An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

A Soft Computing View for the Scientific Categorization of Vegetable Supply Chain Issues

Over the most recent couple of years, the Internet of Things and other empowering innovations have been logically utilized for digitizing the vegetable supply chain (VSC). Background: The unpredictable examples and complexity inserted in enormous data dimensions present a test for an orderly human master examination. Hence in an information-driven setting, soft computing (SC) has accomplished critical energy to investigate, mine, and concentrate confidential information data, or tackle complex improvement issues, finding some harmony between good productivity and maintainability of vegetable supply frameworks. Methods: This paper presents a new and diverse scientific classification of VSC issues from the SC methodology. It characterizes VSC issues and sorts them in light of how they be demonstrated according to the SC perspective. Moreover, we examine the SC methodologies commonly utilized in each phase of the VSC and their related classes of issues. Accordingly, there is an issue in distinguishing and characterizing VSC issues according to a more extensive point of view, enveloping the different SC strategies that can apply in various phases (from creation to retailing), and recognizing the issues that emerge in these phases according to the SC viewpoint. Results: We likewise acquaint some rules with the assistance of VSC analysts and specialists to settle on appropriate strategies while resolving specific issues they could experience. Even though a few latest examinations have arranged the SC writing in this field, they are situated towards a solitary group of SC strategies (a gathering of techniques that share standard qualities) and survey their application in VSC phases. Conclusions: We have suggested a novel approach and complete scientific classification of vegetable supply chain concerns about soft computing. We present a view of three delegate supply chains: cruciferous vegetables, dark green leafy vegetables, and tomatoes. We assembled the scientific type in light of different parts to arrange vegetable supply chain issues as per how they can be demonstrated utilizing soft computing methodologies

A Soft Computing View for the Scientific Categorization of Vegetable Supply Chain Issues

Over the most recent couple of years, the Internet of Things and other empowering innovations have been logically utilized for digitizing the vegetable supply chain (VSC). Background: The unpredictable examples and complexity inserted in enormous data dimensions present a test for an orderly human master examination. Hence in an information-driven setting, soft computing (SC) has accomplished critical energy to investigate, mine, and concentrate confidential information data, or tackle complex improvement issues, finding some harmony between good productivity and maintainability of vegetable supply frameworks. Methods: This paper presents a new and diverse scientific classification of VSC issues from the SC methodology. It characterizes VSC issues and sorts them in light of how they be demonstrated according to the SC perspective. Moreover, we examine the SC methodologies commonly utilized in each phase of the VSC and their related classes of issues. Accordingly, there is an issue in distinguishing and characterizing VSC issues according to a more extensive point of view, enveloping the different SC strategies that can apply in various phases (from creation to retailing), and recognizing the issues that emerge in these phases according to the SC viewpoint. Results: We likewise acquaint some rules with the assistance of VSC analysts and specialists to settle on appropriate strategies while resolving specific issues they could experience. Even though a few latest examinations have arranged the SC writing in this field, they are situated towards a solitary group of SC strategies (a gathering of techniques that share standard qualities) and survey their application in VSC phases. Conclusions: We have suggested a novel approach and complete scientific classification of vegetable supply chain concerns about soft computing. We present a view of three delegate supply chains: cruciferous vegetables, dark green leafy vegetables, and tomatoes. We assembled the scientific type in light of different parts to arrange vegetable supply chain issues as per how they can be demonstrated utilizing soft computing methodologies

Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that shortens the average life span of patients. Patients with misdiagnosed and insufficient medical treatment of BTs have less chance of survival. For tumor analysis, magnetic resonance imaging (MRI) is often utilized. However, due to the vast data produced by MRI, manual segmentation in a reasonable period of time is difficult, which limits the application of standard criteria in clinical practice. So, efficient and automated segmentation techniques are required. The accurate early detection and segmentation of BTs is a difficult and challenging task in biomedical imaging. Automated segmentation is an issue because of the considerable temporal and anatomical variability of brain tumors. Early detection and treatment are therefore essential. To detect brain cancers or tumors, different classical machine learning (ML) algorithms have been utilized. However, the main difficulty with these models is the manually extracted features. This research provides a deep hybrid learning (DeepTumorNetwork) model of binary BTs classification and overcomes the above-mentioned problems. The proposed method hybrid GoogLeNet architecture with a CNN model by eliminating the 5 layers of GoogLeNet and adding 14 layers of the CNN model that extracts features automatically. On the same Kaggle (Br35H) dataset, the proposed model key performance indicator was compared to transfer learning (TL) model (ResNet, VGG-16, SqeezNet, AlexNet, MobileNet V2) and different ML/DL. Furthermore, the proposed approach outperformed based on a key performance indicator (Acc, Recall, Precision, and F1-Score) of BTs classification. Additionally, the proposed methods exhibited high classification performance measures, Accuracy (99.51%), Precision (99%), Recall (98.90%), and F1-Score (98.50%). The proposed approaches show its superiority on recent sibling methods for BTs classification. The proposed method outperformed current methods for BTs classification using MRI images

Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that shortens the average life span of patients. Patients with misdiagnosed and insufficient medical treatment of BTs have less chance of survival. For tumor analysis, magnetic resonance imaging (MRI) is often utilized. However, due to the vast data produced by MRI, manual segmentation in a reasonable period of time is difficult, which limits the application of standard criteria in clinical practice. So, efficient and automated segmentation techniques are required. The accurate early detection and segmentation of BTs is a difficult and challenging task in biomedical imaging. Automated segmentation is an issue because of the considerable temporal and anatomical variability of brain tumors. Early detection and treatment are therefore essential. To detect brain cancers or tumors, different classical machine learning (ML) algorithms have been utilized. However, the main difficulty with these models is the manually extracted features. This research provides a deep hybrid learning (DeepTumorNetwork) model of binary BTs classification and overcomes the above-mentioned problems. The proposed method hybrid GoogLeNet architecture with a CNN model by eliminating the 5 layers of GoogLeNet and adding 14 layers of the CNN model that extracts features automatically. On the same Kaggle (Br35H) dataset, the proposed model key performance indicator was compared to transfer learning (TL) model (ResNet, VGG-16, SqeezNet, AlexNet, MobileNet V2) and different ML/DL. Furthermore, the proposed approach outperformed based on a key performance indicator (Acc, Recall, Precision, and F1-Score) of BTs classification. Additionally, the proposed methods exhibited high classification performance measures, Accuracy (99.51%), Precision (99%), Recall (98.90%), and F1-Score (98.50%). The proposed approaches show its superiority on recent sibling methods for BTs classification. The proposed method outperformed current methods for BTs classification using MRI images

ISUC: IoT-Based Services for the User’s Comfort

Emotions are alluded to as characteristic intuitive perspectives from certain conditions or temperaments. IoT applications can help in routine tasks and businesses. Most advances have not been taken advantage of regarding emotions. Emotions could be detected via the data gathered through IoT. Our investigation of related works revealed an absence of strategic methodologies in planning IoT frameworks according to feelings and shrewd alteration rules; thus, we present a philosophy that can rapidly assist in planning an IoT framework in this situation, where the identification of users is significant. We applied the proposed phases to test an IoT recommender framework named ISUC. The framework involves anticipating a user’s future emotions by utilizing boundaries gathered from IoT gadgets. It suggests new exercises for the user to obtain the ‘last’ state. Experimental results confirm our recommended framework has achieved over 85% exactness in anticipating users’ emotions in the future. The examination results presumed that an IoT-based framework could be created to detect positive emotions (e.g., peace, concretism, patience, enjoyment, and comfort) and negative emotions (e.g., irritation, abstraction, impatience, displeasure, and discomfort) to incite good emotions

Efficient and Secure WiFi Signal Booster via Unmanned Aerial Vehicles WiFi Repeater Based on Intelligence Based Localization Swarm and Blockchain

Recently, the unmanned aerial vehicles (UAV) under the umbrella of the Internet of Things (IoT) in smart cities and emerging communities have become the focus of the academic and industrial science community. On this basis, UAVs have been used in many military and commercial systems as emergency transport and air support during natural disasters and epidemics. In such previous scenarios, boosting wireless signals in remote or isolated areas would need a mobile signal booster placed on UAVs, and, at the same time, the data would be secured by a secure decentralized database. This paper contributes to investigating the possibility of using a wireless repeater placed on a UAV as a mobile booster for weak wireless signals in isolated or rural areas in emergency situations and that the transmitted information is protected from external interference and manipulation. The working mechanism is as follows: one of the UAVs detect a human presence in a predetermined area with the thermal camera and then directs the UAVs to the location to enhance the weak signal and protect the transmitted data. The methodology of localization and clusterization of the UAVs is represented by a swarm intelligence localization (SIL) optimization algorithm. At the same time, the information sent by UAV is protected by blockchain technology as a decentralization database. According to realistic studies and analyses of UAVs localization and clusterization, the proposed idea can improve the amplitude of the wireless signals in far regions. In comparison, this database technique is difficult to attack. The research ultimately supports emergency transport networks, blockchain, and IoT services

Recommending Reforming Trip to a Group of Users

With the quick evolution of mobile apps and trip guidance technologies, a trip recommender that recommends sequential points of interest (POIs) to travelers has emerged and recently received popularity. Compared to other outing recommenders, which suggest the following single POI, our proposed trip proposal research centers around the POI sequence proposal. An advanced sequence of the POI recommendation system named Recommending Reforming Trip (RRT) is presented, recommending a dynamic sequence of POIs to a group of users. It displays the information progression in a verifiable direction, and the output produced is the arrangement of POIs to be expected for a group of users. A successful plan is executed depending upon the deep neural network (DNN) to take care of this sequence-to-sequence problem. From start to finish of the work process, RRT can permit the input to change over time by smoothly recommending a dynamic sequence of POIs. Moreover, two advanced new estimations, adjusted precision (AP) and sequence-mindful precision (SMP), are introduced to analyze the recommended precision of a sequence of POIs. It considers the POIs’ consistency and also meets the sequence of order. We evaluate our algorithm using users’ travel histories extracted from a Weeplaces dataset. We argue that our algorithm outperforms various benchmarks by satisfying user interests in the trips

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load

Recommending Reforming Trip to a Group of Users

With the quick evolution of mobile apps and trip guidance technologies, a trip recommender that recommends sequential points of interest (POIs) to travelers has emerged and recently received popularity. Compared to other outing recommenders, which suggest the following single POI, our proposed trip proposal research centers around the POI sequence proposal. An advanced sequence of the POI recommendation system named Recommending Reforming Trip (RRT) is presented, recommending a dynamic sequence of POIs to a group of users. It displays the information progression in a verifiable direction, and the output produced is the arrangement of POIs to be expected for a group of users. A successful plan is executed depending upon the deep neural network (DNN) to take care of this sequence-to-sequence problem. From start to finish of the work process, RRT can permit the input to change over time by smoothly recommending a dynamic sequence of POIs. Moreover, two advanced new estimations, adjusted precision (AP) and sequence-mindful precision (SMP), are introduced to analyze the recommended precision of a sequence of POIs. It considers the POIs’ consistency and also meets the sequence of order. We evaluate our algorithm using users’ travel histories extracted from a Weeplaces dataset. We argue that our algorithm outperforms various benchmarks by satisfying user interests in the trips