Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) to EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

Nicotine-mediated therapy for Parkinson's disease in transgenic Caenorhabditis elegans model.

Parkinson's disease resultant in the degeneration of Dopaminergic neurons and accumulation of α-synuclein in the substantia nigra pars compacta. The synthetic therapeutics for Parkinson's disease have moderate symptomatic benefits but cannot prevent or delay disease progression. In this study, nicotine was employed by using transgenic Caenorhabditis elegans Parkinson's disease models to minimize the Parkinson's disease symptoms. The results showed that the nicotine at 100, 150, and 200 µM doses reduced degeneration of Dopaminergic neurons caused by 6-hydroxydopamine (14, 33, and 40%), lowered the aggregative toxicity of α-synuclein by 53, 56, and 78%, respectively. The reduction in food-sensing behavioral disabilities of BZ555 was observed to be 18, 49, and 86%, respectively, with nicotine concentrations of 100 µM, 150 µM, and 200 µM. Additionally, nicotine was found to enhance Daf-16 nuclear translocation by 14, 31, and 49%, and dose-dependently increased SOD-3 expression by 10, 19, and 23%. In summary, the nicotine might a promising therapy option for Parkinson's disease.

Assessment of Suitable Habitat of the Demoiselle Crane (Anthropoides virgo) in the Wake of Climate Change: A Study of Its Wintering Refugees in Pakistan.

The inevitable impacts of climate change have reverberated across ecosystems and caused substantial global biodiversity loss. Climate-induced habitat loss has contributed to range shifts at both species and community levels. Given the importance of identifying suitable habitats for at-risk species, it is imperative to assess potential current and future distributions, and to understand influential environmental factors. Like many species, the Demoiselle crane is not immune to climatic pressures. Khyber Pakhtunkhwa and Balochistan provinces in Pakistan are known wintering grounds for this species. Given that Pakistan is among the top five countries facing devastating effects of climate change, this study sought to conduct species distribution modeling under climate change using data collected during 4 years of field surveys. We developed a Maximum Entropy distribution model to predict the current and projected future distribution of the species across the study area. Future habitat projections for 2050 and 2070 were carried out using two representative concentration pathways (RCP 4.5 and RCP 8.5) under three global circulation models, including HADGEM2-AO, BCC-CSM1-1, and CCSM4. The most influential factors shaping Demoiselle Crane habitat suitability included the temperature seasonality, annual mean temperature, terrain ruggedness index, and human population density, all of which contributed significantly to the suitability (81.3%). The model identified 35% of the study area as moderately suitable (134,068 km2) and highly suitable (27,911 km2) habitat for the species under current climatic conditions. Under changing climate scenarios, our model predicted a major loss of the species' current suitable habitat, with shrinkage and shift towards western-central areas along the Pakistan-Afghanistan boarder. The RCP 8.5, which is the extreme climate change scenario, portrays particularly severe consequences, with habitat losses reaching 65% in 2050 and 85% in 2070. This comprehensive study provides useful insights into the Demoiselle Crane habitat's current and future dynamics in Pakistan.

Discovery of Novel Natural Inhibitors Against SARS-CoV-2 Main Protease: A Rational Approach to Antiviral Therapeutics.

BACKGROUND/AIM: The global pandemic caused by the novel SARS-CoV-2 virus underscores the urgent need for therapeutic interventions. Targeting the virus's main protease (Mpro), crucial for viral replication, is a promising strategy. OBJECTIVE: The current study aims to discover novel inhibitors of Mpro. METHODS: The current study identified five natural compounds (myrrhanol B (C1), myrrhanone B (C2), catechin (C3), quercetin (C4), and feralolide (C5) with strong inhibitory potential against Mpro through virtual screening and computational methods, predicting their binding efficiencies and validated it using the in-vitro inhibition activity. The selected compound's toxicity was examined using the MTT assay on a human BJ cell line. RESULTS: Compound C1 exhibited the highest binding affinity, with a docking score of -9.82 kcal/mol and strong hydrogen bond interactions within Mpro's active site. A microscale molecular dynamics simulation confirmed the stability and tight fit of the compounds in the protein's active pocket, showing superior binding interactions. in vitro assays validated their inhibitory effects, with C1 having the most significant potency (IC50 = 2.85 µM). The non-toxic nature of these compounds in human BJ cell lines was also confirmed, advocating their safety profile. CONCLUSION: These findings highlight the effectiveness of combining computational and experimental approaches to identify potential lead compounds for SARS-CoV-2, with C1-C5 emerging as promising candidates for further drug development against this virus.

Autophagy and UPS pathway contribute to nicotine-induced protection effect in Parkinson's disease.

The gradual nature of age-related neurodegeneration causes Parkinson's disease (PD) and impairs movement, memory, intellectual ability, and social interaction. One of the most prevalent neurodegenerative conditions affecting the central nervous system (CNS) among the elderly is PD. PD affects both motor and cognitive functions. Degeneration of dopaminergic (DA) neurons and buildup of the protein α-synuclein (α-Syn) in the substantia nigra pars compacta (SNpc) are two major causes of this disorder. Both UPS and ALS systems serve to eliminate α-Syn. Autophagy and UPS deficits, shortened life duration, and lipofuscin buildup accelerate PD. This sickness has no cure. Innovative therapies are halting PD progression. Bioactive phytochemicals may provide older individuals with a natural substitute to help delay the onset of neurodegenerative illnesses. This study examines whether nicotine helps transgenic C. elegans PD models. According to numerous studies, nicotine enhances synaptic plasticity and dopaminergic neuronal survival. Upgrades UPS pathways, increases autophagy, and decreases oxidative stress and mitochondrial dysfunction. At 100, 150, and 200 µM nicotine levels, worms showed reduced α-Syn aggregation, repaired DA neurotoxicity after 6-OHDA intoxication, increased lifetime, and reduced lipofuscin accumulation. Furthermore, nicotine triggered autophagy and UPS. We revealed nicotine's potential as a UPS and autophagy activator to prevent PD and other neurodegenerative diseases.

Ni-Based Catalysts for CO2 Methanation: Exploring the Support Role in Structure-Activity Relationships.

The catalytic hydrogenation of CO2 to methane is one of the highly researched areas for the production of chemical fuels. The activity of catalyst is largely affected by support type and metal-support interaction deriving from the special method during catalyst preparation. Hence, we employed a simple solvothermal technique to synthesize Ni-based catalysts with different supports and studied the support role (CeO2, Al2O3, ZrO2, and La2O3) on structure-activity relationships in CO2 methanation. It is found that catalyst morphology can be altered by only changing the support precursors during synthesis, and therefore their catalytic behaviours were significantly affected. The Ni/Al2O3 with a core-shell morphology prepared herein exhibited a higher activity than the catalyst prepared with a common wet impregnation method. To have a comprehensive understanding for structure-activity relationships, advanced characterization (e. g., synchrotron radiation-based XAS and photoionization mass spectrometry) and in-situ diffuse reflectance infrared Fourier transform spectroscopy experiments were conducted. This research opens an avenue to further delve into the role of support on morphologies that can greatly enhance catalytic activity during CO2 methanation.

Rigid linker peptides improve the stability and anti-inflammation effect of human serum albumin and α-melanocyte-stimulating hormone fusion proteins.

The anti-inflammatory effect of α-melanocyte-stimulating hormone (α-MSH) in the central nervous system (CNS) has been reported for 40 years. However, the short half-life of α-MSH limits its clinical applications. The previous study has shown that a fusion protein comprising protein transduction domain (PTD), human serum albumin (HSA), and α-MSH extends the half-life of α-MSH, but its anti-inflammatory effect is not satisfactory. In this study, optimization of the structures of fusion proteins was attempted by changing the linker peptide between HSA and α-MSH. The optimization resulted in the improvement of various important characteristics, especially the stability and anti-inflammatory bioactivity, which are important features in protein medicines. Compared to the original linker peptide L0, the 5-amino-acid rigid linker peptide L6 (PAPAP) is the best option for further investigation due to its higher expression (increased by 6.27%), improved purification recovery (increased by 60.8%), excellent thermal stability (Tm = 83.5°C) and better inhibition in NF-κB expression (increased by 81.5%). From this study, the significance of the design of linker peptides in the study of structure-activity relationship of fusion proteins was proved.

Impact of hemodialysis on efficacies of the antiplatelet agents in coronary artery disease patients complicated with end-stage renal disease.

It is controversial whether hemodialysis affects the efficacy of the antiplatelet agents. We aimed to investigate the impact of hemodialysis on efficacies of the antiplatelet agents in coronary artery disease (CAD) patients complicated with end-stage renal disease (ESRD). 86 CAD patients complicated with ESRD requiring hemodialysis were consecutively enrolled. After 5-day treatment with aspirin and clopidogrel or ticagrelor, the platelet aggregations induced by arachidonic acid (PLAA) or adenosine diphosphate (PLADP), and the P2Y12 reaction unit (PRU) were measured before and after hemodialysis. The propensity matching score method was adopted to generate a control group with normal renal function from 2439 CAD patients. In patients taking aspirin, the PLAA remained unchanged after hemodialysis. In patients taking clopidogrel, the PLADP (37.26 ± 17.04 vs. 31.77 ± 16.09, p = 0.029) and corresponding clopidogrel resistance (CR) rate (23 [48.9%] vs. 14 [29.8%], p = 0.022) significantly decreased after hemodialysis, though PRU remained unchanged. Subgroup analysis indicated that PLADP significantly decreased while using polysulfone membrane (36.8 ± 17.9 vs. 31.1 ± 14.5, p = 0.024). In patients taking ticagrelor, PLADP, and PRU remained unchanged after hemodialysis. ESRD patients had higher incidences of aspirin resistance (AR) and CR compared to those with normal renal function (AR: 16.1% vs. 0%, p = 0.001; CR: 48.4% vs. 24.8%, p = 0.024). Hemodialysis does not have negative effect on the efficacies of aspirin, clopidogrel and ticagrelor in ESRD patients with CAD. ESRD patients have higher incidences of AR and CR compared with those with normal renal function.Trial registration ClinicalTrials.gov Identifier: NCT03330223, first registered January 4, 2018.

Novel and experimental therapeutics for the management of motor and non-motor Parkinsonian symptoms.

BACKGROUND  : Both motor and non-motor symptoms of Parkinson's disease (PD) have a substantial detrimental influence on the patient's quality of life. The most effective treatment remains oral levodopa. All currently known treatments just address the symptoms; they do not completely reverse the condition. METHODOLOGY: In order to find literature on the creation of novel treatment agents and their efficacy for PD patients, we searched PubMed, Google Scholar, and other online libraries. RESULTS: According to the most recent study on Parkinson's disease (PD), a great deal of work has been done in both the clinical and laboratory domains, and some current scientists have even been successful in developing novel therapies for PD patients. CONCLUSION: The quality of life for PD patients has increased as a result of recent research, and numerous innovative medications are being developed for PD therapy. In the near future, we will see positive outcomes regarding PD treatment.

A comparative study on the effects of human serum albumin and α-melanocyte-stimulating hormone fusion proteins on the anti-neuroinflammatory in the central nervous system of adult mice.

The immunomodulatory effects of α-melanocyte stimulating hormone (α-MSH) in the central nervous system (CNS) have been investigated for forty years. The clinical applications of α-MSH are limited due to its short half-life. Our previous study has indicated that the short half-life of α-MSH can be extended by fusion with carrier human serum albumin (HSA) and this fusion protein has also retained the anti-inflammatory effect on the CNS. This improvement is still far from the clinical requirements. Thus, we expected to enhance the half-life and activity of the fusion protein by optimizing the linker peptide to get closer to clinical requirements. In a previous study, we screened out two candidates in vitro experiments with a flexible linker peptide (fusion protein with flexible linker peptide, FPFL) and a rigid linker peptide (fusion protein with rigid linker peptide, FPRL), respectively. However, it was not sure whether the anti-inflammatory effects in vitro could be reproduced in vivo. Our results show that FPRL is the best candidate with a longer half-life compared to the traditional flexible linker peptides. Meanwhile, the ability of FPRL to penetrate the blood-brain barrier (BBB) was enhanced, and the inhibition of TNF-α and IL-6 was improved. We also found that the toxicity of FPRL was decreased. All of the results suggested that trying to choose the rigid linker peptide in some fusion proteins may be a potential choice for improving the unsatisfactory characteristics.

Role of electroanatomical mapping-guided superior vena cava isolation in paroxysmal atrial fibrillation patients without provoked superior vena cava triggers: a randomized controlled study.

AIMS: Data about whether empirical superior vena cava (SVC) isolation (SVCI) improves the success rate of paroxysmal atrial fibrillation (PAF) are conflicting. This study sought to first investigate the characteristics of SVC-triggered atrial fibrillation and secondly investigate the impact of electroanatomical mapping-guided SVCI, in addition to circumferential pulmonary vein isolation (CPVI), on the outcome of PAF ablation in the absence of provoked SVC triggers. METHODS AND RESULTS: A total of 130 patients undergoing PAF ablation underwent electrophysiological studies before ablation. In patients for whom SVC triggers were identified, SVCI was performed in addition to CPVI. Patients without provoked SVC triggers were randomized in a 1:1 ratio to CPVI plus SVCI or CPVI only. The primary endpoint was freedom from any documented atrial tachyarrhythmias lasting over 30 s after a 3-month blanking period without anti-arrhythmic drugs at 12 months after ablation. Superior vena cava triggers were identified in 30 (23.1%) patients with PAF. At 12 months, 93.3% of those with provoked SVC triggers who underwent CPVI plus SVCI were free from atrial tachyarrhythmias. In patients without provoked SVC triggers, SVCI, in addition to CPVI, did not increase freedom from atrial tachyarrhythmias (87.9 vs. 79.6%, log-rank P = 0.28). CONCLUSION: Electroanatomical mapping-guided SVCI, in addition to CPVI, did not increase the success rate of PAF ablation in patients who had no identifiable SVC triggers. REGISTRATION: ChineseClinicalTrials.gov: ChiCTR2000034532.

Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer.

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

Hepatic tuberculosis with lower gastrointestinal symptoms mimicking hepatic metastasis: A rare case report.

INTRODUCTION AND IMPORTANCE: The co-occurrence of Mycobacterium tuberculosis (MTB) affecting both the lungs and abdominal viscera is quite common, but instances of isolated Hepatic tuberculosis (TB) without concurrent clinical signs of TB are exceedingly rare. CASE PRESENTATION: We present a case of a 55-year-old woman who complained of abdominal pain, weight loss, fever and changes in bowel habits. A definitive diagnosis of hepatic TB was made through microscopic examination, revealing necrotizing granulomatous inflammation accompanied by caseous necrosis. The patient received antitubercular therapy without experiencing any noticeable side effects during follow-up. CLINICAL DISCUSSION: Hepatic TB without active pulmonary TB is quite rare case. The patient presents with abdominal pain, fever, weight loss and jaundice. The diagnostic process includes CT (computerize tomography) imaging and subsequent biopsy to confirm it histopathologically. Following the same approach, we did biopsy from the targeted hepatic lesion that showed hepatic tuberculosis. In most cases it is treated with anti-tubercular drugs. However, some complicated cases might need surgical intervention. CONCLUSION: This case report highlights the significance of considering TB into account as a potential cause in patients with lower gastrointestinal symptoms in TB endemic areas by emphasizing the diagnostic challenges posed by hepatic tuberculosis with isolated liver involvement. In order to prevent serious complications of abdominal TB, early diagnosis and timely treatment is crucial.

Key players in the regulation of iron homeostasis at the host-pathogen interface.

Iron plays a crucial role in the biochemistry and development of nearly all living organisms. Iron starvation of pathogens during infection is a striking feature utilized by a host to quell infection. In mammals and some other animals, iron is essentially obtained from diet and recycled from erythrocytes. Free iron is cytotoxic and is readily available to invading pathogens. During infection, most pathogens utilize host iron for their survival. Therefore, to ensure limited free iron, the host's natural system denies this metal in a process termed nutritional immunity. In this fierce battle for iron, hosts win over some pathogens, but others have evolved mechanisms to overdrive the host barriers. Production of siderophores, heme iron thievery, and direct binding of transferrin and lactoferrin to bacterial receptors are some of the pathogens' successful strategies which are highlighted in this review. The intricate interplay between hosts and pathogens in iron alteration systems is crucial for understanding host defense mechanisms and pathogen virulence. This review aims to elucidate the current understanding of host and pathogen iron alteration systems and propose future research directions to enhance our knowledge in this field.

Recent Advances in Natural Products with Anti-Leukemia and Anti-Lymphoma Activities.

Leukemia and lymphoma are the most common blood cancers, which pose a critical threat to the health of adults and children. The total incidence and mortality rates of both are approximately 6% globally. Compared with the expensive cost of CAR T cell therapy, natural products from animals, plants and microorganisms have the characteristics of wide-range sources and costeffectiveness in the treatment of cancer. Moreover, the drug resistance that emerged in leukemia and lymphoma treatments shows an urgent need for new drugs. However, in addition to the natural products that have been marketed in the treatment of leukemia and lymphoma, there have been a large number of studies on natural products that fight blood cancer in recent years. This review summarized the recent studies on natural compounds with anti-lymphoma and anti-leukemia activities, hoping to provide novel weapons into the drug development arsenal.

Recent Advances in Xanthine Oxidase Inhibitors.

Uric acid is a product of purine nucleotide metabolism, and high concentrations of uric acid can lead to hyperuricemia, gout and other related diseases. Xanthine oxidase, the only enzyme that catalyzes xanthine and hypoxanthine into uric acid, has become a target for drug development against hyperuricemia and gout. Inhibition of xanthine oxidase can reduce the production of uric acid, so xanthine oxidase inhibitors are used to treat hyperuricemia and related diseases, including gout. In recent years, researchers have obtained new xanthine oxidase inhibitors through drug design, synthesis, or separation of natural products. This paper summarizes the research on xanthine oxidase inhibitors since 2015, mainly including natural products, pyrimidine derivatives, triazole derivatives, isonicotinamide derivatives, chalcone derivatives, furan derivatives, coumarin derivatives, pyrazole derivatives, and imidazole derivatives, hoping to provide valuable information for the research and development of novel xanthine oxidase inhibitors.

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification.

In the realm of hyperspectral image classification, the pursuit of heightened accuracy and comprehensive feature extraction has led to the formulation of an advance architectural paradigm. This study proposed a model encapsulated within the framework of a unified model, which synergistically leverages the capabilities of three distinct branches: the swin transformer, convolutional neural network, and encoder-decoder. The main objective was to facilitate multiscale feature learning, a pivotal facet in hyperspectral image classification, with each branch specializing in unique facets of multiscale feature extraction. The swin transformer, recognized for its competence in distilling long-range dependencies, captures structural features across different scales; simultaneously, convolutional neural networks undertake localized feature extraction, engendering nuanced spatial information preservation. The encoder-decoder branch undertakes comprehensive analysis and reconstruction, fostering the assimilation of both multiscale spectral and spatial intricacies. To evaluate our approach, we conducted experiments on publicly available datasets and compared the results with state-of-the-art methods. Our proposed model obtains the best classification result compared to others. Specifically, overall accuracies of 96.87%, 98.48%, and 98.62% were obtained on the Xuzhou, Salinas, and LK datasets.

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques.

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.

Advancements in Neighboring-Based Energy-Efficient Routing Protocol (NBEER) for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have gained prominence in wireless sensor technology, featuring resource-limited sensor nodes deployed in challenging underwater environments. To address challenges like power consumption, network lifetime, node deployment, topology, and propagation delays, cooperative transmission protocols like co-operative (Co-UWSN) and co-operative energy-efficient routing (CEER) have been proposed. These protocols utilize broadcast capabilities and neighbor head node (NHN) selection for cooperative routing. This research introduces NBEER, a novel neighbor-based energy-efficient routing protocol tailored for UWSNs. NBEER aims to surpass the limitations of Co-UWSN and CEER by optimizing NHNS and cooperative mechanisms to achieve load balancing and enhance network performance. Through comprehensive MATLAB simulations, we evaluated NBEER against Co-UWSN and CEER, demonstrating its superior performance across various metrics. NBEER significantly maximizes end-to-end delay, reduces energy consumption, improves packet delivery ratio, extends network lifetime, and enhances total received packets analysis compared to the existing protocols.

Entropy-based grid approach for handling outliers: a case study to environmental monitoring data.

Grid-based approaches render an efficient framework for data clustering in the presence of incomplete, inexplicit, and uncertain data. This paper proposes an entropy-based grid approach (EGO) for outlier detection in clustered data. The given hard clusters obtained from a hard clustering algorithm, EGO uses entropy on the dataset as a whole or on an individual cluster to detect outliers. EGO works in two steps: explicit outlier detection and implicit outlier detection. Explicit outlier detection is concerned with those data points that are isolated in the grid cells. They are either far from the dense region or maybe a nearby isolated data point and therefore declared as an explicit outlier. Implicit outlier detection is associated with the detection of outliers that are perplexedly deviated from the normal pattern. The determination of such outliers is achieved using entropy change of the dataset or a specific cluster for each deviation. The elbow based on the trade-off between entropy and object geometries optimizes the outlier detection process. Experimental results on CHAMELEON datasets and other similar datasets suggested that the proposed approach(es) detect the outliers more precisely and extend the capability of outliers detection to an additional 4.5% to 8.6%. Moreover, the resultant clusters became more precise and compact when the entropy-based gridding approach is applied on top of hard clustering algorithms. The performance of the proposed algorithms is compared with well-known outlier detection algorithms, including DBSCAN, HDBSCAN, RE3WC, LOF, LoOP, ABOD, CBLOF and HBOS. Finally, a case study for detecting outliers in environmental data has been carried out using the proposed approach and results are generated on our synthetically prepared datasets. The performance shows that the proposed approach may be an industrial-oriented solution to outlier detection in environmental monitoring data.