Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Hospital Mortality Prediction Using Synthetic Minority Oversampling Technique

This paper aims to assess the efficiency, adaptiveness, and feasibility of various Machine Learning Algorithms for predicting mortality rates inside the hospital. The predictors and factors associated with in-hospital mortality rates in Intensive Care Units (ICUs) and other emergency sections have been poorly organized and categorized. Various Data Mining Techniques, and Machine Learning algorithms like Random Forest, XG-Boost, and Decision Tree, have been employed over a legitimate public dataset of various patients and their performance is measured and compared within and with existing works.
An AI Based Support System For The Diagnosis Of Breast Cancer

At present, the health-care system is facing a drastic increase in the number of cancer patients. The huge number of Breast cancer cases among women and its constant increase is a matter of concern for the clinical support systems. Early diagnosis plays a crucial role in the treatment of such a fatal ailment. The late diagnosis is the main cause of the death of many people around the globe. Also, Image processing techniques do exist for the prediction of breast cancer however the scope of misdiagnosis still exists in this technique. For this reason, timely and accurate screening of breast cancer is a major challenge for the clinical support system. In such cases, machine learning can be used as an effective tool to reduce uncertainty in clinical decision-making. Machine learning is the technique for making the machine capable through a large amount of data to perform learning and produce useful outputs. In this work, a machine learning based classifier is developed using the Support vector machine for the diagnosis of breast cancer illness. The clinical data used for the creation and validation of the model is obtained from the UCI repository. SMOTE based oversampling has been performed to balance the classes of malignant and benign tumors in the dataset. A set of seven important features were selected based on their f-value to reduce the time as well as the cost of medical examination. The proposed classifier has reported a testing accuracy of 98.32%.
Optimization of Convolutional Neural Network Topology and Training Parameters using Bees Algorithm

Designing a Convolutional Neural Network (CNN) topology with optimal performance is a challenge. This paper proposes a hybrid algorithm combining the nature-inspired Bees Algorithm (BA) with Bayesian Optimization (BO) technique to improve CNN performance (BA-BO-CNN). In addition, another hybrid algorithm is proposed which uses BA to optimize CNN hyperparameters (BA-CNN) to improve the network performance. Applying the hybrid BA-BO-CNN rather than BA-CNN on human electrocardiogram (ECG) signals the testing accuracy improved from 92.50% to 95%, on Cifar10DataDir benchmark data the accuracy on the validation set increased from 80.72% for BO-CNN to 82.22% for BA-BO-CNN, and finally, on benchmark digits images the training, validation and testing accuracies remained the same compared to the existing BO-CNN, but with more efficient computational time since it is reduced by 3 minutes and 12 seconds for BA-BO-CNN and 4 minutes and 14 seconds for BA-CNN.
Blockchain Based Solution for Secured Transmission of Examination Paper

In a traditional examination management system, a request for setting question papers and the actual question papers from the Controller of Examination (COE) to various teachers and vice-versa, is done physically. Further, the work of sending the finalized paper from the COE to various superintendents of examination is also done physically. In this system, the need for a safe and secure method of setting up and circulating the examination paper is high. The work of sending the finalized paper from COE to various superintendents of examination is done physically, which raises a lot of security concerns. The Blockchain-based system will eliminate the need for physical circulation of the paper, which in turn will reduce extra costs and time required for the paper. The proposed system fulfills all the above criteria with many different technologies working together as one system to provide the desired functionality. The proposed system uses a combination of Blockchain and Interplanetary File System (IPFS) to achieve decentralization and secure peer-to-peer communication between teacher and superintendent.
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms

Real-time video surveillance, through CCTV camera systems has become essential for ensuring public safety which is a priority today. Although CCTV cameras help a lot in increasing security, these systems require constant human interaction and monitoring. To eradicate this issue, intelligent surveillance systems can be built using deep learning video classification techniques that can help us automate surveillance systems to detect violence as it happens. In this research, we explore deep learning video classification techniques to detect violence as they are happening. Traditional image classification techniques fall short when it comes to classifying videos as they attempt to classify each frame separately for which the predictions start to flicker. Therefore, many researchers are coming up with video classification techniques that consider spatiotemporal features while classifying. However, deploying these deep learning models with methods such as skeleton points obtained through pose estimation and optical flow obtained through depth sensors, are not always practical in an IoT environment. Although these techniques ensure a higher accuracy score, they are computationally heavier. Keeping these constraints in mind, we experimented with various video classification and action recognition techniques such as ConvLSTM, LRCN (with both custom CNN layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm, 76.76% on CNN-Transformer and 80% on C3D.
Classification of Colon Cancer through analysis of histopathology images using Transfer Learning

The importance of histopathological image analysis in the early detection of colorectal cancer cannot be overstated. Because of the subjective nature of the evaluation, visual evaluation is time consuming and unreliable. Furthermore, the varying architectural and color characteristics of histological images make automated analysis extremely difficult. In the proposed work, we present a deep learning technique for distinguishing adenocarcinomas from healthy tissues and benign lesions by using Convolutional Neural Networks (CNNs). Although, a fully trained CNN on a large set of annotated Colorectal Cancer (CRC) samples yield high classification accuracy, it suffers from process tediousness. In our research, we used transfer learning techniques based on MobileNetV2 and InceptionResnetV2 models that have been pretrained on the LC25000 dataset. Transfer learning outperformed a fully pretrained CNN on CRC samples with 99.98% accuracy on the test dataset.
Improved Learning-Based Design Space Exploration for Approximate Instance Generation

Design methodologies for approximation of medium to large-scale accelerators have largely relied on search-based design space exploration. Due to the enormously sized solution space, Artificial Intelligence (AI) based heuristic search has remained one of the most common techniques to explore cost and performance trade-offs. As a sub-class of AI techniques, Monte Carlo Tree Search (MCTS) has recently shown great potential as an intelligent stochastic search algorithm to solve computational problems with large branching factors. MCTS employs reinforced learning that combines past statistics and stochastic processes to traverse new paths in the search tree. Inspired by the success of MCTS in the games domain (AlphaGo), it has been recently applied to explore the solution space of approximate circuits. In this paper, a modified MCTS-based intelligent search technique is proposed that can handle huge search space for approximation of fairly large benchmark circuits. The proposed learning-based heuristic for MCTS directs the search towards deeper nodes in the search tree resulting in a rather asymmetric tree to efficiently utilize the search budget on the exploration of more promising nodes in the design space. The modified MCTS algorithm is based on reinforced learning where an agent uses past information to improve the overall gain. Experimental results confirm that the proposed heuristics can reach out to deeper nodes in the search tree which account for larger area savings in the context of approximate computing. The modified search algorithm enables 34.23% more area savings than the original search algorithm.
A Device Fingerprinting Technique to Authenticate End-user Devices in Wireless Networks

Device fingerprinting is a method of identifying a device based on attributes provided by the device configuration and usage. This work utilizes traffic characteristics to generate the fingerprint of end-user devices in a wireless network. In order to create fingerprints, an optimized histogram-driven approach is applied to network traffic. Based on histograms and the density function, the optimized histogram method estimates a bin-width that minimizes expected least square errors (L2). An Extreme Learning Machine (ELM) has been used to classify the devices after fingerprints are generated. The ELM is derived from artificial neural networks, but it is much faster than conventional neural networks. To evaluate the proposed model, two benchmark datasets were used: SIGCOMM-2004 and SIGCOMM-2008. In SIGCOMM-2004, it fingerprinted 74 devices with 96.42% accuracy, while in SIGCOMM-2008, it fingerprinted 48 devices with 86.45% accuracy. Experiments have shown that the proposed method is the most effective.
Domestic Solid Waste Classification Using Convolutional Neural Networks

The overwhelming amount of household solid waste generated daily is alarming, and this contributes to the rise in pollution and drastic climate change. In such a context, automated waste classification at the initial stage of disposal can be an effective solution to separate recyclable items. Convolutional Neural Networks based on deep learning are often used for automated waste classification, but however, research works are limited to insufficient categories of waste such as the TrashNet dataset consisting of 2,527 images and 6 categories of waste. This dataset does not include other important categories such as battery, biological, and clothing items to reflect real-life environmental problems. Therefore, in this paper, a larger dataset consisting of 15,515 images and 12 categories of common household solid waste was used to evaluate the performance of DenseNet121, DenseNet169, EfficientNetB0, InceptionV3, MobileNetV2, ResNet50, VGG16, VGG19, and Xception Convolutional Neural Network models. Data augmentation was applied to solve the problem of class imbalance, and findings of my first research showed that the Xception model compiled with Adam optimiser outperformed all other models with an accuracy of 88.77% and an F1-score of 0.89. The performance of the model was improved to 89.57% with an F1-score of 0.90 when compiled with Nadam optimiser. However, further experimentation showed that the model did not generalise well despite reaching an accuracy of 93.42% and an F1-score of 0.93 when trained without data augmentation. This demonstrates the feasibility of the proposed model for real-life environmental problems.
Vision-based Static Hand Gesture Recognition using Dense-block Features and SVM classifier

Recognition of static hand gesture images using a vision-based technique remains a challenging task due to the variations in illumination, shape of the user’s hand, complex backgrounds, and so on. Further, the current convolutional neural network (CNN) technique requires large gesture images to train a model from scratch. Therefore, in this work novel features are obtained from the dense blocks of the fine-tuned DenseNet 201 to recognize the hand gesture images accurately. The proposed feature (denoted as DenseFeat) is a combination of features from last two dense blocks of the fine-tuned DenseNet which gathers enriched information for a gesture image. In addition, to remove the redundant information in the feature vector, the dimension of the DenseFeat is reduced using the principal component analysis (PCA) approach. A support vector machine (SVM) classifier based on a linear kernel is used to recognize the test gesture images. The efficacy of the proposed method is evaluated on two benchmark datasets using both subject-independent and subject-dependent cross-validation techniques. Furthermore, the qualitative and compressive quantitative analysis of the benchmark datasets illustrates that the proposed method outperforms state-of-the-art techniques in terms of mean accuracy and computational time.
Improving VM Placement in fog Center by Multi-objective optimization

Fog computing works in conjunction with cloud computing because cloud computing has latency issues for the critical applications. When the client requests reach to the fog server they are allocated to the Virtal machines (VMs) and then the VMs are placed into the physical machines (PMs). During the VM placement the provider tries to allocate all the requests to the VMs and then the VMs has to be placed inside the PMs. Virtual Machine Placement (VMP) problem is a NP-hard problem and the provider always wants to optimize the hosted VMs, minimize the PMs and minimize the resource wastage to optimize power consumption. This paper proposes Multi-objective integer linear programming (MOILP) and solves in two different methods such as lexicographical preference method and weighted sum method and it has been implemented for two categories of fog center such as homogeneous and heterogeneous and it has been found that the weighted sum method outperforms over the lexicographical method and also for heterogeneous fog center.
MTJ-Based p-Bit Designs for Enhanced Tunability

Probabilistic bits (p-bits) can be viewed as tunable random number generators (RNGs), whose stochasticity coupled with their tunability makes them enablers for an emerging class of applications, including probabilistic computing. Their tunability is the feature that makes them unique to conventional RNGs. This paper studies the tunability range of existing p-bit designs reported in the literature, highlighting that existing designs have a limited input voltage range within which the p-bit’s stochastic response can be tuned, on the order of sub 0.5 V. This may greatly limit their scalability in large p-bit networks. Accordingly, this work proposes several variant p-bit designs that enable a wider input voltage tunability range and a more continuous response for p-bits. The designs employ both bipolar and continuous stochastic MTJs, and demonstrate an enhancement in the tunability range beyond 4 V (close to half of the supply voltage range).
Empirical Validation of Automated Vulnerability Curation and Characterization

Prior research has shown that public vulnerability systems such as US National Vulnerability Database (NVD) rely on a manual, time-consuming, and error-prone process which has led to inconsistencies and delays in releasing final vulnerability results. This work provides an approach to curate vulnerability reports in real-time and map textual vulnerability reports to machine readable structured vulnerability attribute data. Designed to support the time consuming human analysis done by vulnerability databases, the system leverages the Common Vulnerabilities and Exposures (CVE) list of vulnerabilities and the vulnerability attributes described by the National Institute of Standards and Technology (NIST) Vulnerability Description Ontology (VDO) framework. Our work uses Natural Language Processing (NLP), Machine Learning (ML) and novel Information Theoretical (IT) methods to provide automated techniques for near real-time publishing, and characterization of vulnerabilities using 28 attributes in 5 domains. Experiment results indicate that vulnerabilities can be evaluated up to 95 hours earlier than using manual methods, they can be characterized with F-Measure values over 0.9, and the proposed automated approach could save up to 47% of the time spent for CVE characterization.
A Systematic Mapping Study on Low-Cost Immersive Virtual Reality for Microbiology

Educators are increasingly recognizing the advantages and benefits of Virtual Reality to broaden their teaching experiences in biology. Besides giving them another way to visualize learning objects, transforming their students from spectators to actors in the learning process. However, little has been discussed about the use of these low-cost immersive environments. Thus, the reviewed publications were retrieved by extracting keywords from documents indexed in four digital scientific libraries, which were then systematically filtered using exclusion, inclusion, semi-automated methods. This paper addresses three key points: which technologies are used in microbiology teaching, which of these are in the Virtual Reality technology domain, and which are implemented with low-cost resources. The mapping was performed between application domains and learning content and between design elements and learning content. We noted that most of the reviewed works have an evaluation of the usability of the applications, not focusing on the educational feasibility or the learning outcomes that can be obtained by them. We observed that the publications show gaps in the actual use and our review indicates unexplored areas, both at the level of the application project centered on the user and in the requirements of educational viability, thus being able to motivate future work in the field.
A Risk-Averse Preview-Based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles

A risk-averse preview-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning planner is presented for navigation of autonomous vehicles (AVs). To this end, the multilane road ahead of a vehicle is represented by a finite-state nonstationary Markov decision process (MDP). A risk assessment unit module is then presented, which leverages the preview information provided by sensors along with a stochastic reachability module to assign reward values to the MDP states and update them as scenarios develop. A sampling-based risk-averse preview-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm is finally developed, which generates samples using the preview information and reward function to learn risk-averse optimal planning strategies without actual interaction with the environment. The risk factor is imposed on the objective function to avoid fluctuation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> values, which can jeopardize the vehicle’s safety and/or performance. The overall hybrid automaton model of the system is leveraged to develop a feasibility check unit module that detects unfeasible plans and enables the planner system to react proactively to the changes of the environment. Finally, to verify the efficiency of the presented algorithm, its implementation on two highway driving scenarios of an AV in a varying traffic density is considered.
Accessibility and Positive Applied Uses of Extended Reality for Users With Reduced Mobility

This article presents the results of exploratory research to examine the accessibility limitations that eXtended Reality (XR) presents for people with reduced mobility. The research used in-depth interviews with experts and the application of the Delphi method. In particular, we explored potential solutions to provide a more accessible and usable XR, and looked at positive applied uses of this technology in terms of improving the well-being of people with reduced mobility. The results will be used to develop a cultural and educational XR prototype.
Drying of tomato slices using solar drying method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Suherman Suherman, Riri Marza Rilna, Naufal Afriandi, Evan Eduard Susanto, Hadiyanto Hadiyanto; Drying of tomato slices using solar drying method. AIP Conf. Proc. 28 February 2023; 2667 (1): 020002. https://doi.org/10.1063/5.0112428 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Teacher Assessment Model with Basic Uncertain Information

The assessment problems of university teachers under extent of uncertainties and ambiguities in education system. This paper explores how to handle and merge those factors in the input information and use the detailed assessment techniques. Here, the basic Information which could be useful in the assessment of an educational institution is proposed to confirm the effectiveness of staff eligibility on the bases of criteria. The flexibility of the proposed model with the involved uncertainties and ambiguities application in educational assessment is proposed to verify by fuzzy systems.
A Survey on 3D Scene Graphs: Definition, Generation and Application

With the advancement of intelligent agents, 3D scene understanding has become one of key tasks of computer vision. 3D scene understanding is challenging to represent effectively because objects form various relationships and constantly interact with each other. A scene graph is a powerful tool to concisely represent the properties and relationships of objects in a scene—enabling various multi-modal tasks. Therefore, research on 3D scene graph (3DSG) is attracting increasing attention. However, 3DSG research is in its early stage—requiring a systematically organized survey. In this paper, we survey the latest advancement of 3DSG. In addition, we clarify 3DSG concepts that are currently defined in various ways, provide real-world applicability and present future research directions.