This has been increasing in difficulty for accomplishing time-consuming and complex tasks with the rise in the number of Internet of Things (IoT) devices and the generated big data, along with problems of expanding concerns often in the form of latency and use of power. Fog computing is the potential solution advanced by a dispersed form of computing and has a solution to these issues. IoT-Fog applications are limited in meeting time constraints to reduce service latency and energy consumption, due to the limited processing capacity that fog computing devices have. Based on this factor, this paper now proposes an alternative approach by using Fuzzy Logic to find the solution based on Dynamic Integer Linear Programming to reduce the workload in the clinical decision support system by efficient allocation of resources to the IoT devices from the Fog compute layer while accounting for the system constraints and resource availability. The proposed task-priority scheme is integrated into the proposed clinical decision support system and reduces the latency and energy consumption in fog nodes. Experiments show that the proposed method outperforms the benchmark methods in terms of energy consumption and service delay.

The central remote servers are essential for storing and processing data for cloud computing evaluation. However, traditional systems need to improve their ability to provide technical data security solutions. Many data security challenges and complexities await technical solutions in today's fast-growing technology. These complexities will not be resolved by combining all secure encryption techniques. Quantum computing efficiently evolves composite algorithms, allowing for natural advances in cyber security, forensics, artificial intelligence, and machine learning-based complex systems. It also demonstrates solutions to many challenging problems in cloud computing security. This study proposes a user-storage-transit-server authentication process model based on secure keys data distribution and mathematical post-quantum cryptography methodology. The post-quantum cryptography mathematical algorithm is used in this study to involve the quantum computing-based distribution of security keys. It provides security scenarios and technical options for securing data in transit, storage, user, and server modes. Post-quantum cryptography has defined and included the mathematical algorithm in generating the distributed security key and the data in transit, on-storage, and on-editing. It has involved reversible computations on many different numbers by super positioning the qubits to provide quantum services and other product-based cloud-online access used to process the end-user's artificial intelligence-based hardware service components. This study will help researchers and industry experts prepare specific scenarios for synchronizing data with medicine, finance, engineering, and banking cloud servers. The proposed methodology is implemented with single-tenant, multi-tenant, and cloud-tenant-level servers and a database server. This model is designed for four enterprises with 245 users, and it employs integration parity rules that are implemented using salting techniques. The experimental scenario considers the plain text size ranging from 24 to 8248 for analyzing secure key data distribution, key generation, encryption, and decryption time variations. The key generation and encryption time variations are 2.3233 ms to 8.7277 ms at quantum-level 1 and 0.0355 ms to 1.8491 ms at quantum-level 2. The key generation and decryption time variations are 2.1533 ms to 19.4799 ms at quantum-level 1 and 0.0525 ms to 3.3513 ms at quantum-level 2.

This study introduces a self-organized genetic algorithm (GA) approach to address the challenge of optimizing clustering solutions in datasets with complex structures. The proposed method employs a unique population initialization strategy, wherein individuals represent clustering solutions with gender and age attributes. The iterative process involves mating, crossover, mutation, and selection to evolve and discover the optimal clustering. Self-organization principles are integrated into the GA, enhancing its performance by distinguishing leader individuals with high fitness values and active individuals. The leader individuals act as cluster heads, reducing intra-cluster distances and increasing inter-cluster distances. Empirical results demonstrate that the self-organized approach outperforms alternative techniques, particularly in terms of the average Davies-Bouldin index, showcasing its effectiveness in clustering tasks.

Fine particulate matter (PM2:5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM2:5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM2:5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM2:5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM2:5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models.

In wireless sensor networks (WSNs), sensors are scattered in a particular region to record and evaluate the physical data from the environment. They are widely utilised in various applications, including healthcare, event detection, agriculture, and disaster management. However, the challenges in WSNs include high bandwidth demand, energy consumption, route design, quality of service (QoS) provisioning, unusual activities, etc. However, numerous ways are being considered to solve the aforementioned difficulties. Detecting abnormal network movements is one of the most difficult challenges. An intrusion detection system (IDS) is a productive way to detect infiltration effectively. However, the growth of contemporary technologies necessitates using an effective IDS strategy. As a result, many academicians focus on reducing and eliminating anomalous network security vulnerabilities. According to the findings, machine learning techniques are more critical in addressing these issues. The accuracy of several machine learning algorithms for detecting intrusion in WSNs is investigated in this work. Furthermore, this paper examines the various datasets, performance measurements, and future directions.

Cloudbursts pose a significant threat in India, especially during the South-West Monsoon season that commences in June. India's diverse climate regions, including the northern Himalayan region, Indo-Gangetic Plain, southern peninsula, and coastal areas, experience sporadic cloudbursts, with only 31 recorded instances, mainly in Himachal Pradesh, Uttarakhand, and Jammu and Kashmir. To address the lack of comprehensive Indian cloudburst data, we've curated a dataset, incorporating meteorological factors for cloudburst prediction. This dataset encompasses variables such as Temperature, Wind Gust, Wind Gust Speed, Humidity, Monsoon patterns, Air Pressure, and Cloud Density. Our goal is to improve preparedness and mitigation strategies, safeguarding lives, and property in cloudburst-prone areas. Employing optimized machine learning algorithms, our model analyzes these parameters alongside prevailing weather conditions, facilitating cloudburst event prediction. We evaluate the prediction performance of machine learning algorithms, including Random Forest, Cat Boost, XG Boost, and Decision Tree. The Cat Boost algorithm outperformed others with an accuracy of 86.18%. Moreover, we provide graphical insights into the correlation between humidity and cloudburst occurrence, emphasizing the importance of weather variables in prediction models. This research contributes to cloudburst forecasting, even with limited Indian data, and highlights the potential of utilizing diverse machine learning techniques for improved accuracy.

The fast escalation of COVID-19 since its outbreak has affected the health care system globally. To pare the dissemination of the disease it is important to detect COVID-19 positive cases at an early stage and isolate the patients as early as possible. So, an effective and timely method for detecting the disease is required. Despite the fact that RT-PCR is the benchmark for COVID-19 detection and is very accurate, it is time-consuming. Applying deep learning along with radiography may help in diagnosing patients with COVID-19 much fast. In the paper, we propose a deep learning-based CNN architecture inspired by AlexNet which takes a three-channel image input.

Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset. Feature selection removes the irrelevant features, which improves both the accuracy and prediction performance. The significant advantages of reducing the number of features from the dataset are reducing the training time, reducing overfitting, decreasing the curse of dimensionality, and simplifying the prediction model. The filter feature selection techniques can handle the issues with the high number of features, and this paper uses the symmetric uncertainty coefficient to verify the relevance of the independent features. In this paper, a new feature selection method named as kurtosis-based feature selection has been proposed to select the relevant features which affect the air pollution. Kurtosis-based feature selection is compared with seven filter feature selection techniques on air pollution dataset and validated the performance of the proposed algorithm. It has been observed that the kurtosis-based feature selection extracts only PM2.5 as the key feature and has been compared to the accuracy of the five existing methods. The experimental results illustrate that the kurtosis-based feature selection algorithm reduces the original feature set up to 91.66\%, but the existing filter feature selection techniques reduce the feature set to only 50\%.

At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures

Abstract Clustering is considered one of the practical approaches for boosting the lifespan of the Wireless Sensor Networks (WSNs). It involves in gathering the sensor nodes into groups and elects the cluster heads (CHs) in each group. CHs gather the data from cluster members and transfer the aggregate information to Base Station (BS). However, the most significant obligation in WSN is to elect the optimal CH to enhance the network's lifespan. This paper proposes an optimal cluster head election framework in WSN. A novel hybrid technique selects the optimal CHs: an oppositional grey wolf optimization (OGWO) algorithm that collaborates with generic GWO and opposition-based learning techniques. The hybrid OGWO algorithm dynamically balances between intensification and diversification search process in electing optimal CHs. In addition, the parameters like energy, distance, node degree, and node centrality aid in selecting the optimal CHs in the network. This CHs selection framework improves the efficacy of the network capability and enhances the network lifespan. Further, the superiority of the proposed OGWO technique is validated based on the various impacts like energy, alive nodes, BS location, and several packet delivery aspects. Accordingly, the proposed OGWO technique provides a better network lifetime of ~ 20%, ~ 30% and ~ 45% compared with GWO, ABC and LEACH techniques.

Artificial intelligence has been provided powerful research attributes like data mining and clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis gives huge amount of relevant data that explains evaluation and portrayal of qualities as trending notion. A wide range of scenarios, data from many dimensions may be used to provide efficient clustering results. Multi-view clustering techniques had been outdated, however they all provide less accurate results when a single clustering of input data is applied. Numerous data groups are conceivable due to diversity of multi-dimensional data, each with its own unique set of viewpoints. When dealing multi-view labelled data, obtaining quantifiable and realistic cluster results may be challenge. This study provides unique strategy termed OCMHAMCV (Orthogonal Constrained Meta Heuristic Adaptive Multi-View Cluster). In beginning, OMF approach used to cluster similar labelled sample data into prototypes of dimensional clusters of low-dimensional data. Utilize adaptive heuristics integrate complementary data several dimensions complexity of computational analysis data representation data in appropriate orthonormality constrained viewpoint. Studies on massive data sets reveal that proposed method outperforms more traditional multi-view clustering techniques scalability and efficiency. The performance measures like accuracy 98.32%, sensitivity 93.42%, F1-score 98.53% and index score 96.02% has been attained, which was good improvement. Therefore it is proved that proposed methodology suitable for document summarization application for future scientific analysis.

In data mining, association-rules mining are a crucial technology. The Frequent-Growth (FP) algorithm is a well-known algorithm for association mining. However, FP-Growth method in mining requires two scans of the database which affects the process's efficiency. We have compared two different FP-Growth algorithms namely Painting-Growth algorithm as well as not Painting-Growth algorithm via the study of association mining and FP-Growth algorithm. With the FP-Growth algorithm, we have compared two different types of enhanced algorithms. Performance of these enhanced algorithms is absolutely better than FP-Growth algorithm, according to the experimental results. Painting-Growth algorithm has a data volume of greater than 1050 and also, N Painting-Growth algorithm has a data volume of less than 10000.

The immune system can be compromised when humans inhale excessive cooling. Physical activity helps a person’s immune system, and influenza seasonally affects immunity and respiratory tract illness when there is no physical activity during the day. Whenever people chill excessively, they become more susceptible to pathogens because they require more energy to maintain a healthy body temperature. There is no doubt that exercise improves the immune system and an individual’s fitness. According to an individual’s health history, lifestyle, and preferences, the physical activity framework also includes exercises to improve the immune system. This study developed a framework for predicting physical activity based on information about health status, preferences, calorie intake, race, and gender. Using information about comorbidities, regions, and exercise/eating habits, the proposed recommendation system recommends exercises based on the user’s preferences.

Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.

In recent times, Twitter is one of the major sources to access information. Its feature of the hashtag is something that grabs more attention from the users. One can write one’s mind and heart out on Twitter at any given minute. Due to which there is a rapid increase in the generation of irrelevant content on Twitter. Lately, a new hashtag called “#whitelivesmatter” was used as a counter for another hashtag “#blacklivesmatter”. A lot of anti-government protests and various other violent activities were conducted, recorded, and posted on Twitter with this hashtag. A lot of Kpop fans had taken over this hashtag and flooded Twitter with extremely irrelevant content. Due to which the main and important content of the protests was drowned in these irrelevant tweets, which made it extremely hard for the officials to find and reinforce the law and order. This paper aims at building a model that helps in finding the relevance of text content in the tweet and its hashtag #whitelivesmatter in specific. In this paper, supervised data analysis techniques like text classification are used to get the required output.

COVID-19 outbreak as a pandemic in the month of March 2020 announced by World Health Organization and this pandemic virus was originated from Dragon country China, it is a big pandemic virus has been attacking through out of the world and killing lakhs of people. While this pandemic has persevered to have impact on the lives of many thousands of people, various countries have depended on complete lockdown. During this pandemic circumstance, i.e., in lockdown, individuals around the globe have taken long range interpersonal communication destinations or applications to convey their emotions and discover a way to remain quiet themselves as down. In this work, fleeting supposition investigation coronavirus tweets after some time April 16 to April 13 are appeared. The extremity score of each tweet with VADER calculation in NLP is predominantly engaged in this work. The scores are isolated dependent on compound extremity esteem at last tweets are distinguished into three significant assessment classes like positive, negative and impartial and by demonstrating feeling score plot, fleeting assumptions after some time the information perception is additionally accomplished in this work. This work also focuses data visualization by showing sentiment score plot.

Due to Environmental and climate changes, these days, the whole world is facing air pollution and this problem is becoming further critical issues, which has terribly disturbed human lives as well as well-being. So, there is an immense need to search on air quality forecasting and has continuously considered as a key issue in environment safeguard. This paper covers the study associated with air pollution prediction using deep learning (DL) techniques based on remote sensing data. Major of the research work on air pollution are on the long-term forecasting of outdoor pollutants particulate matters (PM2.5 and PM10), ozone and nitrogen oxide. This paper discusses about the air pollution that causes the mortality and morbidity.

In recent years, the sentiment analysis using Twitter data is the most prevalent theme in Natural Language Processing (NLP). However, the existing sentiment analysis approaches are having lower performance and accuracy for classification due to the inadequate labeled data and failure to analyze the complex sentences. So, this research develops the novel hybrid machine learning model as Catboost Recurrent Neural Framework (CRNF) with an error pruning mechanism to analyze the Twitter data based on user opinion. Initially, the twitter-based dataset is collected that tweets based on the coronavirus COVID-19 vaccine, which are pre-processed and trained to the system. Furthermore, the proposed CRNF model classifies the sentiments as positive, negative, or neutral. Moreover, the process of sentiment analysis is done through Python and the parameters are calculated. Finally, the attained results in the performance parameters like precision, recall, accuracy and error rate are validated with existing methods.

Now at present development the entire world using vast variety of smart devices associated among sensors & handful of actuators. There is an enormous progress within the field of electronic communication; processing the data through devices and the bandwidth in internet technologies makes very easy to access and to interact with the variety of devices all over the whole world. There is a wide range research in the area of Internet of Things (IoT) along Cloud Technologies making to build incredible data which are creating from this type of heterogeneous environments and can be able to transform into a valuable knowledge with the help of data mining techniques. The knowledge that is generated will takes a crucial role in making intellectual decisions and also be a best possible resource management and services. In this paper we organized a comprehensive assessment on various data mining techniques engaged with small and large scale IoT applications to make the environment smart.

The most well-recognized fields in data mining is association rule mining. It’s been used within various applications including industry baskets, computer networks, recommendation systems and healthcare. Exploratory data analysis and data mining (DM) applications rely heavily on clustering. Cluster analysis seeks to categorize a group of patterns into groups based on their similarity. This paper aims to enhance the clustering technique of association rules over transactional datasets. At the outset the concepts behind association rules are explained followed by an overview of some of the recent research in this field. The benefits and drawbacks are addressed and a conclusion is drawn.