KNNHI: Resilient KNN algorithm for heterogeneous incomplete data classification and K identification using rough set theory

The original K-nearest neighbour ( KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.

Modeling the Impact of Covid-19 on the Farm Produce Availability and Pricing in India

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

Beyond Recognition: Visual Contributions to Verbal Working Memory

Purpose: It is well recognized that adding the visual to the acoustic speech signal improves recognition when the acoustic signal is degraded, but how that visual signal affects postrecognition processes is not so well understood. This study was designed to further elucidate the relationships among auditory and visual codes in working memory, a postrecognition process. Design: In a main experiment, 80 young adults with normal hearing were tested using an immediate serial recall paradigm. Three types of signals were presented (unprocessed speech, vocoded speech, and environmental sounds) in three conditions (audio-only, audio–video with dynamic visual signals, and audio–picture with static visual signals). Three dependent measures were analyzed: (a) magnitude of the recency effect, (b) overall recall accuracy, and (c) response times, to assess cognitive effort. In a follow-up experiment, 30 young adults with normal hearing were tested largely using the same procedures, but with a slight change in order of stimulus presentation. Results: The main experiment produced three major findings: (a) unprocessed speech evoked a recency effect of consistent magnitude across conditions; vocoded speech evoked a recency effect of similar magnitude to unprocessed speech only with dynamic visual (lipread) signals; environmental sounds never showed a recency effect. (b) Dynamic and static visual signals enhanced overall recall accuracy to a similar extent, and this enhancement was greater for vocoded speech and environmental sounds than for unprocessed speech. (c) All visual signals reduced cognitive load, except for dynamic visual signals with environmental sounds. The follow-up experiment revealed that dynamic visual (lipread) signals exerted their effect on the vocoded stimuli by enhancing phonological quality. Conclusions: Acoustic and visual signals can combine to enhance working memory operations, but the source of these effects differs for phonological and nonphonological signals. Nonetheless, visual information can support better postrecognition processes for patients with hearing loss.

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

Hybridization of DBN with SVM and its Impact on Performance in Multi-Document Summarization

Data available from web based sources has grown tremendously with growth of the internet. Users interested in information from such sources often use a search engine to obtain the data which they edit for presentation to their audience. This process can be tedious especially when it involves the generation of a summary. One way to ease the process is by automation of the summary generation process. Efforts by researchers towards automatic summarization have yielded several approaches among them machine learning. Thus, recommendations have been made on combining the algorithms with different strengths, also called hybridization, in order to enhance their performance. Therefore, this research sought to establish the impact of hybridization of Deep Belief Network (DBN) with Support Vector Machine (SVM) on precision, recall, accuracy and F-measure when used in the case of query oriented multi-document summarization. The experiments were carried out using data from National Institute of Standards and Technology (NIST), Document Understanding Conference (DUC) 2006. The data was split into training and test data and used appropriately in DBN, SVM, SVM-DBN hybrid and DBN-SVM hybrid. Results indicated that the hybridized algorithm has better precision, accuracy and F-measure as compared to DBN. Pre-classification hybridization of DBN with SVM (SVM-DBN) gives the best results. This research implies that use of DBN and SVM hybrid algorithms would enhance query oriented multi-document summarization.

Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.

Rapport building and witness memory: Actions may ‘speak’ louder than words

Building rapport during police interviews is argued as important for improving on the completeness and accuracy of information provided by witnesses and victims. However, little experimental research has clearly operationalised rapport and investigated the impact of rapport behaviours on episodic memory. Eighty adults watched a video of a mock crime event and 24-hours later were randomly allocated to an interview condition where verbal and/or behavioural (non-verbal) rapport techniques were manipulated. Memorial performance measures revealed significantly more correct information, without a concomitant increase in errors, was elicited when behavioural rapport was present, a superiority effect found in both the free and probed recall phase of interviews. The presence of verbal rapport was found to reduce recall accuracy in the free recall phase of interviews. Post-interview feedback revealed significant multivariate effects for the presence of behavioural (only) rapport and combined (behavioural + verbal) rapport. Participants rated their interview experience far more positively when these types of rapport were present compared to when verbal (only) rapport or no rapport was present. These findings add weight to the importance of rapport in supporting eyewitness cognition, highlighting the potential consequences of impoverished social behaviours for building rapport during dyadic interactions, suggesting ‘doing’ rather than simply ‘saying’ may be more beneficial.

Recall Accuracy in Children: Age vs. Conceptual Thinking

The aim of this study was to replicate a previous experiment using a different stimulus event. The present study examined the relationship between age, development of conceptual thinking, and responses to free recall, suggestive and specific option-posing questions in children and adults. Sixty-three children (aged 7–14) and 30 adults took part in an experiment in which they first participated in a live staged event, then, a week later, were interviewed about the event and tested using the Word Meaning Structure Test. Age and level of conceptual thinking were positively correlated in children. Compared to age, conceptual thinking ability better predicted children's accurate free recall and inaccurate responses to specific option-posing questions, but not inaccurate responses to suggestive questions.

Spatiotemporal patterns of pain distribution and recall accuracy: a dose-response study

Objectives Clinical decisions rely on a patient’s ability to recall and report their pain experience. Monitoring pain in real-time (momentary pain) may reduce recall errors and optimize the clinical decision-making process. Tracking momentary pain can provide insights into detailed changes in pain intensity and distribution (area and location) over time. The primary aims of this study were (i) to measure the temporal changes of pain intensity, area, and location in a dose-response fashion and (ii) to assess recall accuracy of the peak pain intensity and distribution seven days later, using a digital pain mapping application. The secondary aims were to (i) evaluate the influence of repeated momentary pain drawings on pain recall accuracy and (ii) explore the associations among momentary and recall pain with psychological variables (pain catastrophizing and perceived stress). Methods Healthy participants (N=57) received a low (0.5 ml) or a high (1.0 ml) dose of hypertonic saline (5.8%) injection into the right gluteus medius muscle and, subsequently, were randomized into a non-drawing or a drawing group. The non-drawing groups reported momentary pain intensity every 30-s. Whereas the drawing groups reported momentary pain intensity and distribution on a digital body chart every 30-s. The pain intensity, area (pixels), and distribution metrics (compound area, location, radiating extent) were compared at peak pain and over time to explore dose-response differences and spatiotemporal patterns. All participants recalled the peak pain intensity and the peak (most extensive) distribution seven days later. The peak pain intensity and area recall error was calculated. Pain distribution similarity was determined using a Jaccard index which compares pain drawings representing peak distribution at baseline and recall. The relationships were explored among peak intensity and area at baseline and recall, catastrophizing, and perceived stress. Results The pain intensity, area, distribution metrics, and the duration of pain were lower for the 0.5 mL than the 1.0 mL dose over time (p<0.05). However, the pain intensity and area were similar between doses at peak pain (p>0.05). The pain area and distribution between momentary and recall pain drawings were similar (p>0.05), as reflected in the Jaccard index. Additionally, peak pain intensity did not correlate with the peak pain area. Further, peak pain intensity, but not area, was correlated with catastrophizing (p<0.01). Conclusions This study showed differences in spatiotemporal patterns of pain intensity and distribution in a dose-response fashion to experimental acute low back pain. Unlike pain intensity, pain distribution and area may be less susceptible in an experimental setting. Higher intensities of momentary pain do not appear to influence the ability to recall the pain intensity or distribution in healthy participants. Implications The recall of pain distribution in experimental settings does not appear to be influenced by the intensity despite differences in the pain experience. Pain distribution may add additional value to mechanism-based studies as the distribution reports do not vary with pain catastrophizing. REC# N-20150052

Analisis Sentimen Netizen Twitter terhadap Pemberitaan PPN Sembako dan Jasa Pendidikan dengan Pendekatan Social Network Analysis dan Naive Bayes Classifier

Pandemi covid-19 yang terjadi memberikan dampak di berbagai bidang kehidupan. Salah satu dampaknya penerimaan negara semakin tertekan hebat. Padahal di sisi lain negara dalam proses pemulihan ekonomi nasional (PEN) yang membutuhkan dana sangat besar. Sehingga pemerintah ingin menggenjot pendapatan negara dari pajak pertambahan nilai (PPN). Jika pemungutan PPN dapat dilakukan dengan seoptimal mungkin, maka akan meningkatkan penerimaan negara. Rencana tersebut mengakibatkan maraknya pemberitaan mengenai pengenaan PPN sembako dan jasa pendidikan di Indonesia. Pemberitaan tersebut secara otomatis memicu opini di masyarakat. Salah satu cara untuk melihat opini masyarakat adalah melalui media sosial Twitter. Penelitian ini bertujuan untuk mengkaji lebih dalam tentang network dan sentimen netizen Twitter tentang PPN Sembako dan jasa pendidikan. Hasil Social Network Analisis (SNA) menghasilkan 5 klaster dengan record ke-90 merupakan bottleneck node yaitu aktor utama penyebaran informasi antar klaster. Model Naive Bayes Classifier memberikan hasil Recall Accuracy bahwa untuk Accuracy Classified sebesar 74.865 persen sementara persentase untuk Incorrectly Classified Instance sebesar 25.135 persen. Hasil klasifikasi berdasarkan emosi terbentuk 5 ekspresi fear, sadness, surprise, joy, dan anger dan emosi kata yang paling banyak adalah emosi anger (amarah), artinya mayoritas respon masyarakat terhadap kebijakan pengenaan PPN sembako dan jasa pendidikan diidentifikasikan oleh R Studio sebagai wujud keamarahan.