Evaluation of adjective and adverb types for effective Twitter sentiment classification.

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

Remote Health Monitoring Systems for Elderly People: A Survey.

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.

Identification of offensive language in Urdu using semantic and embedding models.

Automatic identification of offensive/abusive language is very necessary to get rid of unwanted behavior. However, it is more challenging to generalize the solution due to the different grammatical structures and vocabulary of each language. Most of the prior work targeted western languages, however, one study targeted a low-resource language (Urdu). The prior study used basic linguistic features and a small dataset. This study designed a new dataset (collected from popular Pakistani Facebook pages) containing 7,500 posts for offensive language detection in Urdu. The proposed methodology used four types of feature engineering models: three are frequency-based and the fourth one is the embedding model. Frequency-based are either determined by the term frequency-inverse document frequency (TF-IDF) or bag-of-words or word n-gram feature vectors. The fourth is generated by the word2vec model, trained on the Urdu embeddings using a corpus of 196,226 Facebook posts. The experiments demonstrate that the stacking-based ensemble model with word2vec shows the best performance as a standalone model by achieving 88.27% accuracy. In addition, the wrapper-based feature selection method further improves performance. The hybrid combination of TF-IDF, bag-of-words, and word2vec feature models achieved 90% accuracy and 97% AUC. In addition, it outperformed the baseline with an improvement of 3.55% in accuracy, 3.68% in the recall, 3.60% in f1-measure, 3.67% in precision, and 2.71% in AUC. The findings of this research provide practical implications for commercial applications and future research.

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

Comparison of researchers' impact indices.

Researchers contribute to the frontiers of knowledge by establishing facts and reaching new conclusions through systematic investigations, and by subsequently publishing the outcomes of their research findings in the form of research papers. These research publications are indicative of researchers' scientific impact. Different bibliometric indices have been proposed to measure the impact or productivity of a researcher. These indices include publication count, citation count, number of coauthors, h-index, etc. The h-index, since its inception, has been ranked as the foremost impact indicator by many studies. However, as a consequence of the various short comings identified in h-index, some variants of h-index have been proposed. For instance, one dimension which requires significant attention is determining the ability of exceptional performers in a particular research area. In our study, we have compared effectiveness of h-index and some of its recent variants in identifying the exceptional performers of a field. We have also found correlation of h-index with recently proposed indices. A high correlation indicates same effect of these indices as of h-index and low correlation means these indices make non-redundant contribution while ranking potential researchers of a field of study. So far, effectiveness of these indices has not been explored/validated on real data sets of same field. We have considered these variants/modifications of h-index along with h-index and tested on comprehensive data set for the field of Computer Science. The Award winners' data set is considered as the benchmark for the evaluation of these indices for individual researchers. Results show that there is a low correlation of these indices with h-index, and in identifying exceptional performers of a field these indices perform better than h-index.

Pyrazinamide resistance and mutations in pncA among isolates of Mycobacterium tuberculosis from Khyber Pakhtunkhwa, Pakistan.

BACKGROUND: Pyrazinamide (PZA) is an important component of first-line drugs because of its distinctive capability to kill subpopulations of persistent Mycobacterium tuberculosis (MTB). The prodrug (PZA) is converted to its active form, pyrazinoic acid (POA) by MTB pncA-encoded pyrazinamidase (PZase). Mutation in pncA is the most common and primary cause of PZA resistance. The aim of the present study was to explore the molecular characterization of PZA resistance in a Pashtun-dominated region of Khyber Pakhtunkhwa, Pakistan. METHODS: We performed drug susceptibility testing (DST) on 753 culture-positive isolates collected from the Provincial Tuberculosis Control Program Khyber Pakhtunkhwa using the BACTEC MGIT 960 PZA method. In addition, the pncA gene was sequenced in PZA-resistant isolates, and PZA susceptibility testing results were used to determine the sensitivity and specificity of pncA gene mutations. RESULTS: A total of 69 isolates were PZA resistant (14.8%). Mutations were investigated in 69 resistant, 26 susceptible and one H37Rv isolates by sequencing. Thirty-six different mutations were identified in PZA-resistant isolates, with fifteen mutations, including 194_203delCCTCGTCGTG and 317_318delTC, that have not been reported in TBDRM and GMTV Databases and previous studies. Mutations Lys96Thr and Ser179Gly were found in the maximum number of isolates (n = 4 each). We did not detect mutations in sensitive isolates, except for the synonymous mutation 195C > T (Ser65Ser). The sensitivity and specificity of the pncA sequencing method were 79.31% (95% CI, 69.29 to 87.25%) and 86.67% (95% CI, 69.28 to 96.24%). CONCLUSION: Mutations in the pncA gene in circulating isolates of geographically distinct regions, especially in high-burden countries, should be investigated for better control and management of drug-resistant TB. Molecular methods for the investigation of PZA resistance are better than DST.

Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations.

EGFRs are a vast group of receptor tyrosine kinases playing an important role in a number of tumors, including lungs, head and neck, breast, and esophageal cancers. A couple of techniques are being used in the process of drug design. Drug repositioning or repurposing is a rising idea that consists of distinguishing modern remedial indications for officially existing dynamic pharmaceutical compounds. Here, a novel approach of analyzing drug-drug interaction networks, based on clustering methodology is used to reposition effective compounds against mutant EGFR having G719X, exon 19 deletions/insertions, L858R, and L861Q mutations. Data about 2062 drugs are obtained, and mining is performed to filter only those drugs which fulfill Lipinski rule of five. Clustering is performed, and DDIs are built on the clusters to identify effective drug compounds. Only 1052 compounds fulfill Lipinski rule. 12 clusters are formed for 1052 drugs compounds. DDIs are developed for each cluster. Only 15 drugs are suggested to be more effective assuming strong interactions in a DDI.

Prevalence of Pyrazinamide Resistance in Khyber Pakhtunkhwa, Pakistan.

AIMS: Pyrazinamide (PZA) is an important component of first-line tuberculosis (TB) treatment because of its distinctive capability to kill subpopulations of persister Mycobacterium tuberculosis (MTB). The significance of PZA can be understood by its inclusion in the most recent World Health Organization-recommended multidrug-resistant (MDR) TB regimen. Very little information is available about the prevalence of PZA-resistant TB from geographically distinct regions of high burden countries, including Khyber Pakhtunkhwa (KPK), Pakistan, because drug susceptibility testing (DST) of PZA is not regularly performed due to the complexity. In this study, we aimed to find the prevalence of PZA resistance in geographically distinct, Pashtun-dominant KPK Province of Pakistan and its correlation with other first- and second-line drug resistance. MATERIALS AND METHODS: In this study, DST of PZA was performed through an automated BACTEC MGIT 960 system (BD Diagnostic Systems). The resistant samples were further subjected to DST of isoniazid (INH), rifampicin (RIF), ethambutol (EMB), streptomycin (SM), moxicillin (MOX), amikacin (AMK), ofloxacin (OFX), kanamycin (KM), and capreomycin (CAP). RESULTS: Out of 1,075 MTB-positive isolates, 83 (7.7%) were found to be resistant to PZA. Among the PZA-resistant isolates, 76 (90-91.6%) and 67 (80-80.7%) were found to be resistant to INH and RIF, respectively, whereas 63 (76%) were resistant to both first-line drugs, INH and RIF (MDR-TB). The resistance level of EMB, OFX, and SM was also significantly high in PZA resistance, 35 (42%), 40 (48%), and 41 (49-50%) respectively. CONCLUSION: PZA resistance is significantly associated with other first- and second-line drug resistance. A significant number of PZA-resistant isolates are MDR cases. Therefore, DST of PZA should regularly be performed along with other drugs for better management of treatment of MDR and extensively drug resistant (XDR), to avoid side effects in patients.