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An improved version of Alpha-N, a self-powered, wheel-driven Automated Delivery Robot (ADR), is presented in this study. Alpha-N-V2 is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. For autonomous navigation and path planning, Alpha-N uses a vector map and calculates the shortest path by Grid Count Method (GCM) of Dijkstra’s Algorithm. The RFID Reading System (RRS) is assembled in Alpha-N to read Landmark determination with Radio Frequency Identification (RFID) tags. With the help of the RFID tags, Alpha-N verifies the path for identification between source and destination and calibrates the current position. Along with the RRS, GCM, to detect and avoid obstacles, an Object Detection Module (ODM) is constructed by Faster R-CNN with VGGNet-16 architecture that builds and supports the Path Planning System (PPS). In the testing phase, the following results are acquired from the Alpha-N: ODM exhibits an accuracy of [Formula: see text], RRS shows [Formula: see text] accuracy and the PPS maintains the accuracy of [Formula: see text]. This proposed version of Alpha-N shows significant improvement in terms of performance and usability compared with the previous version of Alpha-N.

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This paper introduces a rescue robot named Sigma-3 which is developed for potential applications such as helping hands for humans where a human can't reach to have an assessment of the hazardous environment. Also, these kinds of robot can be controlled remotely with an adequate control system. The proposed methodology forces on two issues -1. Novel mechanism design for measuring rotation, joints, links of Degree of Freedom (DOF) for an arm which is integrated with Sigma-3 2. Precise measuring of end-effector motion control over three dimensions. In the proposed mechanism design, the DOF measurement is presented by a planar and spatial mechanism where 4 types of rigid joints build up each DOF with controlling by six High Torque MG996R servo motors. Rotation and DOF measurement are consisting of different theoretical references of Rotation Matrix, Inverse Kinematics with experimental results. Presented methodology over Oscillation Damping performance exhibits less than 3% error while configuring for on hands testing. Another evaluation of operating time state strongly defends the mechanism of low power consumption ability.

Recommender Systems (RSs) have become an essential part of most e-commerce sites nowadays. Though there are several studies conducted on RSs, a hybrid recommender system for the real state search engine to find appropriate rental apartment taking users preferences into account is still due. To address this problem, a hybrid recommender system is proposed in this paper constructed by two of the most popular recommendation approaches — Collaborative Filtering (CF), Content-Based Recommender (CBR). CF-based methods use the ratings given to items by users as the sole source of information for learning to make a recommendation. However, these ratings are often very sparse in applications like a search engine, causing CF-based methods to degrade accuracy and performance. To reduce this sparsity problem in the CF method, the Cosine Similarity Score (CSS) between the user and predicted apartment, based on their Feature Vectors (FV) from the CBR module is utilized. Improved and optimized Singular Value Decomposition (SVD) with Bias-Matrix Factorization (MF) of the CF model and CSS with FV of CBR constructs this hybrid recommender. The proposed recommender was evaluated using the Statistical Cross-Validation consisting of Leave-One-Out Validation (LOOCV). Experimental results show that it significantly outperformed a benchmark random recommender in terms of precision and recall. In addition, a graphical analysis of the relationships between the accuracy and error minimization is presented to provide further evidence for the potentiality of this hybrid recommender system in this area.