The modernhealthcaresystem has been linked to quicker growth as well as the capacity to transform themedical data for predicting thesignificant healthcare policy thatfacilitate timely preventive health services. Cardiovascular disease (CVD) is the key source of disability and death of developing country. In the modern global climate, identifying the Cardiovascular (CV) based on initial signs is extremely difficult. The CV riskprediction using DL method is employed in this work. Here,retinal fundus image preprocessing is the initial process, in which grey color conversion technique is utilized. Following this,optic disc is detected with the aid of Psi-Net and the proposed Fractional Chef based Optimization (FCBOA) is used in training process of Psi-Net. Afterwards, blood vessel segmentation is accomplished using the FCBOA enabled Spatial Attention U-Net (SA-UNet). Moreover, output image of segmentation and optic disc detection are fedto feature extraction. Furthermore, the texture features are extracted from input image. Moreover, these two classes of feature extracted images are applied to the CV risk prediction system, where the FCBOS-based SpinalNet (FCBOA-SpinalNet) is utilized for categorizing the image as normal or hypertensive type. The CV risk prediction is evaluated with regards to three metrics includes accuracy, sensitivity, and specificity, which offer thefinest values of 0.913, 0.917, and 0.918.

In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving techniques reduce the amount of energy consumed based on proper task scheduling approaches. The existing fuzzy-based hybrid genetic algorithm considers job length to optimize the makespan. However this fuzzy genetic encoded chromosome (fuzzy-GEC) algorithm considers both makespan and energy consumption to optimize the calculation of fitness value for assigning the task to the virtual machine (VM) by considering the characteristics of the task and VM. According to these characteristics, a fuzzy genetic rule-based encoding scheme is developed to schedule the tasks onto the virtual machines to minimize makespan and energy consumption. The effectiveness of the new technique is evaluated against FCFS and conventional genetic scheduling algorithm using Google Cloud trace workload. The results show the efficiency of the developed approach for makespan and energy consumption.

Memes have grown significance as social media has grown in popularity. Memes are challenging to categorize using conventional techniques since they are typically constructed using a combination of images and text, and their content is frequently hilarious or sarcastic. We suggest a deep learning-based method for classifying memes into a variety of categories, such as sexism, politics, and criticism. This highlights the necessity for a system that can evaluate memes automatically before they raise controversy or spread humor. Before judging the text as racist and abusive or not, it will first extract the text from the supplied image. If the language is found to be unacceptable, the third phase will further categorize the information into three categories: mildly racist and abusive, very racist and abusive, and hateful racist and abusive. The five thousand memes that made up the dataset for this study were divided into four categories: hateful racist and abusive, highly racist and abusive, slightly racist and abusive, and not racist and abusive at all. In current history, there has been an upsurge in concern over the propagation of inappropriate memes on social media. The proposed project provides a deep learning-based approach to categorizing objectionable memes using CNNs, RoBERTa, and BERT. We enhanced pre-trained algorithms using a dataset of racist and abusive and non-racist and abusive memes. Utilizing various assessment metrics, like accuracy, precision, recall, and F1-score, we assessed each model's performance.

Accelerometer-based IoT wearable sensors for PD symptom detection and assessment are discussed in this chapter. Accelerometers measure PD-related movement patterns and tremors in the IoT system. These discrete body sensors collect non-invasive, real-time data for early symptom detection and continuous monitoring. Accelerometers can track symptoms such tremors, bradykinesia, and postural instability. It also emphasizes early PD detection and how it might improve patient outcomes and lower healthcare expenditures. The integration of machine learning algorithms for data analysis further enriches the capabilities of these wearable sensors, enabling the identification of subtle changes in motor function over time. This chapter concludes that IoT-based accelerometer sensors can transform Parkinson's disease monitoring. By detecting, analyzing, and personalizing care, these sensors may enhance PD patients' lives. IoT accelerometers provide early intervention and better management of this complex neurological disorder.

In addition to the physical security of energy networks, cyber security is essential to protecting these systems as well. Cyber threats can stem from malicious hackers who have infiltrated the networks to gain unauthorized access to sensitive data, or from vulnerabilities within the systems themselves. It is increasingly important that smart grid companies invest in cyber security solutions, such as strong passwords, two-factor authentication, encryption, and regular software updates, to counteract these threats. Additionally, it is beneficial for organizations to create incident response plans that are tailored to their specific needs, and which define the chain of command and actions to take in the case of an incident. To details the usage of smart grids in various domains and places in an effective way and explains the efficient way of consuming power for smart ventilators by monitoring and providing cyber security against cyber-attacks. The distributed power from various regions is collected from the less predominant places and supplied to the smart ventilators through smart inverters.

The development of cloud technology has led to more resources being made available on demand. The recent spike in the cloud service demand requires further improvement of cloud-based data centers. As a result, effective task scheduling is necessary for cloud computing. To ensure equal load distribution to systems with increased scalability and performance, data centers must have a suitable task scheduling mechanism. An efficient task scheduling strategy tries to optimize output, decrease response time, use fewer resources, and conserve energy by matching the appropriate resources to the workload. The suggested technique employs a two-stage task scheduling approach. In the first stage, virtual machines are created by performing classification and clustering techniques based on historical task data, and in the second stage, a hybrid ant genetic algorithm is used to schedule the best VM for the task by combining the advantages of genetic algorithms with pheromone values from ant colony algorithms. The suggested approach accomplished cost-effective task scheduling with a short make-span.

Municipal solid waste is considered to eliminate the problem of dumping and spreading in rural and urban areas of developing countries. Accumulation of solid wastes in open spaces receives greater concern in solid waste management systems because it leads to environmental hazards and health issues. To build a clean environment, it is essential to construct an advanced and intelligent waste management system to handle different compositions of waste materials. The significant step of waste management is the separation of waste components, which is normally carried out by manual operation. As a result, it can generate improper disposal of waste materials, to simplify the separation process mechanically, a novel automated Dense Net- BiLSTM-based red fox (DNBiLSTM-RF) approach is proposed in this paper. The proposed solid waste classification framework is analyzed by using waste data which is gathered from the Tehran waste management organization. The input waste data is preprocessed initially to transform raw amorphous data into appropriate data structures and extract the most significant dense and latent data features. The abnormal variations in waste patterns generate outliers which are effectively removed by applying the interquartile range (IQR) filtering process. Finally, the proposed DNBiLSTM-RF classifier accurately discriminates municipal waste materials into six different categories such as wood waste, textiles, food residues, rubber, paper, and plastics. The hyperparameters of the DenseNet-BiLSTM model are fine-tuned using a red fox (RF) optimization algorithm to enhance the classification performance of the model. The effectiveness of the DNBiLSTM-RF approach is evaluated using performance indicators namely root mean square error (RMSE), mean absolute error (MAE), the ratio of RMSE to the standard deviation (SD), Nash-Sutcliffe efficiency, coefficient of determination, recall, precision, F-measure, and accuracy. The analytic result demonstrates the feasibility of the proposed DNBiLSTM-RF approach in classifying waste materials into respective categories precisely with an accurate rate of about 98.9% over other state-of-the-art approaches.

accurate diagnosis of plant diseases is essential for reversing declines in crop output. To study plant illnesses, one must examine the outward symptoms produced by the plant in question. It is also essential for long-term husbandry that plant health be monitored and complaints about diseases made. Disease auditing in plants is tedious work that requires special equipment. It takes a huge commitment of time, courage when dealing with plant illnesses, and fortitude to endure the devilishly long processing time. As a result, landing the photos of the leaves and comparing it with the data sets is how image processing is employed for the detection of plant conditions. To learn to distinguish between photos of unhealthy and healthy leaves, a group of computers is trained on datasets of both types. In conclusion, we now have a straightforward method to descry the complaint existent in crops at a massive scale by employing machine learning to train on the vast data sets available privately. To aid greenhouse growers, this report has been written.

Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

Text summarizing is the process of condensing text such that extraneous information is deleted and only the most important information is extracted and presented in the most understandable manner. Due to the exponential growth of social media data, it is now essential to assess this text in order to extract statistics, particulars and utilize it to the best benefit of several requisitions, applications and implementations. In recent years, the natural language processing and text mining communities have been more interested in the problem of automated summarizing, chiefly on the topic of opinion summarization. In society, views are essential for making decisions. A person or corporation depends on the advice and opinions of others while making choices. In this study, we provide a graph-based technique for creating summaries of repeated points of view and integrating the claims using sentiment analysis. The resultant summaries are carefully crafted to convey the text’s core and are based on abstraction.

In the competitive landscape of the job market, universities are faced with the challenge of not only providing quality education but also ensuring the successful placement of their graduates into the workforce. The use of advanced machine learning models offers a promising solution to this challenge by providing universities with the ability to efficiently analyse vast amounts of data and identify patterns that can be used to optimize their recruitment and placement strategies. In this paper, we delve into the exciting world of machine learning and explore how it can revolutionize university graduate employability. From predictive models that analyse student performance, interests, and career aspirations to identify the best-fit job opportunities for each student, to machine learning algorithms that match job candidates with suitable job openings based on their skills, experience, and qualifications, the possibilities are endless. It is essential for universities and companies to implement responsible machine learning practices and ensure that their algorithms are fair and unbiased to prevent issues of bias and discrimination. As the job market becomes more competitive and complex, universities and companies must leverage advanced technologies to remain competitive and attract top talent. By embracing machine learning and developing responsible machine learning practices, universities can optimize their recruitment and placement strategies and enhance the employability of their graduates. The use of advanced machine learning models presents an exciting opportunity for universities to optimize their recruitment and placement strategies and revolutionize university graduate employability.

The resources in cloud computing are provisioned to fulfill the application's computational requirements. The load balancer in a cloud data center assigns resources to virtual machines. However, the fault occurs when the server is under heavy load. As a result, resources must be adjusted. In this study, we are combining the support vector regression technique (SVRT) prediction model with resource scaling and migration. SVRT is used to predict future utilization of multi-attribute resources of a host. The approach is ideal for coping with nonlinear cloud resource workloads. After scaling the resources as required by the expected workload, the migration is in use to attain load balancing in the VMS. This paradigm for cloud computing infrastructures aims to improve system usage, lower costs and power consumption, and meet service level agreements (SLAs). We have evaluated the applicability of this framework using Google Cloud Trace.

In this paper, we have focused on the problem of malicious URLs. URL attacks have been on the rise in 2020, with most of the work being online based due to the pandemic there arises a greater scope of Phishing URLs etc. There have been existing systems but they are mostly paid, whereas with this project we aim to deploy a freemium add-on in a web browser, hosted on cloud with a real time dynamic classified URLs database so as to make the process more accessible and at the same time, less CPU and RAM consuming. The main highlights of our thesis have been that the accuracy measures of the two mains algorithms have been really close but there are discrepancies in the confusion matrix itself. Although these differences arise because of the time bindings and we would face such problems while deploying this project as an add-on service on a browser, a slow-fast multilayered system seems a better prospective plan to pursue in the future.

Electronic waste, also known as e-waste, refers to electrical or electronic devices that are discarded from households and workplaces. These used e-wastes are meant to be renovated, reused, recycled, or disposed of, and the processing of these wastes often causes disease and harms the environment. As a result, it is important to handle waste and collect it from the disposal site on a regular basis. Besides, in order to separate precious metals from discarded waste, it is important to identify them by category. Therefore, this article proposes a novel method known as e-waste management by exploiting the dynamic convolutional neural network (DCNN). This enhances the classification accuracy with the aid of exactly mapping the features of the images. Meanwhile, the collection of waste can be optimized in order to reduce the distance and time. The e-wastes in the smart garbage bin are frequently monitored by smartphone applications to collect the waste on time. Moreover, it also significantly reduces the training error, classification error, localization error, and validation error on the test images. The experimental depicts that the proposed method hones up the classification accuracy to the great extent.

Data mining or knowledge discovery is the way toward examining data according to substitute perspectives and summarizing it into accommodating information. This information can be then used to fabricate a pay, decreases costs, or both. Programming made with web mining as its key subject ought to permit clients to isolate information from a wide extent of assessments or centers, demand it, and sum up the affiliations perceived. Taking everything into account, information mining is the way toward discovering affiliations or models among many fields in colossal social instructive assortments. This paper effectively tracks down the rehashed bought things by clients. This proposed algorithm is having a higher running time than the existing FUP incremental algorithm. This algorithm efficiently finds the frequent items, and dynamically the items can be added. The entire history of the frequent item database was added and put into separate clusters. At last, we compare and choose the best-purchased items of the customer and also predict the past purchased items in the history. Based on the output, we can easily find the current status of the customer purchase.

This paper proposes malicious nodes detection and the link failure detection by malicious nodes in wireless body area networks (WBAN) environment. The malicious sensor node detection system is proposed using machine learning classification approach and the link failure detection is proposed using deep learning classification approach. The proposed co-active adaptive neuro fuzzy inference system classification based malicious node detection system in WBAN achieves 99.8% of packet delivery ratio (PDR) for single malicious node and also achieves 95.6% of PDR for 10 numbers of malicious nodes. The proposed system analyzed and consumes 0.5 ms of detection latency for detecting single malicious node and also consumes 1.89 ms for detecting 10 numbers of malicious nodes. The performance of the link failure detection can be analyzed using Convolutional neural network consumes 0.16 ms for detecting a single link failure in WBAN. The CNN based system achieves 91.7% of PDR and consumes 2.2 ms for detecting 10 link failures in WBAN.

In a wireless communication industry Cognitive Radio Network is the most important concern to deal with long range communications in efficient manner with futuristic remote sensing and channel allocation policies. This kind of communication principles need to tackle the attacks in wide manner. Generally, the attacks come from the intruder end with several variations but the most common attacks are network sniffing, route trapping and so on, but in the case of Cognitive Radio Network the major threat is called Primary User Impersonation (PUI). This kind of PUI attacks acts like a regular Primary User (PU) and raise a signals accordingly to make confuse the Secondary Users to come out from the communication line as well as the entire communication spectrum utilized by the Secondary User gets affected. Due to these kinds of affections the legitimate Secondary Users suffer and left from the respective channels utilized for communication. This type of attacks highly creates an impact over the Cognitive Radio Network and causes the failure over spectrum accessing. The flexible accessibility by some voluntary users maintains a constant selection of actions to determine their appropriate target, so they can differentiate between accomplishing their objectives and being compliant with their goals. The critical role is protected against obstruction and moreover mandated by vitality criteria. At the aim to develop energy saving and power gathering cooperative spectrum, factors that are central to the success of the project must be suggested in the literature as two major protocols, such as: Time Sharing (TS) and Energy Sharing (ES). An energy utilization constraint restriction is imposed on the power cells (battery) to make sure they do not charge and discharge at around the parallel period Primary User Impersonation detection in an efficient manner, in which it uses recent advancements in cooperative spectrum sensing to devise an algorithm called Energetic Cognitive Radio Network Game Planner (ECRNGP). A Game Theory logic provides an intelligent attack identification abilities to the proposed approach to identify the threat signals immediately and notify that to the respective transmitter unit to make appropriate preventive measures for that as well as this proposed approach efficiently identifies the Primary User Impersonation and provides a sufficient protection to the CRN environment as well as Secondary Users to make a perfect communication between entities without any attack oriented constraints.

The core intention of this theory is prognosis of differential renal syndromes and prediction of nephrolithiasis utilizing Artificial Neural Networks (ANN). An Artificial Neural Network (ANN) is an information processing paradigm that is propelled similar to the natural sensory organs, such as the brain, nerves that process, exchange and stores the data. This system is produced to support the medical specialist in their determination by enabling them to less utilization of time in recognizing small imperfections in the Renal system. It is fundamental to remind that this product does not affirm to supplant the physician, as expert evaluation assessment is regularly essential, particularly in new cases. The key component of this proposed pattern is the new structure of the information processing system. Neural networks, because of its striking capacity to obtain unpredictable or uncertain information, is applied to extract patterns and perceive trends that, are too hard to handle to the human beings or other computer techniques [Kevin J Am Soc Nephrol, 2019]. The developed and trained neural network is considered as an "expert" in the class of information that is provided to analyze. This expert system then can be used to afford projections given new patterns. Neural networks are widely used in the areas like character recognition, image compression, stock market prediction, travelling sale's man problem; security based miscellaneous and medical applications. In other words the conventional technique for the human subjective assessment strategy is being substituted by computer based systems utilizing soft computing approaches [Am Soc Nephrol, 2019]. Presently neural networks are widely utilized as a part of medical fields so that the proposed system is designed, constructed and developed for diagnosis of differential renal syndromes that acts as most excellent support for the therapeutic professionals [Xie Kidney Dis, 2020][Dayyal Bioscience, 2018]. As there is brisk increase in population the need of more nephrologists is vital. To reach the demand the proposed system is developed to support the medical practitioners [Shahid PLOS ONE, 2019]. The proposed system not only supports the medical practitioners in giving accurate results but also reduces the time and cost.

Among many type of cancers in human body especially for woman in this modern world, the breast cancer is a key problem for their physical fitness. The early diagnosing of cancer for middle aged women in a major issue among us. In breast cancer detection using the images based on mammography, it's very difficult to detect exact cancer affected area due to the properties of the image. The Mammogram images occupy large size of file due to its high-resolution of graphic images. One can identify the cancer cells and the normal cells with contrast value, but the contrast difference is very thin. To identify and symbolize metastatic tumors in early stages and perform surveillance for most cancers recurrence, the volume of tumor can be measured by the use of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) and useful characterization through Positron Emission Tomography (PET) imaging. In this article, breast cancer images are taken from medical repository and cancer affected regions are detected by region of interest and the cancer affected area is classified by the Susan technique and the results are tabulated. SUSAN corner detector is a good method which yield high quality features. To use in a real-time application, this is computationally demanding. In short amount of time, this can be used to build a feature detector. This Susan corner detector outperforms current feature detectors by a large margin.