As the massive data is increasing exponentially on web and information retrieval systems and the data retrieval has now become challenging. Stemming is used to produce meaningful terms by stemming characters which finally result in accurate and most relevant results. The core purpose of stemming algorithm is to get useful terms and to reduce grammatical forms in morphological structure of some language. This paper describes the different types of stemming algorithms which work differently in different types of corpus and explains the comparative study of stemming algorithms on the basis of stem production, efficiency and effectiveness in information retrieval systems.

This paper investigated the effects of variant lighting conditions on the recognition process. A framework is proposed to improve the performance of gesture recognition under variant illumination using the luminosity method. To prove the concept, a workable testbed has been developed in the laboratory by using a Microsoft Kinect sensor to capture the depth images for the purpose of acquiring diverse resolution data. For this, a case study was formulated to achieve an improved accuracy rate in gesture recognition under diverse illuminated conditions. For data preparation, American Sign Language (ASL) was used to create a dataset of all twenty-six signs, evaluated in real-time under diverse lighting conditions. The proposed method uses a set of symmetric patterns as a feature set in order to identify human hands and recognize gestures extracted through hand perimeter feature-extraction methods. A Scale-Invariant Feature Transform (SIFT) is used in the identification of significant key points of ASL-based images with their relevant features. Finally, an Artificial Neural Network (ANN) trained on symmetric patterns under different lighting environments was used to classify hand gestures utilizing selected features for validation. The experimental results showed that the proposed system performed well in diverse lighting effects with multiple pixel sizes. A total aggregate 97.3% recognition accuracy rate is achieved across 26 alphabet datasets with only a 2.7% error rate, which shows the overall efficiency of the ANN architecture in terms of processing time.

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work.

Human pose recognition is a new field of study that promises to have widespread practical applications. While there have been efforts to improve human position estimation with radio frequency identification (RFID), no major research has addressed the problem of predicting full-body poses. Therefore, a system that can determine the human pose by analyzing the entire human body, from the head to the toes, is required. This paper presents a 3D human pose recognition framework based on ANN for learning error estimation. A workable laboratory-based multisensory testbed has been developed to verify the concept and validation of results. A case study was discussed to determine the conditions under which an acceptable estimation rate can be achieved in pose analysis. Using the Butterworth filtering technique, environmental factors are de-noised to reduce the system’s computational cost. The acquired signal is then segmented using an adaptive moving average technique to determine the beginning and ending points of an activity, and significant features are extracted to estimate the activity of each human pose. Experiments demonstrate that RFID transceiver-based solutions can be used effectively to estimate a person’s pose in real time using the proposed method.