Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques.

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Comprehensive review on toxic heavy metals in the aquatic system: sources, identification, treatment strategies, and health risk assessment.

Heavy metal pollution in water sources has become a major worldwide environmental issue, posing a threat to aquatic ecosystems and human health. The pollution of the aquatic environment is increasing as a result of industrialization, climate change, and urban development. The sources of heavy metal pollution in water include mining waste, leachates from landfills, municipal and industrial wastewater, urban runoff, and natural events such as volcanism, weathering, and rock abrasion. Heavy metal ions are toxic and potentially carcinogenic. They can also buildup in biological systems and cause bioaccumulation even at low levels of exposure, heavy metals can cause harm to organs such as the nervous system, liver and lungs, kidneys and stomach, skin, and reproductive systems. There were various approaches tried to purify water and maintain water quality. The main purpose of this article was to investigate the occurrence and fate of the dangerous contaminants (Heavy metal and metalloids) found in domestic and industrial effluents. This effluent mixes with other water streams and is used for agricultural activities and other domestic activities further complicating the issue. It also discussed conventional and non-conventional treatment methods for heavy metals from aquatic environments. Conclusively, a pollution assessment of heavy metals and a human health risk assessment of heavy metals in water resources have been explained. In addition, there have been efforts to focus on heavy metal sequestration from industrial waste streams and to create a scientific framework for reducing heavy metal discharges into the aquatic environment.

What do retina fellows-in-training think about the vitreoretinal surgical simulator: A multicenter survey.

Purpose: To profile vitreoretinal (VR) fellows-in-training from India exposed to the Eyesi surgical simulator, to identify potential barriers to voluntary use, and enumerate the most preferred tools and tasks before incorporating them into a formal skill-transfer curriculum. Methods: A questionnaire consisting of 22 questions was designed and circulated through an online portal (surveymonkey.com) to four different institutes of India having a VR surgical fellowship program and using a functional Eyesi (Haag-Streit) simulator. All fellows and trainees who were exposed to the simulator were eligible to participate, irrespective of time spent on the simulator and exposure to training steps on real patients. The responses collected were private and anonymous. Results: Of the 37 respondents, most (n = 25, 68%) considered surgical simulators to be the best training tool before operating on the human eye. A majority (n = 35, 94.5%) of participants spent <3 h per week on the simulator, which, most (n = 30, 81%) felt was not enough time. The main reasons for this underutilization were work-hour limitations (54.8%), lack of a structured training program (19.3%), or a dedicated supervisor (16.1%). Again, the majority (n = 33, 89%) of participants responded that VR surgical skills acquired during simulator training were transferrable to the operating room, which was reflected by their response (n = 31, 83.7%) that simulator-based training should be made mandatory before operating room exposure. Conclusion: This study gives an insight into the overall practice patterns and preferences in simulation training of surgical VR fellows-in-training across India. It indicates that the simulator is extremely helpful to fellows and if adopted, VR surgical simulators with organized, directed, and supervised sessions will considerably improve the surgical training experience.

Unusual presentation of a vascularized iris lesion in an infant.

We report an unusual presentation of a 10-month-old girl with left eye (LE) redness and watering. Evaluation showed an iris vascular lesion and lens opacity in her LE. Child underwent USG B-scan and ultrasound biomicroscopy, by which an extensive mass lesion arising from iris and ciliary body with absent calcification was revealed. Following extensive evaluation, child underwent cataract extraction and trans-scleral total excision of the mass lesion. Histopathology proved it as juvenile xanthogranuloma (JXG) with vascular proliferation. JXG is a rare benign self-limiting dermatologic disorder affecting mainly infants and small children. Ocular lesions are the most common extracutaneous manifestation. Cataract in JXG is less frequently reported. This case is reported due to its rarity and as it presented solely as an intraocular lesion with combined diffuse infiltration into ciliary body and cataract which is unusual. Early recognition and systematic approach helped in sight saving and organ salvaging.

A Case of Idiopathic Retinitis Vasculitis Aneurysms and Neuroretinitis (IRVAN) Treated with Adalimumab.

PURPOSE: To report a case of IRVAN in a 13-year-old girl responding well to Adalimumab and Azathioprine. RESULTS: A 13-year-old girl presented to us with central scotoma for a duration of 10 months. She was treated earlier with oral steroids with poor response. Fundus examination revealed features of IRVAN. She was treated with intravitreal dexamethasone implant in both eyes with oral Mycophenolate Mofetil (MMF) with transient response to it. So she was switched over to subcutaneous Adalimumab 40 mg once in 2 weeks and oral Azathioprine 50 mg BD. The disease activity was well controlled with the current regime. CONCLUSION: Though various treatment modalities have been described in literature for the treatment of IRVAN. This is the first case of IRVAN to be treated with Adalimumab along with Azathioprine to be reported.

Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning.

Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer.

At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%.

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer's Disease-Based Neurodegenerative Disorders.

Alzheimer's disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient's illness. To aid in the diagnosis of Alzheimer's disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer's disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer's disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network's classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms.

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time.

Banana Plant Disease Classification Using Hybrid Convolutional Neural Network.

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies.

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.

Real world outcomes of sutureless and glueless sclerally fixated intraocular lens implantation.

PURPOSE: To report the outcomes of sutureless intrascleral fixation of a 3-piece intraocular lens in the ciliary sulcus, in a large cohort of patients with aphakia of various aetiology METHODS: Retrospective, non-comparative, single centre interventional study of 250 aphakic eyes of various causes, which underwent sutureless and glueless intrascleral fixation of 3-piece intraocular lens (IOL). All patients were required to have at least 3 months of follow up post procedure to be included in the study. Anatomical and functional outcomes obtained were statistically analysed for significance. RESULTS: A total of 250 eyes of 246 patients were included in the study population. The average age was 56.5 years ± 16.4 (range 6-86 years). The mean best-corrected visual acuity (BCVA) significantly improved from 0.74 ± 0.6 logMAR (approx. Snellen equivalent 20/110) to 0.48 ± 0.36 logMAR (approx. Snellen equivalent 20/60), (p < 0.001) following surgery. Early postoperative complication (<2 weeks) included hypotony (n = 10, 4%), ocular hypertension (n = 38,15.2%) and vitreous haemorrhage (n = 50, 20%). Late complications included retinal detachment (n = 14, 5.6%%), cystoid macular oedema (n = 24, 9.6%), scleral erosion (n = 1, 0.4%), haptic extrusion to subconjunctival space (n = 3, 1.2%) and IOL subluxation or dislocation (n = 5, 2%) CONCLUSION: This cost-effective and easier technique of sutureless scleral fixated 3-piece IOL implantation provided good visual acuity outcomes in a large cohort of patients and was well tolerated.

All India Ophthalmological Society (AIOS) Task Force guidelines to prevent intraocular infections and cluster outbreaks after cataract surgery.

Infectious endophthalmitis is a serious and vision-threatening complication of commonly performed intraocular surgeries such as cataract surgery. The occurrence of endophthalmitis can result in severe damage to the uveal and other ocular tissues even among patients undergoing an uncomplicated surgical procedure. If the infections result from common factors such as surgical supplies, operative or operation theater-related risks, there can be a cluster outbreak of toxic anterior segment syndrome (TASS) or infectious endophthalmitis, leading to several patients having an undesirable outcome. Since prevention of intraocular infections is of paramount importance to ophthalmic surgeons, the All India Ophthalmological Society (AIOS) has taken the lead in the formation of a National Task Force to help ophthalmic surgeons apply certain universal precautions in their clinical practice. The Task Force has prepared a handy checklist and evidence-based guidelines to minimize the risk of infectious endophthalmitis following cataract surgery.

An extended phenotype of RP1L1 maculopathy - case report.

BACKGROUND: To report the ophthalmological findings of a new phenotypical variant of RP1L1 maculopathy in an Indian patient with a homozygous variant in the RP1L1 gene. MATERIALS AND METHODS: A 39-year-old male presented with complaints of disturbance in the central field of vision in both eyes (BE) for a duration of 6 months. He underwent ophthalmic examinations and diagnostic imaging. A complete retinal degeneration panel consisting of 228 genes was evaluated for pathologic variations using next-generation sequencing (NGS), which showed a variant in the RP1L1 gene. RESULTS: On fundus examination, he was found to have ill-defined foveal mottling in BE. Spectral domain optical coherence tomography (SD-OCT) showed sub-foveal hyper-reflective deposits and outer retinal layer disruption. A provisional diagnosis of the atypical variant of adult-onset foveomacular vitelliform dystrophy (AOFVD) was made on the basis of clinical, OCT, Fundus autofluorescence (FAF) and electrophysiological features. Genetic assessment of the proband revealed the presence of a homozygous base pair deletion in exon 4 of RP1L1 gene (chr8:g.10468194_10468195del), which results in frameshift and premature truncation of the protein 24 amino acids downstream to codon 1138 (p.Lys1138SerfsTer24). This variant was confirmed in the proband's parents by Sanger sequencing. The diagnosis was revised to RP1L1 maculopathy, as the RP1L1 gene variant is most commonly associated with this entity. CONCLUSION: This report presents the multimodal imaging of a previously unreported phenotype of RP1L1 maculopathy associated with a genetic variant of RP1L1 gene, thereby expanding the spectrum associated with RP1L1 maculopathy.

Optimized Attribute Selection Using Artificial Plant (AP) Algorithm with ESVM Classifier (AP-ESVM) and Improved Singular Value Decomposition (ISVD)-Based Dimensionality Reduction for Large Micro-array Biological Data.

In the tremendous field of the bioinformatics look into, enormous volume of genetic information has been produced. Higher throughput gadgets are made accessible at lower cost made the age of Big data. In a time of developing information multifaceted nature and volume and the approach of huge information, feature selection has a key task to carry out in decreasing high dimensionality in AI issues. Dealing with such huge data has turned out to be incredibly testing strategy for choosing the exact features in enormous medical databases. Large clinical data frequently comprise of an enormous number of identifiers of the disease. Data mining when applied to clinical data for identification of diseases, a few identifiers are will not be much useful and sometimes may even have negative impacts. Consequently, when the FS is applied, it is vital as it can expel those insignificant disease identifiers. It likewise builds the adequacy of decision by a physician emotionally supportive network by viably diminishing the time of learning of the framework. In this paper, a unique approach is presented for the feature selection utilizing the Artificial Plant algorithm which uses the Enhanced Support Vector Machine classifier. The features got are additionally dimensionally decreased by presenting the Improved Singular Value Decomposition strategy; finally, enhancement is done by the outstanding BAT streamlining method. The examinations are completed with real-time large cervical cancer data and it demonstrated to be more effective than the current methods.

Genotoxicity studies with an ethanolic extract of Kalanchoe pinnata leaves.

Kalanchoe pinnata is a medicinal plant, used mainly in African, Brazilian, and Indian traditional medicine for the treatment of several human disorders. Whole leaf extracts, crude juice of the leaves, and aqueous and organic extracts of the leaves are used. Over the last decade, ethanolic extracts have become the most popular form of Kalanchoe medicinal preparation. In this study, an ethanolic extract of this plant leaf was tested in a battery of standard regulatory genetic toxicology tests. This extract did not induce reverse mutations in the Salmonella/microsome assay but induces a weak genotoxic response in the mouse lymphoma assay and the in vivo micronucleus assay in mice. Our results indicate that this material may cause DNA damage, and its use should be restricted.