Abstract

Internet of Things (IoT) is a fairly impending technologically. In recent years IoT technology spreads into all the areas which are, Agriculture sector, Industrial sector, Defense sector, Pharmacy sector, Science and Technology sector, and Irrigation sector etc. Due to its wide verity of applications, the human life becomes very much simpler than the previous. This IoT consists of many smart devices connected to each other with the help of the internet for communicate to each other for better results. With the help of IoT and other smart devices, online patient monitoring system and diagnosis are the innovative solutions for remote patients. Due to COVID-19 pandemic situations, online doctor, patient interactive system is very much essential. To further investigation of the disease or the health issues, initial test results measurements are safe. Consulting a doctor, diagnosing the disease, and Treatment a patient are very much possible through the proposed IOT Based Health Monitoring and Management System is very much useful for rural people. The proposed system is safe and the measured values are very much nearer to the physical patient test result.

Abstract

Travel blogs are a rich source of information about tourist spots. Millions of travellers use them to decide where to go and what to do. However, they are often devoid of factual information, which leads to a lack of trust and credibility surrounding blogs. On the other hand, Wikipedia is rich with accurate factual descriptions but is often bland and unfit for blogs. We propose a system to identify blog-worthy sentences on Wikipedia to augment travel blogs with factual information to make them more reliable and trustworthy. We curate a dataset of over 1.83 M Wikipedia sentences from 234,305 travel-related articles and assign a blog-worthiness score to each sentence by comparing them against 20 M sentences from ∼600 K

Abstract

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.

Abstract

Electricity markets are getting more dynamic and complex today. On the one hand, there are outside-in factors like new entrants and increasing competition, regulatory mandates, and volatility of the electricity prices. In contrast, on the other hand, there are inside-out factors like the adoption of renewable energies, storages, and EV, emphasis on the generation and retail portfolio optimization together with creating affinity services around the empowered consumers. The paper will start with the introduction of this dynamic environment across the globe with a specific focus on Australia, the UK, and US electricity markets. Introduction of 5 min settlement in Australian Electricity Market, the rise of ancillary service products in the UK, or new roles like Community Choice Aggregators (CCA) in the US are some of the

Abstract

Online active learning algorithms reduce human labeling costs by querying only a subset of informative incoming instances from the data stream to update the classification model. Active learning for online multiclass classification under complete information has been well addressed; however, it remains unaddressed for the bandit setting. In this paper, we investigate online active learning techniques under the bandit feedback setting. We proposed an efficient algorithm for learning a multiclass classifier with bandit feedbacks under the active learning setting. The proposed algorithms enjoy a regret bound of the order O(logT) in the active learning setting as well as in the standard (non-active) bandit feedbacks. We show the effectiveness of the proposed approach using extensive experiments on several benchmark

Abstract

Coherence is an important aspect of text quality and is crucial for ensuring its readability. It is essential for outputs from text generation systems like summarization, question answering, machine translation, question generation, table-to-text, etc. An automated coherence scoring model is also helpful in essay scoring or providing writing feedback. A large body of previous work has leveraged entity-based methods, syntactic patterns, discourse relations, and traditional deep learning architectures for text coherence assessment. However, these approaches do not consider factual information present in the documents. The transitions of facts associated with entities across sentences could help capture the essence of textual

Abstract

Answering questions in the context of videos can be helpful in video indexing, video retrieval systems, video summarization, learning management systems and surveillance video analysis. Although there exists a large body of work on visual question answering, work on video question answering (1) is limited to domains like movies, TV shows, gameplay, or human activity, and (2) is mostly based on common sense reasoning. In this paper, we explore a new frontier in video question answering: answering knowledge-based questions in the context of news videos. To this end, we curate a new dataset of ∼12K news videos spanning across ∼156 h with ∼1M multiple-choice question-answer pairs covering 8263 unique entities. We make the dataset publicly available (https://tinyurl.com/videoQAData). Using this dataset, we

Abstract

The COVID-19 pandemic has reformed the teaching-learning processes in engineering education across the globe. Virtual classrooms substituted physical classrooms with the widespread use of online meeting platforms. The proliferation of virtual classrooms not only paved the way for accelerated digital transformation but also brought back some elementary issues in engineering education. Many engineering students face difficulties in comprehending the fundamental concepts in their courses during virtual learning. As real-world engineering solutions depend on conceptual clarity, misconceptions of basic engineering principles need to be taken seriously. If not identified, analysed and corrected with constructive feedback, misconceptions on various engineering topics can create challenging obstacles in learning. Against this backdrop, this research study introduces a novel solution titled Classification of Students Misconceptions in Individualised Learning Environment (C-SMILE). The primary objective of the C-SMILE system is to examine the usefulness of personalised automated feedback to students to enhance their conceptual understanding by pinpointing their misconceptions. Besides, we propose a method by which students’ misconceptions can be effectively classified for every instructional objective in every engineering course using machine learning techniques. Our pilot-study results show that the proposed C-SMILE system can precisely classify students’ misconceptions in engineering education settings.

Abstract

Objectives: Abnormal vaginal discharge (Sayalan al-Rahim) is a common public health problem that significantly disrupts the health-related quality of life (HRQoL). Syndromic management infers the concurrent treatment of two or more infections. Hence, a comparative, single-blind study was planned to determine the efficacy of Acacia (Acacia nilotica Linn.) pod's sitz bath (Abzan) plus vaginal pessary (Farzaja) vs. placebo in abnormal vaginal discharge syndromic management, its associated symptoms, and women's HRQoL

Abstract

Elliptic Curve Cryptography (ECC)-based authenti-cation schemes for the Radio-frequency identification (RFID) environment have emerged as a very safe, secure, and robust candidate of mutual authentication. However, the limited resource availability in a passive tag makes such schemes difficult to realize in practice. Public/private key pair generation, storage, and the computation using those keys are the major bottlenecks towards the faster and efficient design of such schemes. Therefore, we have designed our scheme without using public/private key pairs. We only utilize the Elliptic Curve Discrete Logarithm Problem (ECDLP) property for the realization of our key-less scheme on the secure elliptic curves. Our scheme is primarily aimed at efficient implementation of authentication schemes in Warehouse Management System (WMS), where the data is stored in local servers. This new idea helps to save the memory space in the tags and the server. Moreover, the computation cost also reduces to a great extent in comparison to other schemes. We have compared our scheme with several other schemes and found it efficient in terms of storage cost as well. The simulation result of our scheme with the popular aut