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Recently, light fidelity (Li-Fi) technology becomes important for rapid data transmission in the network. Integration of different communication technologies can be used to address the traffic limitations posed by increasing number of Internet users. Hybrid Wi-Fi/Li-Fi networks (HLWN) make use of rapid Li-Fi data transmission and Wi-Fi coverage So, the combination of them can resolve the limitations of both technologies and enhance the efficiency of the overall network. At the same time, the choice of access point (AP) remains a difficult task as the coverage regions of various networks overlapped with one another. Based on instant channel data, the traditional load balancing schemes allocate every client to a certain AP, offering the optimum solutions at a time instance. But it can results in several handoffs for clients and degrade the throughput performance. Therefore, this article develops a Mobility aware LB using Kho–Kho optimization algorithm (MALB-KKOA) for HLWN. The presented MALB-KKOA technique is investigated in two aspects such as It is permissible to have both several AP associations (MA) and a single AP association (SA) (MA). In the SA phase, every client is aided by an AP either Li-Fi or Wi-Fi where the MALB-KKOA model allocates every client to a particular network over a time duration to reduce handover. On the other hand, in the MA phase, every client is concurrently assisted by Li-Fi as well as Wi-Fi, and no vertical handover takes place. In addition, the MALB-KKOA model is mainly based on the approaches utilized by the players in a tag-team game named Kho–Kho. Moreover, the MALB-KKOA model computes an objective function for the minimization of packet loss and delay. The experimental validation of the MALB-KKOA model is tested and the results signified its promising performance over other recent approaches. [ABSTRACT FROM AUTHOR]

Fingerprint recognition has emerged as one of the most reliable biometric authentication methods, owing to its uniqueness and permanence. However, the security and confidentiality of the user's data are key considerations in modern biometric systems. In this study, we describe an intelligent computational technique for automatically validating fingerprints for identification and verification purposes. The feature vector is created by fusing Gabor filtering features with deep learning techniques like the faster region-based convolutional neural network (Faster R-CNN). This study uses linear and decision-based median filtering (DBMF) techniques to minimize visual impulse noise. Faster-R-CNN with DBMF was applied to the feature vectors to reduce overfitting problems while improving classification precision and reliability. For fingerprint matching, the Euclidean distance between the associated Harris-SURF feature vectors of two feature points is used to measure feature-matching similarity between two fingerprint images. Furthermore, for fine-tuned matching an iterative technique known as RANSAC (Random Sample Consensus) is used. The experimental results collected from the public-domain fingerprint databases FVC-2002 DB1 and FVC-2000 DB1 show that the proposed design is viable and performs well with an accuracy of 99.43%, MSE value of 43.321%, and an execution time of 3.102 ms which was more exact than existing models. • Reduces image impulse noise using decision-based median and linear filtering. • Feature vectors were processed with Faster-R-CNN and DBMF to alleviate overfitting. • Optimizes the matching procedure and automatically eliminates incorrect matches. • Experimentation with publicly available datasets of FVC2002 DB1 and FVC2000 DB1. [ABSTRACT FROM AUTHOR]

The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance. [ABSTRACT FROM AUTHOR]

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of '5' phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users' tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method. [ABSTRACT FROM AUTHOR]

One of the uncrewed aerial vehicles (UAV) in a Flying Ad-hoc network (FANET) can link directly to the infrastructure. At the same time, the other UAVs in the system may have a multi-hop connection in which each node works as a relay and a data collection node. We may not have the support of traditional infrastructure-based networks when natural disasters such as floods or earthquakes strike. This is fatal because trapped people are challenging to find by search and rescue personnel. In such cases, an airborne network of small drones is valuable for giving quick and adequate coverage of the affected area and instant insights to rescue workers. At the same time, such networks face various challenges, and ongoing research and development show promise in making such technology more dependable and effective. This paper presents Multidimensional Perception and Energy Awareness Optimized Link State Routing (MPEAOLSR) for Flying Ad-hoc networks, which is based on Reinforcement Learning (RL). The protocol is a mobile wireless LAN-specific version of the traditional link state algorithm. The protocol largely relies on the idea of multipoint relays (MPRs). During the flooding process, MPRs are chosen to forward broadcast messages. This technique considerably minimizes message overhead associated to a standard flooding system in which each node retransmits each message after receiving the first copy. In RL-MPEAOLSR, only nodes designated as MPRs generate link state information. Furthermore, the RL-MPEAOLSR node can opt to report just links between itself and its MPR selectors, decreasing the number of control messages flooding the network. Because MPRs function well in large and dense networks, the MPEAOLSR protocol is suited for them. The proposed approach outperforms the conventional Energy Awareness Optimised Link State Routing for Flying Ad-Hoc Networks, according to the results. The technique far outperforms current methods in terms of modern bandwidth consumption of 1478.04kpbs, network density of 95.64%, packet delivery ratio of 95.85%, packet loss ratio of 31.94%, delay in transmission time of 6.981 s and accuracy 6.981%. [ABSTRACT FROM AUTHOR]

The efficient detection of nutrient deficiency and proper fertilizer for that deficiency becomes the critical challenges various farmers face. The family Cucurbitaceae includes members cultivated globally as a source of indigenous medicines, food, and fiber. Luffa acutangula (L.) Roxb, generally called Ridge gourd, belongs to the Cucurbitaceae family and is an annual herb originating in several areas of India, particularly in the coastal regions. Nutrient deficiency detection in ridge gourd is essential to improve crop productivity. In agricultural practises, the early identification and categorization of nutrient deficiencies in crops is essential for sustaining optimal growth and production. Addressing these nutrient deficiencies, we applied the Ring Toss Game Optimization with a Deep Transfer Learning-based Nutrient Deficiency Classification (RTGODTL-NDC) to Ridge Gourd (Luffa acutangula). This research proposes a new ring toss game optimization with a deep transfer learning-based nutrient deficiency classification (RTGODTL-NDC) method. The RTGODTL-NDC technique uses pre-processing, segmentation, feature extraction, hyperparameter tuning, and classification. The Gabor filter (GF) is mainly used for image pre-processing, and the Adam optimizer with SqueezeNet model is utilized for feature extraction. Finally, the RTGO algorithm with the deep hybrid learning (HDL) model is applied to classify nutrient deficiencies. The suggested framework has the potential to improve crop management practises by allowing for proactive and targeted interventions, which will result in improved agricultural health, production, and resource utilisation. The outcomes represented by the RTGODTL-NDC method have resulted in improved performance. For example, based on accuracy and specificity, the RTGODTL-NDC methodology rendered maximum a c c u y of 97.16% and specificity of 98.29%. The outcomes show how effective the transfer learning-based model is in identifying nutrient deficits in ridge gourd plants, as seen by its high level of accuracy. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Rapid industrialization has led to the generation of a considerable amount of waste, both solid and liquid, in industrial fields like food processing, sugar, pulp, sago or starch, dairies, paper, fruit processing, poultry, distilleries, slaughterhouses, tanneries, and so forth. Despite the requirement for pollution control measures, the waste is discharged into water bodies or generally dumped on land without appropriate management, and thus becomes a significant source of environmental pollution and health hazards. The most essential step of waste management is the segregation of waste into the various elements, and normally this process is done automatically by hand-picking. A smart waste material classification technique is required to simplify the procedures. Therefore, the study presents a new Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) method. The presented MDTLDC-IWM model facilitates the use of DL models for the identification and classification of waste materials in the IWM system. To accomplish this, the presented MDTLDC-IWM model follows two key phases, namely waste object recognition and waste object classification. At the initial stage, the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) algorithm is used. Next, in the second stage, the stacked sparse auto encoder (SSAE) model is applied for the waste object classification method. The SSAE model is effectively optimized using the Aquila Optimization Algorithm (AOA), which helps to accomplish maximum classification of waste objects. The MDTLDC-IWM model has achieved a precision of 96.84 percent and an F score of 96.71 percent. A benchmark dataset is used to test the experimental validity of the presented MDTLDC-IWM model. Extensive comparative analysis reported the enhanced performance of the MDTLDC-IWM model over recent state-of-the-art approaches. [Display omitted] • This study introduces a novel Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) approach. • The described MDTLDC-IWM model supports the employment of DL models in the IWM system for the identification and classification of waste products. Since the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) method is employed. • The stacked sparse auto encoder (SSAE) model is utilised for the classification of waste objects. [ABSTRACT FROM AUTHOR]

Chitosan is modified by phthaloylation using an excess of phthalic anhydride at 130 °C and blended with the sulfonated polyethersulfone (SPES) to produce composite blend membranes. In particular the introduction of the phthaloyl group into the chitosan matrix increases its solubility in organic solvent, film formability, flexibility, low methanol permeability and with suitable ion conductivity. SPES and N-phthaloyl chitosan (NPHCs) blend membranes with various compositions were prepared and detailed investigation on water uptake, proton conductivity and methanol permeability has been conducted for its suitability in fuel cell environments. Methanol permeability studies envisaged that NPHCs blend membranes are impervious to methanol. The thermograms display the good thermal stabilities of blend membranes than Nafion-117. Relatively high selectivity parameter values of these membranes indicated their greater advantages over Nafion-117 membrane for targeting on fuel cell applications, especially in direct methanol fuel cell (DMFC) environments. [ABSTRACT FROM AUTHOR]

Hybrid membranes based on sulfonated poly(1,4-phenylene ether ether sulfone) (SPEES)/tungstophosphoric acid (TPA) were prepared. SPEES/TPA membrane surfaces were modified with polypyrrole (Ppy) by in situ polymerization method to reduce the TPA leaching. The morphology and electrochemical property of the surface coated membranes were studied by SEM, AFM, water uptake, ion exchange capacity, proton conductivity, methanol permeability and tensile strength. The water uptake and the swelling ratio of the surface coated membranes decreased with increasing the Ppy layer. The surface roughness of the hybrid membrane was decreased with an increase in Ppy layer on the membrane surface. The methanol permeability of SPEES/TPA-Ppy4 hybrid membrane was significantly suppressed and found to be 2.1 × 10 −7 cm 2 s −1 , which is 1.9 times lower than pristine SPEES membrane. The SPEES/TPA-Ppy4 membrane exhibits highest relative selectivity (2.86 × 10 4 S cm −3 s) than the other membrane with low TPA leaching. The tensile strength of hybrid membranes was improved with the introduction of Ppy layer. Combining their lower swelling ratio, high thermal stability and selectivity, SPEES/TPA-Ppy4 membranes could be a promising material as PEM for DMFC applications. [ABSTRACT FROM AUTHOR]

• Fuzzy logic-based energy management based new traffic flow model was applied in the cyber physical system. • This research focuses on the construction of a fuzzy logic-based energy management and TFP model (FLEM-TFP) for CPS in ITS with this rationale. • An adaptive neuro fuzzy inference system (ANFIS) model is used to calculate the engine torque necessary depending on various measurements. • The FLEM-TFP technique combines two distinct processes: optimal ANFIS-based energy management and a TFP model based on SFO. • The SFO model is created by training fuzzy wavelet neural network (FWNN) with the SFO algorithm to predict traffic flow in an ITS environment. Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures. [Display omitted] [ABSTRACT FROM AUTHOR]

Polymer composite membranes based on sulfonated poly(phenylene ether ether sulfone) (SPEES) membrane with varying concentrations of poly(ether imide) (PEI) were prepared. The sulfonation of PEES was carried out with concentrated sulfuric acid at 10 °C, and the sulfonation degree was maintained as 70 %. The introduction of PEI into the hydrophilic SPEES matrix provided good chemical stability to the membrane. The water uptake, ion exchange capacity, and proton conductivity decrease with increasing PEI content. Scanning electron microscopy (SEM) indicates that PEI particles are homogeneously dispersed into the composite membrane. Thermogravimetric analysis (TGA) showed that all the composite membranes exhibited good thermal stability. On the other hand, the methanol permeability of the composite membranes gradually decreases from 4.23 × 10 cm s to 8.84 × 10 cm s, which is lower than that of the Nafion 117 membrane. The relative selectivity of the PEI-incorporated SPEES membranes was higher than that of the Nafion membranes. From the observed results, the prepared SPEES/PEI-15 composite membrane can be considered as apposite polymer electrolyte membranes for the application of direct methanol fuel cells (DMFCs). [ABSTRACT FROM AUTHOR]

Blend membranes comprising sulfonated poly (phenylene ether ether sulfone) (SPEES) - charged surface modifying macromolecules (cSMMs) were fabricated as an alternative proton exchange membrane (PEM) for H 2 /O 2 fuel cell applications. Prepared membranes were characterized by determining the ion exchange capacity, water uptake, proton conductivity, oxidative stability and dimensional stability. The water uptake of SPEES/cSMM blended membrane was found to be higher than that of the pristine SPEES and the Nafion 117 membranes. Proton conductivity of the blend membranes is in the range of 10 −3 to 10 −2 S/cm. The conductivity of the prepared membranes increases with temperature, in particular, the poly (propylene glycol) – hydroxy benzene sulfonate (PPG-HBS) blended SPEES membrane shows rise in conductivities from 1.61 × 10 −2 S/cm (25 °C) to 5.22 × 10 −2 S/cm (80 °C). Surface morphology of the membranes was investigated by tapping mode atomic force microscopy (AFM), which indicates that the nodule size and surface roughness are increased by the incorporation of cSMM into the SPEES matrix. Surface modified blended membranes exhibited excellent thermal stability and acceptable dimension stability in 80 °C, which implies that the SPEES/cSMM blended membranes are promising materials for PEMFC application. [ABSTRACT FROM AUTHOR]

Surface modification of sulfonated poly(vinylidenefluoride-co-hexafluoropropylene) (sPVdF-co-HFP) membrane was attempted by blending with differently charged surface modifying macromolecules (cSMMs) with different polyols. The prepared membranes were characterized using Fourier transform infrared spectroscopy, thermal analysis, mechanical properties, ion exchange capacity, atomic force microscopy, contact angle, and water uptake. The ionic conductivities of the prepared membranes are in the order of 10 −3 S cm −1 . The proton conductivity was found to be dependent upon the water uptake of the membranes. Among the modified membranes, the sPVdF-co-HFP/cSMM-1 (2 × 10 −7 cm 2 s −1 ) blended membrane shows lower methanol permeability whereas the sPVdF-co-HFP/cSMM-4 (5 × 10 − 3 S cm −1 ) blended membrane indicates the highest proton conductivity. However, the sPVdF-co-HFP/cSMM-2 (21.8 × 10 3 S cm −3 s) blended membrane exhibits the highest overall membrane characteristic value. These characteristics make the prepared membranes a promising electrolyte for direct methanol fuel cell application. [ABSTRACT FROM AUTHOR]

Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species' obligate brood parasitism (laying eggs in other species' nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior. [ABSTRACT FROM AUTHOR]

• Decision Tree, SVM, and Naive Bayes classification devices were used by Deepti and Dilip to diagnose diabetes. • The goal was to classify large amounts of the separator. PIMA Indian database was used. • Naive Bayes, scored 76.30 per cent, received the best accuracy. Six separators were used. • The LIBSVM wallet F select script selects four characteristics and selects 9 and 20 characteristics. • The sugar prediction can be more accurate with 72 percent precision. Diabetes mellitus is a disease commonly called Diabetes. Diabetes is among the most frequent diseases globally. This disease affects internationally with different ailments and complications in majority of peoples. Diabetes is a chronic disease with the ability to create a global medical care crisis. In compliance with International Diabetes Federation 382 million people are reportedly living with diabetes throughout the entire world. The diabetes disorder can't be cured but it can control, diagnosis and prediction of diabetes is vital to restrain the death rate as a result of its seriousness globally. Many In recent years, machine learning (ML) algorithms have been used in the prediction of diabetes. A clever predictive model utilizing deep modelling is commonly advised with the aid of conditional data collection to forecast the severity and particular risk factor of diabetics. In this article, to resolve this problem we employed the Interpretable Filter based Convolutional Neural Network (IF-CNN) prediction model and Pet Dog-Smell Sensing (PD-SS) algorithm that can automatically predict the diabetes from PIMA Indian diabetes datasets. This may enhance the general strategy of disease prediction in patients database that may solve the issues faced by traditional algorithms employing the Deep Neural Network (DNN) methods. The automated extraction, selection and classification of attributes, disease forecast is the hard task with aggressive performance for your PIMA information which may be implemented with the projected Deep Learning version economically. This may enhance the general plan of disease prediction from patients database that may solve the issues faced by traditional algorithms employing the Interpretable Filter established Deep Learning version prediction model to diagnose and predict the diabetes disease in multi-level databases. The intention of this research is to create a method that could carry out early diabetes predictions for a more reliable patient by including findings of SVM and CNN-LSTM(Long Short-Term Memory) machine learning methods also IF-CNN achieved 96.26% accuracy. [ABSTRACT FROM AUTHOR]

One of the deadliest diseases in the world is brain cancer. Children and adults are also susceptible to this malignancy. It also has the poorest rate of survival and comes in a variety of shapes, textures, and sizes, depending on where it is found. Bad things will happen if the tumour brain is misclassified. As a reason, early detection of the right kind and grade of tumour is critical in determining the best treatment strategy. Brain tumours may be identified by looking at magnetic resonance imaging (MRI) pictures of the patient's brain. The manual method becomes time-consuming and may lead to human mistakes due to the huge quantities of data and the different kinds of brain tumours. As a result, a computer-assisted diagnostic (CAD) system is needed. Image categorization methods have advanced significantly in recent years, particularly deep learning networks, which have achieved success in this field. In this case, we used a multilayer stacked probabilistic belief network to accurately classify brain tumors. Here the MRI brain images are Pre-processed using the Hybrid Butter worth Anisotropic filter and contrast Blow up Histogram Equalization. Followed by pre-processing, the denoised image can be segmented by using the bounding box U-NET segmentation methodology. Then after segmenting the target, the specialized features regarding the tumor can be extracted using the In-depth atom embedding method. Then they obtained can reduce feature dimensionality by using the Backward feature eliminating green wing optimization. The extracted features can be given as input for the classification process. A Multilayer stacked probabilistic belief network is then used to classify the tumour as malignant or benign. The suggested system's efficacy was tested on the BraTS dataset, which yielded a high level of accuracy. Subjective comparison study is also performed out among the suggested technique and certain state-of-the-art methods, according to the work presented. Experiments show that the proposed system outperforms current methods in terms of assisting radiologists in identifying the size, shape, and location of tumors in the human brain. [ABSTRACT FROM AUTHOR]

The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data. Code-mixed data is framed by combining words and phrases from two or more distinct languages in a single text. It is quite challenging to identify emotion or offensive terms in the comments since noise exists in code-mixed data. The majority of advancements in hostile language detection and sentiment analysis are made on monolingual data for languages with high resource requirements. The proposed system attempts to perform both sentiment analysis and offensive language identification for low resource code-mixed data in Tamil and English using machine learning, deep learning and pre-trained models like BERT, RoBERTa and adapter-BERT. The dataset utilized for this research work is taken from a shared task on Multi task learning DravidianLangTech@ACL2022. Another challenge addressed by this work is the extraction of semantically meaningful information from code-mixed data using word embedding. The result represents an adapter-BERT model gives a better accuracy of 65% for sentiment analysis and 79% for offensive language identification when compared with other trained models. [ABSTRACT FROM AUTHOR]

Recently, computer vision-based face image analysis has sparked considerable interest in a variety of applications such as surveillance, security, biometrics and so on. The goal of the facial analysis was to derive facial soft biometrics such as identification, gender, age, ethnicity, expression and so on. Among these, ethnicity recognition remains a hot study topic, a major aspect of society with profound linkages to a variety of environmental and social concerns. The introduction of machine learning (ML) and deep learning (ML) technologies has proven advantageous for effective ethnicity recognition and classification. In this regard, the IDL-ERCFI technique, which is based on intelligent DL, is designed in this paper. The IDL-ERCFI technique's purpose is to distinguish and classify ethnicity based on facial photos. The IDL-ERCFI technique uses face landmarks to align photos before sending them to the network. Furthermore, the proposed model employs an Exception network as a feature extractor. Because the retrieved features are high-dimensional, the feature reduction procedure employs the principal component analysis (PCA) technique, which is effective in overcoming the "curse of dimensionality." Furthermore, the ethnicity classification procedure is carried out using an optimal kernel extreme learning machine (KELM), with parameter tuning of the KELM model carried out using the glow worm swarm optimization (GSO) technique. A complete experimental analysis is carried out to demonstrate the superiority of the IDL-ERCFI technique over the other techniques. • The IDL-ERCFI technique's purpose is to distinguish and classify ethnicity based on facial photos. • The IDL-ERCFI technique uses face landmarks to align photos before sending them to the network. • The feature retrieval, the feature reduction procedure employs the principal component analysis (PCA) model. • The ethnicity classification procedure is carried out using KELM, with a parameter tuning process. • KELM model carried out experimental analysis with the glow worm swarm optimization (GSO) technique. [ABSTRACT FROM AUTHOR]

Latest advancements in fiber optic communication have gained significant attention among researchers owing to many benefits such as high data rate, acceptable cost, bandwidth, low attenuation, etc. Fiber optic networks are found to be a commonly utilized platform to transfer data in several applications such as personal, commercial, military areas, etc. Although fiber optic networks are highly beneficial, security remains a challenging design issue. Numerous state of art works has been developed to achieve security in fiber optic communication. Among the various methods, compression then encryption is an effective way to effectively and securely transmit the data. With this motivation, this paper presents a new Low Complexity Compression Then Encryption using Optimal Homomorphic Encryption (LCCE-OHE) technique for secured fiber optic communication. The proposed LCCE-OHE technique operates on two major phases namely compression and encryption. At the first stage, low complexity compression using Neighboring Indexing Sequence (NIS) with Deflate algorithm, named Normalized Information Distance (NID) is used. Besides, in the second stage, Quasi Oppositional Sail Fish Optimizer with Homomorphic Encryption (QOSFO-HE) technique is employed. The QOSFO algorithm is derived by incorporating the quasi oppositional learning (QOBL) concept to the SFO algorithm and is applied to optimally select the encryption keys. The performance validation of the proposed model takes place on two benchmark datasets and the experimental results are examined interms of different performance measures. The experimental values highlighted the improved compression efficiency and security level of the LCCE-OHE technique over the other techniques. [ABSTRACT FROM AUTHOR]

In this study, polyetherimide (PEI) ultrafiltration (UF) membranes were fabricated by incorporating different amounts of hydrophilic polyvinylpyrrolidone (PVP) via the phase inversion technique. The PEI/PVP blended membrane showed the highest pure water flux (147.1 L m-2 h-1), water content (70.2%), and surface energy (112.1 mN/m) when the 8 wt % of PVP was blended into the base polymer PEI. This particular membrane, designated as PVP 8, also showed the lowest contact angle (56.1°), hydraulic resistance (2.2 kPa/(L m-2 h-1)), and shrinkage ratio (4.3%). The addition of PVP has reduced the tensile strength of the PEI membrane because of the decrease in rigidity. The UF experiments with the following proteins, bovine serum albumin (BSA), egg albumin (EA), pepsin, and trypsin, had experienced a decrease in protein rejection whereas permeate flux increased by increasing PVP loading. The BSA adsorption experiment revealed that the adsorbed amount of BSA on the surface of the membrane was notably decreased from 0.17 mg cm-2 of neat PEI membrane to 0.08 mg cm-2 of PVP 8. The flux recovery ratio (FRR) experiment also showed that the fouling resistance property of the membrane was enhanced with the increment of PVP. [ABSTRACT FROM AUTHOR]

N -phthaloyl chitosan (NPHCs), which could be dissolved in various organic solvents, is synthesized for the modification of poly (ether imide) (PEI) ultrafiltration membrane. Blend membrane with 2 wt% NPHCs exhibited higher pure water flux (112.2 l m −2 h −1 ), higher water content (63.4%) and lower hydraulic resistance (3 kPa/l m −2 h −1 ). The top surface morphology of the control PEI membrane changed from a dense surface to visible pores with the increase in NPHCs concentration. The surface roughness of PEI membranes increased with an increase in NPHCs concentration in the casting solution. Application studies were carried out to find the rejection and permeate flux of proteins such as bovine serum albumin (BSA), egg albumin (EA), pepsin and trypsin and toxic heavy metal ions such as Cr(III), Zn(II), Cd(II) and Pb(II). The result shows that the flux and separation performances are dependent upon the content of NPHCs. Furthermore, the blend membranes were subjected to the determination of pore statistics and MWCO. It was found that the blending of NPHCs into the PEI membrane had a visible effect upon MWCO and pore size. The significant effect of hydrophilicity of NPHCs on the fouling of PEI/NPHCs blend membranes by BSA was also discussed. It was found that the blend membranes with 2 wt% NPHCs content had a higher FRR (88.6%), higher reversible fouling (23.7%) and lower irreversible fouling (11.4%) which explained their improved antifouling properties. Thus, the modified chitosan proved to play an important role in the improvement of UF membrane performance. [ABSTRACT FROM AUTHOR]

The charged surface modifying macromolecule (cSMM) was blended into the casting solution of poly(ether imide) (PEI) to prepare surface modified ultrafiltration membranes by phase inversion technique. The separation of proteins including bovine serum albumin, egg albumin, pepsin and trypsin was investigated by the fabricated membranes. On increasing cSMM content, solute rejection decreases whereas membrane flux increases. The pore size and surface porosity of the 5 wt% cSMM blend PEI membranes increases to 41.4 Å and 14.8%, respectively. Similarly, the molecular weight cut-off of the membranes ranged from 20 to 45 kDa, depending on the various compositions of the prepared membranes. The toxic heavy metal ions Cu(II), Cr(III), Zn(II) and Pb(II) from aqueous solutions were subjected to rejection by the prepared blended membrane with various concentration of polyethyleneimine (PETIM) as water soluble polymeric ligand. It was found that the rejection behavior of metal ion depends on the PETIM concentration and the stability complexation of metal ion with ligand. [ABSTRACT FROM AUTHOR]

Due to the advancements in 5 G technologies, high-definition, and the internet of things (IoT), the capacity demand of optical networks has been exponentially increased. Optical communication networks offer several metrics such as high transmission capacity, low transmission loss, better anti-interference, robustness, etc which offers new opportunities to the communication field. To satisfy the increasing demands of optical networks, effective network resource utilization become essential. So, it is needed to design proper planning tools with superior accuracy for quality of transmission (QoT) in optical networks. Recently, artificial intelligence (AI) techniques pose new opportunities for resolving these issues and machine learning (ML) algorithms offer better performance over the analytical approaches. With this motivation, this paper presents a novel AI based cognitive QoT prediction (AI-CQoT) model for optical communication networks. The proposed AI-CQoT model aims to predict the QoT for the quality of service (QoS) link setup using AI techniques with the transmission equation based synthetic data generation. The proposed model uses the Label weighting extreme learning machine (LW-ELM) model for the prediction process which includes a link and signal characteristics as input features. Besides, the LW-ELM model is trained by the use of transmission equations. For improving the predictive performance of the LW-ELM model, the parameters such as weight matrix W and penalty factor C are optimally tuned by the use of the shuffled shepherd optimization (SSO) algorithm. A detailed experimental validation is performed to highlight the improved performance of the AI-CQoT model and the results are investigated in terms of different performance measures. [ABSTRACT FROM AUTHOR]

In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH 3 , CO, NO 2 , CH 4 , CO 2 , PM 2.5 , temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques. • An IoT enabled ETAPM-AIT is designed to improve human health. • The proposed model aims to report the status of air quality in real time. • For the classification, AAA based ENN model is used as a classifier. • The experimental outcome highlights the optimal performance of ETAPM-AIT over recent techniques. [ABSTRACT FROM AUTHOR]