<abstract>
<title style='display:none'>Abstract</title>
This project is concerned to analyze and increase the efficiency of patrol inspection and thus reduces time consumption for inspection. Inspection is the process of checking whether the specification of the product meet the standard specification provided by the customer. Inspector takes more time for checking the parameter that meet the specification at all time than Checking the parameters that does not satisfy the customer specification which leads to rejection .Thus the inspector should inspect the parameter that increase the rejection or rework with more attention .on the whole this concept helps the inspector to improve the quality of the product by inspecting more components according to the frequency of the component.
</abstract>

Patrol Inspection and Time Study to Improve Quality

<abstract><title style='display:none'>Abstract</title>Nanorobotics is the technology of creating machines or robots of the size of few hundred nanometres and below consisting of components of nanoscale or molecular size. There is an all around development in nanotechnology towards realization of nanorobots in the last two decades. In the present work, the compilation of advancement in nanotechnology in context to nanorobots is done. The challenges and issues in movement of a nanorobot and innovations present in nature to overcome the difficulties in moving at nano-size regimes are discussed. The efficiency aspect in context to artificial nanorobot is also presented.</abstract>

Nanorobot Movement: Challenges and Biologically inspired solutions

<abstract>
<title style='display:none'>Abstract</title>
Pneumatic actuators offer several advantages over electromechanical and hydraulic actuators for positioning applications. Nonetheless, pneumatic actuators are subject to high friction forces, dead band and dead time, which make fast and accurate position control difficult to achieve. This research paper presents the process of controller identification, design, modeling and control for pneumatic actuator system. System Identification approach is used with the purpose to estimate the mathematical model of pneumatic actuator system and for controller design. Data collection of input and output signal of the system has been performed from experiment procedure. This data is used for estimate the model by selecting Auto-Regressive Exogenous (ARX) model as a model structure. The accepted model is based validation test namely as residual correlation, Akaike Final Prediction Error and best fit percentage. Different control schemes such as PID and LQR (Linear Quadratic Regulator) have been applied for controller design. PID controllers with Ziegler Nichols tuning are enabled to provide good performance in various systems. The effects of Tustin transformation, zero order hold and discrete model are tested in PID controller designed. The methodology for this paper combines off-line model based on analysis with on-line iteration. Different external loads are added in order to investigate the effectiveness to the designed controllers in real time system. The tracking performance of the closed loop system is satisfied which offers considerable robustness even on a slight increase in load. The results obtained in the experiment are successful to prove that the output signals which with the controller are almost the same for both simulation and experimental modes.
</abstract>

Modeling and Controller Design of Pneumatic Actuator System with Control Valve

<abstract>
<title style='display:none'>Abstract</title>
A forest has different types of vegetation like herbs, trees, shrubs and different species of animals. In one way or other, these renewable resources are very essential to mankind. Forest fires are the most common hazards in forests which lead to serious destruction of forest wealth, bio-diversity and natural habitat. Early detection and preventive measures are necessary to protect forests from fires. In order to achieve early detection, there are two most used traditional methods of human surveillance. One is directly through human observation and the other is through distant video surveillance. Doing the observation through distant mode, one can achieve surveillance through automation approach of detection. Automated fire alert detection system proposed in this paper comprises of two sensors, namely smoke and fire. These sensors detect change in a measurable physical quantity and help in the early detection of a forest fire. A key feature of this fire detection system is to alert the user remotely by using a GSM module, whenever a fire is detected.
</abstract>

Forest fire detection using wireless sensor networks

<abstract>
<title style='display:none'>Abstract</title>
The scale of dairy farming worldwide has changed significantly over recent years, with a move towards larger, more intensive, profit-driven enterprises, primarily due to market pressures. This change has resulted in demand for technologies that can reduce costs and labour inputs while increasing farm productivity. This is mainly achieved through the use of farm automation and advanced technological techniques.
An important aspect of farm automation that is currently being researched is the area of automated animal health monitoring. In this research, we have identified specific diseases which are common in dairy animals which can be identified through the use of non-invasive, low-cost, sensor technology. These diseases have been mapped to specific aspects of animal behaviour that have been mapped to the three sensors which are most significant to identify these diseases. The identified sensors will be shown to be vital in the development of the next generation of health monitoring system for dairy animals. Such a system will allow the automatic identification of animal health events, greatly increasing overall herd health and yield while reducing animal health inspection and long-term animal healthcare costs.
</abstract>

Sensor Technology For Animal Health Monitoring

<abstract><title style='display:none'>Abstract</title>Electro-hydraulic servo actuator (EHA) system consists of several dynamic parts which are widely used in motion control application. These dynamic parts need to be controlled to determine direction of the motion. In this research paper, system identification technique is used for system modeling and the model of the system is estimated by using parameter estimation technique. This process started with collection of input and output data from experimental procedure. The data collected is used for model estimation and Auto Regressive with eXogeneous input (ARX) model is chosen as model structure of the system. Based on the input and output data of the system, best fit criterion and correlation analysis of the residual is analyzed to determine the adequate model to represent the EHA system. Once the model is obtained, discrete PID and feed forward plus LinearQuadratic Regulator (LQR) controller is developed to improve the performance and position tracking performance of EHA system. In order to verify these controllers, it is applied to the real time system and the performance of the system is monitored. The result obtained shows that the output of the system in simulation mode and experimental works is almost similar for both controllers. The output of the system also tracked the input given successfully. Finally, by comparing the best tuning output from these two different controllers, feed forward plus LQR controller proved to give a better output performance than the classical discrete PID controller by minimize the phase lag and reduce disturbance effect in the system.</abstract>

Feed Forward Linear Quadratic Controller Design For An Industrial Electro Hydraulic Actuator System With Servo Valve

<abstract>
<title style='display:none'>Abstract</title>
The rapid growth of natural language processing (NLP) applications, such as text summarization, speech recognition, information extraction, and machine translation, has led to the development of structured query language (SQL) for extracting information from structured data. However, due to limited resources, converting Natural Language (NL) queries to SQL in Bengali is challenging. This article proposes an unsupervised machine learning model to find semantically Bengali closed words that can generate SQL from NL queries in Bengali. The main objective of the proposed system is to provide support in the creation of patient-oriented explanations and educational resources by simplifying intricate medical terminology. The major findings of the proposed system are as follows: The use of machine translation in the field of medicine facilitates the dissemination of healthcare information to a diverse international audience and improves the performance of entity recognition tasks, including the identification of medical conditions, drugs, or procedures within clinical notes or electronic health data. This system allows a naive user to extract health-related information from a healthcare-structured database without any knowledge of SQL. The system accepts a query and generates a response according to the query in Bengali language. Query tokenization and stop word removal are carried out in the preprocessing stage, and unsupervised machine learning techniques are implemented to process the input query sentence. Tokenized words are converted into vectors using the skip-gram model, with noise-contrastive estimation (NCE) applied to discriminate between actual and irrelevant words. Stochastic gradient descent (SGD) optimizes the model by randomly choosing a small amount of data from the dataset and using cosine similarity to measure closer words. The semantically closer words are found using an unsupervised learning method to generate the SQL.
</abstract>

Medical nearest-word embedding technique implemented using an unsupervised machine learning approach for Bengali language

<abstract>
<title style='display:none'>Abstract</title>
Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.
</abstract>

University at Albany (UAlbany), State University of New York (SUNY)

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

<abstract>
<title style='display:none'>Abstract</title>
The evolving cyber-attack landscape demands flexible and precise protection for information and networks. Network anomaly detection (NAD) systems play a crucial role in preventing and detecting abnormal activities on the network that may lead to catastrophic outcomes when undetected. This paper aims to provide a comprehensive analysis of NAD using unsupervised learning (UL) methods to evaluate the effectiveness of such systems. The paper presents a detailed overview of several UL techniques, lists the current developments and innovations in UL techniques for network anomaly and intrusion detection, and evaluates 13 unsupervised anomaly detection algorithms empirically on benchmark datasets such as NSL-KDD, UNSW-NB15, and CIC-IDS 2017 to analyze the performance of different classes of UL approaches for NAD systems. This study demonstrates the effectiveness of NAD algorithms, discusses UL approaches' research challenges, and unearths the potential drawbacks in the current network security environment.
</abstract>

A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection

<abstract>
<title style='display:none'>Abstract</title>
This paper proposes and evaluates a novel control approach for trajectory tracking, stability enhancement, and vibration reduction of a flexible joint robot (FJR). The FJR is a 2-degree-of-freedom underactuated nonlinear system that is challenging to control due to vibration, underactuation, uncertainties, and external disturbances. The control objectives are high trajectory tracking performance together with suppressing vibration. The proposed control approach uses integral sliding mode control (ISMC) combined with a barrier function based on back-stepping. This ensures robust and smooth performance by eliminating the reaching phase where sliding mode control (SMC) is typically not robust. Robustness is thus guaranteed from the start. The FJR is modeled as a 4th-order system using Lagrangian mechanics and decomposed into two 2nd-order subsystems for control design. Sliding variables are defined for each subsystem. Using these variables, the proposed control achieves robust trajectory tracking, stability, and vibration reduction. Numerical simulations in MATLAB validate the superior performance of the proposed ISMC-barrier function control compared to conventional ISMC for the FJR. This novel control approach addresses the challenges of controlling this FJR.
</abstract>

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

<abstract>
<title style='display:none'>Abstract</title>
Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It is acquired by the progressive demise of the dopaminergic neurons existing in the substantia nigra pars compacta region of the human brain. In the absence of a single accurate test, and due to the dependency on the doctors, intensive research is being carried out to automate the early disease detection and predict disease severity also. In this study, a detailed review of various artificial intelligence (AI) models applied to different datasets across different modalities has been presented. The emotional intelligence (EI) modality, which can be used for the early detection and can help in maintaining a comfortable lifestyle, has been identified. EI is a predominant, emerging technology that can be used to detect PsD at the initial stages and to enhance the socialization of the PsD patients and their attendants. Challenges and possibilities that can assist in bridging the differences between the fast-growing technologies meant to detect PsD and the actual implementation of the automated PsD detection model are presented in this research. This review highlights the prominence of using the support vector machine (SVM) classifier in achieving an accuracy of about 99% in many modalities such as magnetic resonance imaging (MRI), speech, and electroencephalogram (EEG). A 100% accuracy is achieved in the EEG and handwriting modality using convolutional neural network (CNN) and optimized crow search algorithm (OCSA), respectively. Also, an accuracy of 95% is achieved in PsD progression detection using Bagged Tree, artificial neural network (ANN), and SVM. The maximum accuracy of 99% is attained using K-nearest Neighbors (KNN) and Naïve Bayes classifiers on EEG signals using EI. The most widely used dataset is identified as the Parkinson's Progression Markers Initiative (PPMI) database.
</abstract>

Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges

<abstract>
<title style='display:none'>Abstract</title>
In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.
</abstract>

Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

<abstract><title style='display:none'>Abstract</title>The intention of this paper is to provide an overview on the subject of Human-Computer Interaction. The overview includes the basic definitions and terminology, a survey of existing technologies and recent advances in the field, common architectures used in the design of HCI systems which includes unimodal and multimodal configurations, and finally the applications of HCI. This paper also offers a comprehensive number references for each concept, method, and application in the HCI.</abstract>

Human-Computer Interaction: Overview on State of the Art

<abstract>
<title style='display:none'>Abstract</title>
The development of dynamic rehabilitation devices can be evaluated as a research fastgrowing field. Indeed, robot-assisted therapy is an advanced new technology mainly in stroke rehabilitation. Although patients benefit from this enormous development of technology, including the presence of rehabilitation robots, the therapeutic field still suffering a lack in hand robotic rehabilitation devices. In this context, this work proposes a new design of a 3D printed hand exoskeleton for the stroke rehabilitation. Based on the EMG signals measured from the muscles responsible for the hand motion, the designed mechatronic system detects the intention of hand opening or hand closing from the stroked subject. Based on an embedded controller and five servomotors, the low cost robotic system is able to drive in real time three degrees of freedom (DOFs) for each finger. The real tests with stroked subjects showed that the designed hand exoskeleton architecture has a positive effect on the motion finger range and mainly in the hand ability to perform some simple tasks. The case studies showed a good recovery of the motor functions and consequently the developed system efficiency.
</abstract>

DESIGN AND DEVELOPMENT OF 3D PRINTED MYOELECTRIC ROBOTIC EXOSKELETON FOR HAND REHABILITATION

<abstract>
<title style='display:none'>Abstract</title>
In this paper, Self Tuning Fuzzy PID controller is developed to improve the performance of the electro-hydraulic actuator. The controller is designed based on the mathematical model of the system which is estimated by using System Identification technique. The model is performed in linear discrete model to obtain a discrete transfer function for the system. Model estimation procedures are done by using System Identification Toolbox in Matlab. Data for model estimation is taken from an experimental works. Fuzzy logic is used to tune each parameter of PID controller. Through simulation in Matlab by selecting appropriate fuzzy rules are designed to tune the parameters Kp, Ki and Kd of the PID controller, the performance of the hydraulic system has improved significantly compare to conventional PID controller.
</abstract>

Application Of Self-Tuning Fuzzy Pid Controller On Industrial Hydraulic Actuator Using System Identification Approach

<abstract>
<title style='display:none'>Abstract</title>
Technological advances in manufacturing smart high-performances electronic devices, increasingly available at lower costs, nowadays allow one to improve users’ quality of life in many application fields. In this work, the human–machine interaction obtained by using a next generation device (Myo armband) is analyzed and discussed, with a particular focus to healthcare applications such as upper-limb prostheses. An overview on application fields of the Myo armband and on the latest research works related to its use in prosthetic applications is presented; subsequently, the technical features and functionalities of this device are examined. Myo armband is a wearable device provided with eight electromyographic electrodes, a 9-axes inertial measurement unit and a transmission module. It sends the data related to the detected signals, via Bluetooth Low Energy technology, to other electronic devices which process them and act accordingly, depending on how they are programmed (in order to drive actuators or perform other specific functions). Applied to the prosthetic field, Myo armband allows one to overcome many issues related to the existing prostheses, representing a complete electronic platform that detects in real-time the main signals related to forearm activity (muscles activation and forearm movements in the three-dimensional space) and sends these data to the connected devices. Nowadays, several typologies of prostheses are available on the market; they can be mainly distinguished into low-cost prostheses, which are light and compact but allow for a limited number of movements, and high-end prostheses, which are much more complex and featured by high dexterity, but also heavy, bulky, difficult to control and very expensive. Finally, the Myo armband is an optimum candidate for prosthetic application (and many others) and offers an excellent low-cost solution for obtaining a reliable, easy to use system.
</abstract>

Technical Features and Functionalities of Myo Armband: An Overview on Related Literature and Advanced Applications of Myoelectric Armbands Mainly Focused on Arm Prostheses

<abstract>
<title style='display:none'>Abstract</title>
Strategies to facilitate detection enhancement in various methods of nitrate detection are presented in this paper. The main nitrate detection methods for the past 10 years will be reviewed according to their advantages and disadvantages, followed by a focus on the techniques on increasing detection capabilities. For each detection method, an investigation of the technique of improvement is carried out. The utilisation of advance material such as membranes, reduction agents etc. is also identified as the key aspect for system improvement. Many researchers in the field of potentiometry, electrochemical, and biosensors have focused on miniaturising their detection systems to enhance the capability of nitrate in-situ measurement. The performance of miniaturised sensor systems is comparable to that of conventional systems.
</abstract>

TECHNIQUES IN ADVANCING THE CAPABILITIES OF VARIOUS NITRATE DETECTION METHODS: A REVIEW

<abstract>
<title style='display:none'>Abstract</title>
It is of little surprise that falling is often accepted as a natural part of the aging process. In fact, it is the impact rather than the occurrence of falls in the elderly, which is of most concern. Aging people are typically frailer, more unsteady, and have slower reactions, thus are more likely to fall and be injured than younger individuals. Typically, research and industry presented various practical solutions for assisting the elderly and their caregivers against falls via detecting falls and triggering notification alarms calling for help as soon as falls occur in order to diminish fall consequences. Furthermore, fall likelihood prediction systems have been emerged lately based on the manipulation of the medical and behavioral history of elderly patients in order to predict the possibility of falls occurrence. Accordingly, response from caregivers may be triggered prior to most fall occurrences and accordingly prevent falls from taking place. This paper presents an extensive review for the state-of-the- art trends and technologies of fall detection and prevention systems assisting the elderly people and their caregivers. Furthermore, this paper discusses the main challenges, facing elderly fall prevention, along with suggestions for future research directions.
</abstract>

Fall Detection and Prevention for the Elderly: A Review of Trends and Challenges

Open Access Agreement

Sample Paper

International Journal on Smart Sensing and Intelligent Systems

The International Journal on Smart Sensing and Intelligent Systems (S2IS) is an on-line journal to be available to everybody at no cost. From volume 2018 the journal is...

Impact Factor

Five Year Impact Factor

Cite Score

SNIP

MNiSW

Google Scholar h5-index

Google Scholar h5-median

The International Journal on Smart Sensing and Intelligent Systems (S2IS) is an on-line journal to be available to everybody at no...

International Journal on Smart Sensing and Intelligent Systems

All Volumes and Issues in this Journal