare generically are known as single purpose systems, where multi-purpose systems are built to serve for multiple problems (e.g. heart attack, fall detection etc.) by using single system. This study analyzes usages of machine learning techniques in healthcare systems for independent livings. Answer Set Programming (ASP), Artificial Neural Networks, Classification, Sampling and Rule Based Reasoning etc. are some state of art techniques used to determine emergent situations and observe changes in patient data. Among all methods, ASP logic is used most widely, it is due to its feature to deal with incomplete data. It is also observed that system using ANN shows better accuracy than other systems. It is observed that most of the systems created are for single purpose. In this work, 10 single purpose systems and 5 multi-purpose systems are studied. There is need to create more generic systems that can be used for patients with multiple diseases. Furthermore, most of the systems created are prototypical. There is need to create systems that can serve healthcare services in real world. Some systems are hard to be used in real life due to large hardware requirements. Although, good systems are created but still there is need to build more efficient, affordable, adoptive and generic systems.

Because of the evolution of artificial intelligence (AI), home security has progressed from a basic security system to an active architecture that is responsive and adaptive to real world situations. Due to the rapid adoption of AI in smart systems, there is increasing suspicion surrounding privacy issues and ethical ambiguity, as well as gaps when it comes to regulating these technologies. We provide an overview of AI in smart home security applications and examine the area of security, access control, intrusion detection, human action recognition, and research on intelligent automation. We summarize the last decade of evolution, with some summaries of previous on computer vision, authentication systems, and finding unusual patterns recently. Our key findings include the development of approaches to improve real time security monitoring, dramatic reductions in false alarms, and customization of home access using AI. Improvements in security have also increased risk with respect to ethical ambiguity as well as technical issues in certain cases. In this paper, we offers pathways for improved AI system design, proposed formal data protection regulations, and examples of simplifying complex system for user comprehension, which also establishes the groundwork for future efforts. Home security should balance new opportunities with ethical considerations.