Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components

Intepretable Deep Gaussian Naive Bayes Algorithm (Idgnba) Based Task Offloading Framework for Edge-Cloud Computing

When it comes to Internet of Things (IoT) applications and machine learning based computing, resource-restricted edge devices are inadequate due to the exponential growth of mobile information and the massive need for processing power. An edge offload, the migration of complex tasks from IoT devices to edge cloud servers, is a distributed computing paradigm that has the potential to overcome the IoT device resource limits, lessen the computational load, and increase the effectiveness with which activities are processed. However, due to the NP-hard nature of the optimum offloading decision-making issue, an efficient solution using traditional optimization techniques is difficult. Current deep learning algorithms still have a lot of problems, such as their slow pace of learning and limited ability to adapt to new environments. We provide a unique interpretable deep Gaussian naive Bayes technique (IDGNBA) for extremely fine offloading choices to address these issues. Through several simulation studies, we assess the efficacy of IDGNBA and find that it performs better in terms of offloading than traditional techniques. The model has strong mobility and can quickly adjust to a fresh MEC working atmosphere while taking offloading decisions in real-time

A Review on Cloud Data Security Challenges and existing Countermeasures in Cloud Computing

Cloud computing (CC) is among the most rapidly evolving computer technologies. That is the required accessibility of network assets, mainly information storage with processing authority without the requirement for particular and direct user administration. CC is a collection of public and private data centers that provide a single platform for clients throughout the Internet. The growing volume of personal and sensitive information acquired through supervisory authorities demands the usage of the cloud not just for information storage and for data processing at cloud assets. Nevertheless, due to safety issues raised by recent data leaks, it is recommended that unprotected sensitive data not be sent to public clouds. This document provides a detailed appraisal of the research regarding data protection and privacy problems, data encrypting, and data obfuscation, including remedies for cloud data storage. The most up-to-date technologies and approaches for cloud data security are examined. This research also examines several current strategies for addressing cloud security concerns. The performance of each approach is then compared based on its characteristics, benefits, and shortcomings. Finally, go at a few active cloud storage data security study fields

A Call Graph Reduction based Novel Storage Allocation Scheme for Smart City Applications

Today s world is going to be smart even smarter day by day Smart cities play an important role to make the world smart Thousands of smart city applications are developing in every day Every second very huge amount of data is generated The data need to be managed and stored properly so that information can be extracted using various emerging technologies The main aim of this paper is to propose a storage scheme for data generated by smart city applications A matrix is used which store the information of each adjacency node of each level as well as the weight and frequency of call graph It has been experimentally depicted that the applied algorithm reduces the size of the call graph without changing the basic structure without any loss of information Once the graph is generated from the source code it is stored in the matrix and reduced appropriately using the proposed algorithm The proposed algorithm is also compared to another call graph reduction techniques and it has been experimentally evaluated that the proposed algorithm significantly reduces the graph and store the smart city application data efficientl

A Recent Connected Vehicle - IoT Automotive Application Based on Communication Technology

Realizing the full potential of vehicle communications depends in large part on the infrastructure of vehicular networks. As more cars are connected to the Internet and one another, new technological advancements are being driven by a multidisciplinary approach. As transportation networks become more complicated, academic, and automotive researchers collaborate to offer their thoughts and answers. They also imagine various applications to enhance mobility and the driving experience. Due to the requirement for low latency, faster throughput, and increased reliability, wireless access technologies and an appropriate (potentially dedicated) infrastructure present substantial hurdles to communication systems. This article provides a comprehensive overview of the wireless access technologies, deployment, and connected car infrastructures that enable vehicular connectivity. The challenges, issues, services, and maintenance of connected vehicles that rely on infrastructure-based vehicular communications are also identified in this paper

A Systematic Survey of Classification Algorithms for Cancer Detection

Cancer is a fatal disease induced by the occurrence of a count of inherited issues and also a count of pathological changes. Malignant cells are dangerous abnormal areas that could develop in any part of the human body, posing a life-threatening threat. To establish what treatment options are available, cancer, also referred as a tumor, should be detected early and precisely. The classification of images for cancer diagnosis is a complex mechanism that is influenced by a diverse of parameters. In recent years, artificial vision frameworks have focused attention on the classification of images as a key problem. Most people currently rely on hand-made features to demonstrate an image in a specific manner. Learning classifiers such as random forest and decision tree were used to determine a final judgment. When there are a vast number of images to consider, the difficulty occurs. Hence, in this paper, weanalyze, review, categorize, and discuss current breakthroughs in cancer detection utilizing machine learning techniques for image recognition and classification. We have reviewed the machine learning approaches like logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), decision tree (DT), and Support Vector Machines (SVM)

Perspective Chapter: Epidural Administration-New Perspectives and Uses

Neuraxial techniques are commonplace in labor analgesia. Techniques for labor analgesia range from intrathecal and epidural anesthesia to peripheral nerve blocks, nitrous oxide, intravenous infusions, and acupuncture. The epidural approach is the most popular as it allows for local anesthetics to diffuse into the intrathecal space along with repeated or continuous doses of medication for labor and primary anesthetic for surgeries. The epidural technique affects differing spinal nerves (i.e., pain, autonomic, sensory, and motor) with varied effects depending on the concentration and volume of LA used. Adverse effects do exist following these techniques with hypotension being a major concern. A multitude of anesthetic agents can be given in the epidural; opioids are the most frequently used local anesthetic adjuvants. Alpha 2 adrenoreceptor agonists are also used as local anesthetic adjuvants. Although not performed routinely, peripheral nerve blocks play a complementary and supplementary role in epidural analgesia and anesthesia. There are absolute and relative contraindications to epidural anesthesia. Alternatives to neuraxial anesthesia that can be offered include infusion of ultrashort acting opioids, nitrous oxide, opioid agonist-antagonists, ketamine, TENS, and acupuncture. Local Anesthetic Systemic Toxicity may be more prevalent in the pregnant

Surgical Treatment of Patients with Lennox-Gastaut Syndrome Phenotype

Lennox-Gastaut syndrome (LGS) is a devastating and refractory generalized epilepsy affecting children and adolescents. In this study we report the results of resective surgery in 18 patients with LGS phenotype who underwent single-lobe/lesionectomy or multilobe resection plus multiple subpial transection and/or callosotomy. After surgery, seven patients became completely seizure-free (Engel Class I) and five almost seizure-free (Engel Class II). Additional four had significant seizure control (Engel Class III), and two had no change in seizure frequency (Engel Class IV). Of the 4 patients without any lesion on brain MRI, 2 ended with Engel Class II, 1 with III and the other with IV in Engels' classification. Mean intelligence quotient (IQ) increased from 56.1 ± 8.1 (mean ± SD) before operation to 67.4 ± 8.2 (mean ± SD) after operation, a significant improvement (P = 0.001). Results also indicated that the younger the patient at surgery, or the shorter the interval between onset of seizure and resective operation, the better the intellectual outcome. Our data suggest that resective epilepsy surgery can be successful in patients with LGS phenotype as long as the EEG shows dominance of discharges in one hemisphere and corresponding ipsilateral imaging findings, even with contralateral ictal discharges

A Fog-Cluster Based Load-Balancing Technique

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework