The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

There has been debates related to online and blended learning from a perspective of learner experiences in terms of student satisfaction, engagement and performances. In this paper, we analyze student feedback and report the findings of a study of the relationships between student satisfaction and their engagement in an online course with their overall performances. The module was offered online to 844 university students in the first year across different disciplines, namely Engineering, Science, Humanities, Management and Agriculture. It was assessed mainly through continuous assessments and was designed using a learning-by-doing pedagogical approach. The focus was on the acquisition of new skills and competencies, and their application in authentic mini projects throughout the module. Student feedback was coded and analyzed for 665 students both from a quantitative and qualitative perspective. The association between satisfaction and engagement was significant and positively correlated. Furthermore, there was a weak but positive significant correlation between satisfaction and engagement with their overall performances. Students were generally satisfied with the learning design philosophy, irrespective of their performance levels. Students, however, reported issues related to lack of tutor support and experiencing technical difficulties across groups. The findings raise implications for institutional e-learning policy making to improve student experiences. The factors that are important relate to the object of such policies, learning design models, student support and counseling, and learning analytics.

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Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy

With development in the area of electronics and artificial intelligence (AI), medical devices (MD) have been sophisticated as well. MD management strategies today are very different than decades ago, so it is reasonable to consider how we can prepare for where we are going in the future. This paper presents the result of application of machine learning (ML) techniques in management of infant incubators in healthcare institutions. A total of 140 samples was used for development of Expert system based on ML classifiers. These samples were collected during 2015–2017 period, as part of yearly inspections of incubators in healthcare institutions by ISO 17020 accredited laboratory. Dataset division 80–20 was used for classifiers development and validation. Performance of the following machine learning algorithms was investigated: Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbour (kNN), and Support Vector Machine (SVM). Resulting classifiers were compared by performance and classifier based on Decision Tree algorithm yielded highest accuracy (98.5%) among other tested systems. Obtained results suggest that by introducing ML algorithms in MD management strategies benefit healthcare institution firstly in terms of increase of safety and quality of patient diagnosis and treatments, but also in cost optimization and resource management.

Prediction of medical device performance using machine learning techniques: infant incubator case study

Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance

In today’s world, machine learning has gained much popularity, and its algorithms are employed in every field such as pattern recognition, object detection, text interpretation and different research areas. Machine learning, a part of AI (artificial intelligence), is used in the designing of algorithms based on the recent trends of data. This paper aims at introducing the algorithms of machine learning, its principles and highlighting the advantages and disadvantages in this field. It also focuses on the advancements that have been carried out so that the current researchers can be benefitted out of it. Based on artificial intelligence, many techniques have been developed such as perceptron-based techniques and logic-based techniques and also in statistics, instance-based techniques and Bayesian networks. So, overall this paper produces the work done by the authors in the area of machine learning and its applications and to draw attention towards the scholars who are working in this field.

Machine Learning: A Review of the Algorithms and Its Applications

Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that’s we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is used and it recognizes friends' photos, that's also machine learning. Spam filters in email saves the user from having to wade through tons of spam email, that's also a learning algorithm. In this paper, a brief review and future prospect of the vast applications of machine learning has been made.

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Applications of Artificial Intelligence in Machine Learning: Review and Prospect

In recent decades, artificial intelligence (AI) has found numerous applications in the field of medicine. In this paper, the key idea is that doctors are not available at emergency or doctors may do some mistakes by coincidence. To overcome or eliminate these problems, here we consider some diseases and design a prototype. In this work, we investigate different types of diseases according to various infectious agents (bacteria, virus, and parasite) and the respective diseases caused by them. Afterward, we make a formulation so that it deals with history of the disease and prescribing the proper medications according to the disease detected.

AI Doctor: An Intelligent Approach for Medical Diagnosis

The article aims to review, analyze, design and implement a philosophy of medical diagnosis by artificial intelligence (AI) and soft computing techniques. The theme of the paper is that there are abundant corruptions in therapeutic judgment; in its place of appropriate diagnosis, majority practitioners go behind the narrow path by trapping the people at a serious phase. The ordinary community suffers deficient in diagnosis for higher investigative costs as well as the lack of certified practitioners. This article proposes some AI techniques to eradicate this lacuna by designing a prototype. The proposed prototype here termed as machine intelligent diagnostic system (MIDs), which has the capability of learning, thinking, reasoning and managing uncertainty as a real-world doctor. The model structured according to AI techniques considers the different cases of the disease to implement it. This article analyzes the shortcoming of MIDs, which can perform as a doctor to serve society as regular fashion as well as at the time of crisis as a crisis-manager. The degrees of the acuteness of patients’ symptoms are perceived by a membership function, which is used to tackle the emotion of the patients, and a fuzzy logic membership function is being used to calculate probabilities of diseases. Finally, this work finds smart results of MIDs, which can serve as doctors to some extent to compensate for the crisis of doctor in the universe.

Machine intelligent diagnostic system (MIDs): an instance of medical diagnosis of tuberculosis

The Bluetooth arrangement provides a very robust and powerful technology within a short range wireless communication. The communication over Bluetooth is complicated with a heavy security threat. Here, in this paper we proposed for a system, Bluetooth Enabled surveillance System (BESS) that will help transmitting live video streaming in between two mobile phone via built-in cell phone camera. This system can be useful for surveillance of a secret area or monitoring of any important work. This system can help to prevent certain attack within a small range.

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Real-Time Video Streaming Over Bluetooth Network Between Two Mobile Nodes

Presently, diagnosis of disease is an important issue in the field of health care using Artificial Intelligence (AI). Doctors are not always present and sometimes although doctors are available but people are not able to afford them due to financial issues. The basic information like blood pressure, ages, etc. are known at that moment without knowing any symptoms how the disease can be predicted. If people know the symptoms of how the disease can be predicted? Both of these aspects, we would look into, propose algorithms, and implement them for the welfare of the society. The proposed algorithms are capable of classifying diseases of people and healthy people in efficient manner. In this work, the authors also link the concepts of probability with fuzzy logic and describe how to interpret them. Then, we can consider human being as a kind of machine and we know that any machine can be described by a parameter called reliability but the definition of classical reliability if used in case of human being fails miserably. The aim of this paper is to make a bridge among fuzzy logic, probability, and reliability.

Sumit Das

Papers

Applications of Artificial Intelligence in Machine Learning: Review and Prospect

AI Doctor: An Intelligent Approach for Medical Diagnosis

Machine intelligent diagnostic system (MIDs): an instance of medical diagnosis of tuberculosis

Real-Time Video Streaming Over Bluetooth Network Between Two Mobile Nodes

Artificial Intelligent Reliable Doctor (AIRDr.): Prospect of Disease Prediction Using Reliability