We suggest using edge computing to install an intrusion detection system (IDS) at the network edge in order to address the problem of intrusion detection in wireless sensor networks (WSNs). This IDS functions as a proactive defence system designed specifically with WSNs in mind. Our method combines an arithmetic optimisation algorithm (AOA) with Wireshark and machine learning to create an edge intelligence framework for identifying denial-of-service (DoS) assaults in wireless sensor networks (WSNs). We use the wireshark for optimisation adjustment and a parallel strategy to promote communication between populations in order to increase model accuracy. Our model achieves $\mathbf{9 8 . 9 \%}$ accuracy (ACC) through simulated studies utilising the IDS dataset in Matlab2018b. The effectiveness and practical significance of our suggested intrusion detection approach are highlighted by these experimental results.

Wearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality. The ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts. Our empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at − 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of − 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.

The smart city traffic management domain is perpetually a crucial sector that requires innovative strategies due to expanding urbanization and vehicle use. In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Federated Learning (FL) to deal with urban traffic effectively. This approach leverages the sophisticated functionalities of TCNs to evaluate and estimate traffic trends, such as congested phases, traffic flow, and ideal mobility routes. The system guarantees data privacy and utilizes decentralized information analysis using Federated Learning. In this approach, every point in the intelligent city network, including traffic sensors and cameras, serves to collectively comprehend traffic patterns without disclosing raw data. Using this cooperative method not only improves the model’s ability to forecast outcomes accurately but also enables efficient real-time traffic management, with the ability to adapt to changing conditions. The method has shown significant efficacy in enhancing traffic flow and mitigating congestion. The critical criteria are a 20% drop in average commuting times, a 25% drop in traffic congestion, and a 15% enhancement in emergency response times. These statistics highlight the system’s efficiency in improving urban traffic control by integrating modern technologies.

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids’ selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm’s usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines.

precise estimations of consuming load profiles are crucial for the successful oversight of electrical energy systems in both financial & environmental terms. Based on this foundation, it is feasible to strategize & execute the utilization of manageable power generation and storage systems, along with power acquisition, considering the necessary preparation as well as technological and contract limitations. The documented electrical load profiles will significantly rise throughout the digitization of the energy sector. To enhance prediction accuracy, it is essential to create and analyze models that consider the increasing data volume and aim to enhance predicting accuracy through an increased number of data. Artificial neural networks (ANN) are being more commonly employed to address not linear issues involving large datasets influenced by humans and various other unexpected factors. The artificial neural network (ANN) technique is selected to forecast load patterns. The study aims to simulate and evaluate the efficacy & optimal performance of prediction models using an Artificial Neural Network for power consumption profiles.

Search engines are essential for getting information from web sites in the internet age. They use a variety of ranking algorithms, depending on content-based information extraction techniques or statistical searching strategies, to organise the retrieved results. It is still difficult, nevertheless, to comprehend the underlying information on each web page without opening each one individually. This challenge motivates the concept and implementation of an object-attribute-value (O-A-V) information extraction system as a web model. To help with future searches, this model functions as a user dictionary by helping to filter search terms in queries. To evaluate the first model, natural language processing (NLP) and semantic web (SW) queries in the form of English phrases are employed. Similar to this, content-based picture information extraction and retrieval against user queries is used by image search engines like Google Images. Using picture descriptions, a domain ontology is constructed in order to close the semantic gap between user expectations and image retrieval outcomes. The second suggested model looks at subject-predicate-object (S-P-O) interactions by analysing natural language user queries using an NLP parser algorithm. This extraction builds upon the O-A-V web model, which is ontology-based. The article presents a SPARQL auto-query generation module to auto-generate SPARQL questions by utilising S-P-O extraction and taking into account the intricacy of creating SPARQL queries from the standpoint of a user. The auto-generated SPARQL query is used to obtain photos after these queries are placed on the ontologies. Systems for retrieving documents and images were developed and evaluated against industry standards in order to provide answers to these problems. The outcomes show improvements over baseline systems.

Utilizing IoT devices for automated signal extraction and data processing is a cornerstone of Computer-Aided Diagnostics, addressing various clinical challenges. Among these, diagnosing osteoarthritis, a critical knee joint disorder early on is paramount to prevent severe joint damage. Vibroarthrography (VAG), a novel approach, leverages sound waves produced during knee joint movement to diagnose various stages of this disorder. This article presents a computational system based on VAG signals, seamlessly integrated with IoT devices for knee joint data extraction. Employing machine learning techniques facilitates the classification of osteoarthritis levels. By offering this system as consumer electronics, it reduces costs and radiation exposure compared to traditional clinical modalities. Our implementation gathered 187 clinical data points using the proposed computational system, integrating IoT devices to capture vibrations. Analyzing the recorded data involved computing various feature sets, enabling multiple classifications of osteoarthritis levels. Evaluation based on accuracy, precision, recall, and AUC demonstrated the efficacy of our proposed binary and multiclass classification models, indicating its potential as a mechanism for collecting and analyzing data for early-stage osteoarthritis detection.

Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and interpretation, making it easier for a computer to comprehend and extract meaningful information from visual data. Adaptive K-means clustering is a variant of the classic K-means clustering algorithm in which the number of clusters (K) is continuously adjusted during the clustering process. Unlike classic K-means, which requires you to choose the number of clusters before executing the algorithm, adaptive K-means identifies the best number of clusters based on the features of the data. The proposed model works as follows. Firstly, pre-processing is performed by acquiring all the input images. Secondly, adaptive k-means clustering is employed for segmentation. Thirdly, important features are automatically extracted from X-ray images by making use of a feature-based image registration technique. Then, the detection of bone fractures is automatically carried out. The results are compared with those of existing studies, and it is observed that this model provides better results.

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples.

The Digital twins will duplicate the actual objects, create the virtual world and execute using IoT devices and Sensors. The Emergency Room Service (ERS) is a critical phase for patients in health condition evaluation, Digital Health records will help us in understanding the cause of illness, medical history will help us to start the treatment. The most challenging for ERS is anonymous person or unknown follow ups about patients. The proposed model (Emergency Service Room with Digital Twins), helps to treat a patient with fast-track service and reduce the length of Stay in ER. The risk factor of a patient's life by reviewing the medical history of the patients through digital health records. This novel method will help doctors in treating patients by Computing Image Processing in face recognition of patients. The biometric used for authentication to access the cloud for digital health records. The communication system used to acknowledge the family, Insurance Company and expert adviser. The empirical results successfully proved the novel proposed idea with above 80% of success rate. We can build an intelligent expert system to collaborate the Digital Health Record, E-H-S, Expert Adviser, and an expert system in the future. In treating Anonym patients in the Emergency department.

A wireless remote-control lock system is used for pedestrian entrances to both homes and businesses. To engage and disengage a door latch component, this locking mechanism is powered by electricity and consists of a striker plate assembly with a movable striker plate element A radio frequency receiver and a circuit that responds to signals from an operator-controlled radio emitter power the striker assembly. The striker assembly has the ability to unlock the door either temporarily or permanently. The circuit in the control unit is made up of a self-latching relay, a selection switch, and a second relay. The door will stay open for up to 3.5 seconds, or until the emitter transmits a new signal to the receiver. Because everyone is accustomed to using physical keys to lock and unlock doors, this is the most natural and prevalent method. The physical key is a well-known and dependable mechanism, but it has limitations. It is impossible to have multiple distinct keys for the same lock. Several of the locks work with multiple key types.

Seasonal Autoregressive Integrated Moving Average (SARIMA) models for dynamic cloud resource provisioning are introduced and evaluated in this work. Various cloud-based apps provided historical data to train and evaluate SARIMA models. The SARIMA(1,1,1)(0,1,1)12 model has an MAE of 0.056 and an RMSE of 0.082, indicating excellent prediction ability. This model projected resource needs better than other SARIMA settings. Sample prediction vs. real study showed close congruence between projected and observed resource consumption. MAE improved with hyperparameter adjustment, according to sensitivity analysis. Moreover, SARIMA-based resource allocation improved CPU usage by 12.5%, RAM utilization by 20%, and storage utilization by 21.4%. These data demonstrate SARIMA's ability to forecast cloud resource needs. SARIMA-based resource management might change dynamic cloud resource management systems due to cost reductions and resource usage efficiency. This research helps industry practitioners improve cloud-based service performance and cost.

Unintentional deaths occur at a very high rate in developing countries. Curved roads have significantly more fatalities than straight roads. This occurs mainly on U-turns, hairnin turns, and narrow mountain roads. Drivers in this position cannot see the vehicle approaching from the opposite direction. As a result, thousands of people are killed in car accidents every year. The best way to avoid further accidents is to alert the car driver approaching from the side. Place the ultrasonic range detection sensor on one side of the road before the bend and the light indicator system on the opposite side after the bend. When a vehicle approaches from afar, an ultrasonic sensor on one side of the road sends a signal to the other side of the road via a light system. In response to a warning, the driver may stop the car until the other vehicle has passed. A buzzer will also be used to warn the driver of the car that is approaching.

The concerned workers ensure that vehicles are parked in the appropriate spaces. Employees must repeatedly poll their coworkers via personal surveys or the company's internal phone system to ensure that everything is correct. If there is an available parking space, the driver will manually move the vehicle there, regardless of any obstacles. If there is no available space, the car must return and try again later. The proposed intelligent parking system, if implemented, would solve all parking problems. This will take less time and fuel than other options. Intelligent parking solutions will fundamentally alter automobile-centric cities. It may make parking more convenient by bringing order to the chaos. People are always concerned that finding a parking spot will take too long, particularly in densely populated cities. This study focuses on developing a new system in which residents in high-traffic areas can earn extra money by renting out their unused parking spaces to those in need. This strategy could help malls to manage parking more effectively during peak shopping hours. A customer can save time by reserving a parking space ahead of time.

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. Software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. The goal of the current research is to create methods for identifying code smells. The machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods.

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

In this modern era usage of each and every component depends on electric power. Monitoring of power consumption plays a vital role. The main objective of this paper is power monitoring system. LoRaWAN is used for long range wireless communication. A smart meter, also known as an advanced metering system, is a digital metre that is able to measure power consumption. Energy meters with an integrated Serial RS-485 Modbus interface are used that allows data to be stored in cloud. The output recorded from energy meter will be send to cloud using LoRaWAN gateway. The values like voltage, frequency, power factor, Apparent power, current, Voltage to line to line, Voltage to line to Neutral and reactive power are noted.All these values are stored in cloud and analysed using python platform. Reading from Textile industry with three floor was observed for a month. Using the data maximum power consumed day is identified. After identifying the day, the energy meter reading of each component of that particular day is monitor. That data consists of power consumed by each and every component in that particular floor. With the help of that data the component that consumes maximum power can be identified. If a component consumes more power, then there is some defect in that component, with the help of this analysis the defect in that component will be rectified.

Surgeons are always looking for technologies to make their working environment better. They frequently take the lead in implementing innovations that help their industry deliver better surgical and patient experiences. The ongoing advancement of the surgical environment in the digital era has resulted in several breakthroughs being observed as possible disruptive technologies in the surgical work place As augmented reality (AR) grows in popularity, availability, and cost, it is critical to evaluate how technology might be leveraged to better utilize medical data in healthcare. Applications in surgical anatomy, surgical processes, and surgical recovery are being researched all the time. The use of computer-generated sensory input to improve performance on a specific task is referred to as "augmented reality". A technique that combines the view of the real world with important information for doctors displayed on semi-transparent glasses attached to an augmented reality headset. For this investigation, sensors are being utilized to monitor patients in a hospital and gather data in real time. The data is then processed by a computer and delivered immediately to the augmented reality glasses used by doctors. The alarm on these spectacles notifies doctors if something is wrong. Taking the patient's current state of health into account, the physician is able to make wise decisions and carry out the necessary procedures.