Identifying pests in precision agriculture using low-cost image data acquisition.

Unmanned Aerial Vehicles (UAVs), often called drones, have gained progressive prevalence for their swift operational ability as well as their extensive applicability in diverse real-world situations. Of late, UAV usage in precision agriculture has attracted much interest from scientific community. This study will look at drone aid in precise farming. Big data has the ability to analyze enormous amounts of data. Due to this, it is one of the diverse crucial technologies of Information and Communication Technology (ICT) which had applied in precision agriculture for the abstraction of critical information as well as for assisting agricultural practitioners in the comprehension of the most feasible farming practices, and also for better decision-making. This work analyses communication protocols, as well as their application toward the challenge of commanding a drone fleet for protecting crops from infestations of parasites. For computer-vision tasks as well as data-intensive applications, the method of deep learning has shown much potential. Due to its vast potential, it can also be used in the field of agriculture. This research will employ several schemes to assess the efficacy of models includes Visual Geometry Group (VGG-16), the Convolutional Neural Network (CNN) as well as the Fully-Convolutional Network (FCN) in plant disease detection. The methods of Artificial Immune Systems (AIS) can be used in order to adapt deep neural networks to the immediate situation. Simulated outcomes demonstrate that the proposed method is providing superior performance over various other technologically-advanced methods.

The functional roles of short chain fatty acids as postbiotics in human gut: future perspectives.

The significance of gut microbiome and their metabolites (postbiotics) on human health could be a promising approach to treat various diseases that includes inflammatory bowel diseases, colon cancer, and many neurological disorders. Probiotics with potential mental health benefits (psychobiotics) can alter the gut-brain axis via immunological, humoral, neuronal, and metabolic pathways. Recently, probiotic bacteria like Lactobacillus and Bifidobacterium have been demonstrated for SCFAs production, which play a crucial role in a variety of diseases. These acids could enhance the production of mucins, antimicrobial proteins (bacteriocins and peptides), cytokines (Interleukin 10 and 18) and neurotransmitters (serotonin) in the intestine to main the gut microbiota, intestinal barrier system and other immune functions. In this review, we discuss about two mechanisms such as (i) SCFAs mediated intestinal barrier system, and (ii) SCFAs mediated gut-brain axis to elucidate the therapeutic options for the treatment/prevention of various diseases.

Anti-bacterial activity of Cymbopogon citratus nanoparticles against Vibrio species infecting aquatic organisms.

The presence of Vibrio parahaemolyticus, Vibrio alginolyticus, and Vibrio harveyi in aquatic organisms causes vibriosis, leading to their significant mortality. The efficacy of antibiotic treatment is reduced due to increasing antibiotic resistance. As a result, novel therapeutic agents are increasingly needed to treat outbreak of such diseases in aquatic organisms and humans. This study focuses on utilizing the bioactive compounds of Cymbopogon citratus as they are rich in a variety of secondary metabolites which promotes growth, natural immune response and disease resistance against pathogenic bacteria in various ecosystems. In silico studies were performed to evaluate the binding potential of the bioactive compounds against targeted protein beta - lactamase in Vibrio parahaemolyticus and metallo - beta - lactamase in V. alginolyticus via molecular docking. Cymbopogon citratus nanoparticles (CcNps) were synthesized, characterized and toxicity studies were performed by using Vigna radiata, and Artemia nauplii at different concentrations of Cymbopogon citratus nanoparticles. The results revealed that the synthesized nanoparticles were non-ecotoxic and act as potential growth promoters in plants. The antibacterial activity of synthesized Cymbopogon citratus was evaluated using agar well diffusion method. MIC, MBC, and biofilm assays performed by using different concentrations of synthesized nanoparticles. Thus, it was proved that Cymbopogon citratus nanoparticles showed better antibacterial activity against Vibrio species.

Fabrication, Characterization of Basic Blue 7 Dye-Doped PVA Films and Their Third-Order Nonlinear Optical Properties.

In this work, we report on the fabrication, characterization and the third-order nonlinear optical (NLO) properties of basic blue 7 (BB 7) dye‒doped polyvinyl alcohol (PVA) polymer thin films. The absorption spectra of BB 7 dye‒PVA films were measured by UV‒visible absorption studies. The functional groups of BB 7‒PVA films have been identified by FT‒IR spectroscopic analysis. The surface morphology was examined by AFM and SEM analysis which shows that the BB 7 dye‒doped PVA films have a homogenous and smooth surface. The nonlinear absorption and refraction of BB 7‒PVA films were respectively explored by open aperture and closed aperture Z‒scan technique using a 5 mW semiconductor diode laser of 635 nm wavelengths. The BB 7‒PVA films exhibit the reverse saturable absorption (RSA) and self-defocusing effect and the measured third‒order nonlinear susceptibility χ(3) values of these films were found to be of the order of 10- 5 esu. The present experimental results show that BB 7‒PVA films may have potential applications in future photonic and NLO devices.

Toxicity Evaluation, Plant Growth Promotion, and Anti-fungal Activity of Endophytic Bacteria-Mediated Silver Nanoparticles.

In recent years, the uses of silver nanoparticles have increased, which lead to nanoparticles discharge into aquatic bodies which may, if not well controlled, have harmful effect on different organisms. This calls for the need to constantly evaluate the toxicity level of nanoparticles. In this study, green biosynthesized silver nanoparticles mediated by endophytic bacteria Cronobacter sakazakii (CS-AgNPs) were subjected to toxicity evaluation by brine shrimp lethality assay. The ability of CS-AgNPs to improve plant growth by nanopriming of Vigna radiata L seeds treated with different concentrations (1ppm, 2.5ppm, 5ppm and 10ppm) in order to enhance biochemical constituents was investigated, also its inhibitory effect to growth of phytopathogenic fungi Mucor racemose was examined. Results showed that Artemia salina treated with CS-AgNPs exhibited good hatching percentage and LC50 value of 688.41 µg/ml when Artemia salina eggs were exposed to CS-AgNPs during hatching. Plant growth was enhanced at 2.5ppm CS-AgNPs, with increased photosynthetic pigments, protein, and carbohydrate content. This study suggests that silver nanoparticles synthesized via endophytic bacteria Cronobacter sakazakii are safe to use and can be utilized as means of combating plant fungal pathogens.

Identification of natural CTXM-15 inhibitors from aqueous extract of endophytic bacteria Cronobactersakazaki.

Nyctanthes arbor-tristis is one of India's valuable and populous medicinal plants which belongs to the family Oleaceae, and widely recognize as night jasmine. Over the years till date, different parts of the plant are used to treat or cure different ailments via various means of traditional medicine. Endophytes are organisms that live in the cell or body of other organisms with no apparent negative impact on the host which they inhabit and are of great source of novel bioactive compounds possessing important economic value. Secondary metabolites were identified in the aqueous extract of Cronobactersakazakii through quantitative phytochemical and GC-MS analysis. Antibacterial activity of the extract against clinical and ATCC strains of E. coli was assessed. Biological activity spectra of these compounds were predicted and categorized either as probably active (Pa) or probably inactive (Pi). Drug-likeness of bioactive compounds was determined as well as their ability to target protein (CTXM-15) responsible for antibiotic resistance in Gram-negative bacteria. Results revealed the presence of active compounds with pharmacological activities and considerable pharmacokinetics parameters. In addition, ligand-protein interactions of compounds with CTXM-15 proteins were identified. These results suggest that bioactive compounds of endophytic Cronobactersakazakii could contain novel chemical entities for the development of antibiotics against pathogenic microbes and other drugs for the amelioration of several infections.

Citrus limon phytocompounds decorated nanoparticles control poultry pathogens.

Antibiotic resistance poses a significant threat to the global health, food security, and environment. In poultry and livestock, antibiotics are beneficial since they improve poultry performance and are economically effective. Therefore, it is crucial to search for alternatives that can be environmentally safe and successful in treating these infections. In this study, we employed molecular docking to evaluate lemon peel phytochemical's protein binding capability against various poultry pathogens. The nanoparticles (LP AgNPs) obtained from the lemon peel were characterized and tested for their antibacterial activity against more poultry pathogens. LP AgNPs were characterized by using UV-Visible absorption spectra, which revealed an absorption peak at a wavelength of 420-440 nm. The FT-IR analysis demonstrated that flavonoids and phenolic acids acted as capping, reducing, and stabilizing agents during the biosynthesis of AgNPs. EDAX showed a strong peak was observed at 3 keV which revealed the absorption of metallic silver nanoparticles. The mean diameter was from 2 to 20 nm through HRTEM. Zeta potential of the LP AgNps at - 17.2 mV showed the high stability of the green synthesized AgNps. Maximum inhibitory concentrations of LP AgNps against the isolated poultry pathogens were 50 µg/ml concentration. The toxicity tests were performed in the Vigna radiata seedlings and Artemia nauplii, which showed less toxic effects and eco-friendly nature of the LP AgNps. LP AgNps have the potential to treat antibiotic resistant poultry pathogens, thereby paving the way for the development of value-added novel products incorporated with nanoparticles for treating various infection caused by antibiotic-resistant poultry pathogens.

Bioactive Compounds from Nyctanthes arbor tristis Linn as Potential Inhibitors of Janus Kinases (JAKs) Involved in Rheumatoid Arthritis.

Nyctanthes arbor tristis L (NAT) is one of the herbal plants whose parts are commonly used to treat diverse ailment including RA. Although the etiology of the autoimmune disorder RA is still unclear, actions of cytokines have been greatly associated with the mechanism of RA. Despite the huge development of drugs to combat this disorder, the search for alternative medicine is increasing due to the adverse effects of these synthetic drugs. Here, the ability of 30 selected bioactive compounds from the parts of NAT to bind effectively to target proteins of the Janus kinases as a potent inhibitor was predicted in an in silico manner through molecular docking procedure using Autodock 4.2.6 and their interactions visualized using Discovery Studio, followed by evaluating the physiochemical and ADMET properties of compounds of the lowest binding energy comparable to the reference drug baricitinib. Comparing the predicted target information with the standard drug baricitinib, 7 bioactive compounds may be potential lead drug for the treatment of RA owing to their lowest binding energy ranging from - 7.0 kcal/mol to - 10.49 kcal/mol and their pharmacokinetics properties. This can be used for further in vivo and in vitro studies to establish their potency as JAKs inhibitors to treat RA.

Myconanoparticles Break Antibiotic Resistance in Staphylococcus aureus and Acinetobacter baumannii.

In this current study, the extracts of endophytic fungi (Aspergillus niger) were utilized to synthesize the silver nanoparticles (AnNps). In silico screening was carried out by docking secondary metabolites of Aspergillus niger with drug-resistant proteins such as penicillin-binding protein (pbp2a) and clumping factor A of Staphylococcus aureus, penicillin-binding protein (PBP3), and outer membrane protein of Acinetobacter baumannii. The molecular docking analysis revealed the interaction between secondary metabolites of Aspergillus niger with virulence factors of the pathogenic bacteria. AnNps are characterized by various physicochemical methods to determine the size, shape, and stability. Antibacterial efficacy of synthesized nanoparticles (AnNps) was screened in clinical pathogens. AnNp treatment significantly reduced the growth of MDR pathogens. The results suggested that AnNps can be incorporated to produce antimicrobial agents to control drug resistant pathogenic bacteria.

Isolation and genetic analysis of Porcine circovirus 2 in southern India evidences high circulation of Porcine circovirus 2d genotype.

BACKGROUND: Porcine circovirus 2 is globally noted swine pathogen with multiple genotypes associated with vast clinical and subclinical outcomes. This study aimed to isolate and characterize PCV2 genotypes circulating in southern states of India. METHODS AND RESULTS: A total of 434 field samples comprising of serum (n = 273), tissues (n = 109) and swabs (n = 52) collected from swine during 2019 to 2021 from southern states of India were screened for PCV2 by specific polymerase chain reaction (PCR) assay. Molecular prevalence of PCV2 in southern India was found to be 12.21% (n = 53). All the 53 PCV2 positive samples were further subjected to the PCR assay with designed primers targeting full length amplification of ORF2 gene of PCV2 for molecular characterization. Randomly 32 positive samples by full length PCV2-ORF2 gene PCR were sequenced for genotyping. Signature motif and phylogenetic analysis of 32 PCV2 sequences revealed 62.5% (n = 20) prevalence of PCV2d genotype followed by 21.8% (n = 7) of PCV2h or PCV2-IM1 and 15.6% (n = 5) of PCV2b genotypes. Twenty five PCR positive field samples were subjected for virus isolation in PK15 cells and characterized. Out of 25 samples processed 5 (20%) PCV2 isolates obtained in this study were confirmed by PCR and immune fluorescence assay. Molecular characterization of PK15 adapted five PCV2 isolates confirmed circulation of PCV2d, PCV2h and PCV2b genotypes in pigs under field conditions in southern India. CONCLUSIONS: Isolation and molecular epidemiological study of PCV2 in southern states of India evidences high circulation of PCV2d genotypes in field conditions in comparison to other genotypes.

Molecular evidence of porcine circovirus 3 infection in swine: first report in southern India.

The present study examined 434 field samples including serum (n = 273), swabs from natural orifices (n = 52) and postmortem tissue samples (n = 109) from both suspected and asymptomatic swine from Andhra Pradesh, Karnataka, Kerala, Pondicherry, Tamil Nadu, and Telangana states in southern India. All the samples were processed for molecular screening of PCV3 by specific PCR assay. Overall molecular positivity rate of PCV3 was found to be 0.7% in southern India with one sample positive from each state of Tamil Nadu, Kerala and Telangana. All the three PCR positive PCV3 samples are detected from reproductive failures and were processed and propagated in PK15 cell line for virus isolation. Out of 3 samples processed, one (INDKL9PK76) PCV3 isolate could be obtained in this study and it was confirmed by specific PCR at third and fifth passage levels. Sequencing of PCV3 positive PCR amplicon (INDKL9PK76) revealed 1004 nucleotides and BLAST analysis confirmed partial sequence of the PCV3 genome. The aligned contig sequence was submitted to GenBank under the accession number of MW627201. PCV3 sequence in this study revealed 99% homology with PCV3 isolates from Europe and China. Phylogentic analysis of the PCV3 isolate-INDKL9PK76 sequence along with established PCV3 genotypes revealed clustering within PCV3 genotypes. Characterization of PCV3 (INDKL9PK76) isolate based on deduced amino acid composition of PCV3-capsid protein revealed "A" (alanine) and "R" (arginine) at 24th and 27th residues respectively confirming the incidence of PCV3a genotype. This study evidences PCV3 associated reproductive failure in domestic pigs for the first time in southern India.

Canine hepatic calodiosis with cirrhosis.

The nematode parasite Calodium hepaticum (Capillaria hepatica) has a global distribution and is commonly reported in rodents (definitive host), dogs, cats and wild animals. Humans especially children are more susceptible to the parasitic infection. This paper documents an incidental finding of hepatic calodiosis with cirrhosis in a stray dog and discusses the zoonotic implications. A non descript dog was brought for necropsy examination to the Department of Veterinary Pathology, Madras Veterinary College, Tamil Nadu, India. Liver appeared dark brown, mottled with multifocal random variably sized, grey white flat firm areas. Histopathologically, liver tissue revealed multiple random encysted large collection of eggs surrounded by mild inflammation with a few lymphocytes, macrophages and fine fibrosis. The eggs had characteristic barrel shape, bipolar ends, bilayered wall, cross striations between the walls, and yolk. Periodic acid Schiff stain demonstrated the glycolic wall of ova. Marked portal to portal fibrosis was demonstrated by Masson's trichrome (for collagen) and by Warthin-Starry (for reticulin) stains. The stage of parasitic infection was diagnosed as intermediate to chronic due to fibrosis. A need to study the prevalence of the disease in rodents, human and animals is emphasized. Improper burial of carcasses of rodents and dogs may contribute to spread of infection. Pets and stray animals may transmit infection to human and pose health risk.

Autophagy and Programmed Cell Death Are Critical Pathways in Jasmonic Acid Mediated Saline Stress Tolerance in Oryza sativa.

Saline stress is the most limiting condition impacting the plant growth, development, and productivity. In this present study, jasmonic acid (JA) was used as a foliar spray on the rice seedlings grown under saline stress. Increase in photosynthetic pigments, anthocyanin, and total protein content was observed with JA treatment while NaCl showed reduction in biochemical constituents and enhanced antioxidant enzyme activity. The leaf cells of NaCl-treated seedlings accumulated more ROS and had more fragmented nuclei, whereas JA decreased the accumulation and fragmentation during saline stress. In NaCl treatment, gene expression analysis showed many fold upregulation in comparison with other treatments. The results suggest that JA acts as a promoter for growth, physiological, biochemical, and cellular contents, as well as ameliorate the effects of saline stress. The expression of genes demonstrated that saline stress may promote autophagy, which leads to autophagic cell death, and improve tolerance to saline stress in rice seedlings via the jasmonic acid signaling pathway. However, the mechanism by which jasmonate signaling induces autophagy and cell death is unknown and requires further exploration.

Moringa oleifera Nanoparticles Demonstrate Antifungal Activity Against Plant Pathogenic Fungi.

Fungal diseases in plants are creating numerous problems in the developed and developing nations. Silver, a notable metal because of its inertness and its role in nanoscience, has received a considerable amount of focus in the development of an ecofriendly green solution to control many microbial infections. The herbal product from various plant sources with the combination of silver was used to develop nanoparticles, against the pathogens. In this study, we developed Moringa oleifera leaf- and flower-mediated silver nanoparticles with the particle size of 77.45 nm and 63.20 nm respectively. Fungicidal activity of both Moringa oleifera leaf (MLNp) and flower (MFNp) nanoparticles was studied in vitro against plant pathogenic fungi Pestalotiopsis mangiferae isolated from infected coconut palm. Nanoparticles from Moringa oleifera leaves and flowers reduced the radial growth of fungi significantly even at lower concentrations and acted as a potent fungistatic agent.

Molecular detection of porcine parvovirus 1-associated reproductive failure in southern India.

This study used 56 aborted and stillborn fetuses from organized swine farms in Tamil Nadu and Kerala, southern states of India. All samples were screened by using a PCR assay that targets the NS1 gene for PPV. Furthermore, the PCR positive samples were subjected to amplification of the VP2 gene of PPV1 with designed primers and sequenced for further study. The PCR screening of 56 samples found that 14.3% (n = 8) were positive for PPV genome. According to VP2 gene-based PCR for PPV1, 897 bp specific amplicons were detected in all eight of the samples. Two of the eight positive samples (L17 and T5) were sequenced and annotated randomly. The BLAST analysis of contig sequence INDTNCHN-T5 revealed 100% sequence homology with Chinese PPV1genome, whereas sequence from INDTNCHN-L17 revealed 99.43% sequence homology with Spain, Chinese, and German. PPV1 sequences and both the sequences INDTNCHN-T5 and INDTNCHN-L17 were submitted to the GenBank under the accession numbers MW822566 and MW822567 respectively. A phylogenetic analysis of the sequences in this study revealed specific grouping along with PPV1 strains in cluster E. Amino acid analysis of both isolated sequences in addition to the reference sequence from PPV1 showed variations in position 215 (I to T) in both the isolates, variation at position 228 (Q to E) in T5 isolate and variations at position 59 (L to M) and 314 (K to E) in L17 isolate. This study represents the first report of PPV1 cluster E in Tamil Nadu, southern India.

Hybrid Block-Based Lightweight Machine Learning-Based Predictive Models for Quality Preserving in the Internet of Things- (IoT-) Based Medical Images with Diagnostic Applications.

In the contemporary era of unprecedented innovations such as the Internet of Things (IoT), modern applications cannot be imagined without the presence of a wireless sensor network (WSN). Nodes in WSN use neighbor discovery (ND) protocols to have necessary communication among the nodes. The neighbor discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximum percentage of neighbors discovered. The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots. This work develops a lightweight intrusion detection system (IDS) based on two machine learning approaches, namely, feature selection and feature classification, in order to improve the security of the Internet of Things (IoT) while transferring medical data through a cloud platform. In order to take advantage of the comparatively cheap processing cost of the filter-based technique, the feature selection was carried out. The two methods are found to have certain drawbacks. The first category disturbs the original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbors. When the second category is followed, it may have inefficient slots in the wake-up schedules leading to performance degradation. Therefore, the motivation behind the work in this paper is that by combining the two categories, it is possible to reap the benefits of both and get rid of the limitations of both. Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring the original integrity of wake-up schedules and adding efficient active slots. Thus, a Hybrid Approach to Neighbor Discovery (HAND) protocol is realized in WSN. The simulation study revealed that HAND outperforms the existing indirect ND models.

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance.

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.

Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis.

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F-measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM).

A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network.

Background: The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste products from human food consumption, the creation of bile, the regulation of metabolic activities, the cleaning of the blood by sensitizing digestive management, and the storage of vitamins and minerals. To perform the classification of liver illnesses using computed tomography (CT scans), two critical phases must first be completed: liver segmentation and categorization. The most difficult challenge in categorizing liver disease is distinguishing the liver from the other organs near it. Methodology. Liver biopsy is a kind of invasive diagnostic procedure, widely regarded as the gold standard for accurately estimating the severity of liver disease. Noninvasive approaches for examining liver illnesses, such as blood serum markers and medical imaging (ultrasound, magnetic resonance MR, and CT) have also been developed. This approach uses the Partial Differential Technique (PDT) to separate the liver from the other organs and Level Set Methodology (LSM) for separating the cancer location from the surrounding tissue based on the projected pictures used as input. With the help of an Improved Convolutional Classifier, the categorization of different phases may be accomplished. Results: Several accuracies, sensitivity, and specificity measurements are produced to assess the categorization of LSM using an Improved Convolutional classifier. Approximately, 97.5% of the performance accuracy of the liver categorization is achieved with a 94.5% continuous interval (CI) of [0.6775 1.0000] and an error rate of 2.1%. The suggested method's performance is compared to that of two existing algorithms, and the sensitivity and specificity provide an overall average of 96% and 93%, respectively, with 95% Continuous Interval of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity and specificity, respectively.

Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm.

The digestive system is one of the essential systems in human physiology where the stomach has a significant part to play with its accessories like the esophagus, duodenum, small intestines, and large intestinal tract. Many individuals across the globe suffer from gastric dysrhythmia in combination with dyspepsia (improper digestion), unexplained nausea (feeling), vomiting, abdominal discomfort, ulcer of the stomach, and gastroesophageal reflux illnesses. Some of the techniques used to identify anomalies include clinical analysis, endoscopy, electrogastrogram, and imaging. Electrogastrogram is the registration of electrical impulses that pass through the stomach muscles and regulate the contraction of the muscle. The electrode senses the electrical impulses from the stomach muscles, and the electrogastrogram is recorded. A computer analyzes the captured electrogastrogram (EGG) signals. The usual electric rhythm produces an enhanced current in the typical stomach muscle after a meal. Postmeal electrical rhythm is abnormal in those with stomach muscles or nerve anomalies. This study considers EGG of ordinary individuals, bradycardia, dyspepsia, nausea, tachycardia, ulcer, and vomiting for analysis. Data are collected in collaboration with the doctor for preprandial and postprandial conditions for people with diseases and everyday individuals. In CWT with a genetic algorithm, db4 is utilized to obtain an EGG signal wave pattern in a 3D plot using MATLAB. The figure shows that the existence of the peak reflects the EGG signal cycle. The number of present peaks categorizes EGG. Adaptive Resonance Classifier Network (ARCN) is utilized to identify EGG signals as normal or abnormal subjects, depending on the parameter of alertness (µ). This study may be used as a medical tool to diagnose digestive system problems before proposing invasive treatments. Accuracy of the proposed work comes up with 95.45%, and sensitivity and specificity range is added as 92.45% and 87.12%.