Sri Eshwar College of Engineering

Abstract Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

There are a slew of elements at work in the composites sector, from people and markets to technology and innovation, that are continually reshaping the industry's structure. For now, composite materials' winning combination of high strength-to-weight ratio continues to propel them into new areas, but other attributes are just as crucial. These properties, which may be customized for unique purposes, result in a completed product requiring fewer raw materials and fewer joints and fasteners, as well as reduced assembly times, thanks to composite materials. To lower product lifespan costs, composites also have demonstrated resilience in industrial applications to temperature extremes as well as corrosion and wear. Polymers, ceramics, and metals can all be used as matrices. Thermoplastic (TP) resin is the second most prevalent matrix type, and it is becoming increasingly popular among composite makers. By melting or softening and then chilling the material, thermoplastic linear polymer chains are generated and may be reformed into shaped solids. It is common for thermoplastics to be offered in sheet or panel form, which may be treated using in situ consolidation processes, such as pressing, to manufacture durable, near-net-shape components without the need for an autoclave or vacuum bag cure. Correcting abnormalities or fixing harm done in service is possible with reformability.

As an underlayment to cellular 5G communication network, device-to-device (D2D) communications will not only boost capacity utilization and power efficiency but also provide public health and public safety services. One of the most important requirements for these businesses is to have alternate access to cellular networks in the event that they are partially or completely disrupted as a result of a natural disaster. Despite limited communication coverage and bandwidth scarcity, the 3rd Generation Partnership Project (3GPP) must have developed a new device-to-device (D2D) communication method fundamental enhanced mobile that can strengthen spectral efficiencies besides allowing direct communication of gadgets in close propinquity devoid of transitory by elevated-node B (eNB). Unfortunately, enabling data transmission on a cellular connection offers a challenge in terms of two-way radio source administration, because D2D associates recycle cellular users’ uplink radio resources, which might create interference to D2D user equipment’s (DUE) receiving channels. In this study, we concentrate on optimal cluster head selection using the binary flower pollination optimization algorithm by designing an energy-efficient lifetime-aware leisure degree adaptive routing protocol named OptCH_L-LDAR. This topology is constructed with a multi-hop obliging communication system, instructed on the way to wrap an extensive remoteness connecting source and destination. The proposed OptCH_L-LDAR is compared with three state-of-art methods such as binary flower pollination (BFP) algorithm, time division multiple access (TDMA), and data-driven technique (DDT). As a result, the proposed OptCH_L-LDAR achieves 96% of energy efficiency, 89% of lifetime, 97% of outage probability, and 98% of spectral efficiency.

Summary Recently, much of the wireless personal area network (WPAN) research concerns network protocols, scheduling, and security challenges but the major issue of resource utilization has been very rarely investigated. The design of resource sharing in a network gets more attention when the number of users increases. While optimizing performance, resource utilization plays a critical role. In this paper, the numerical performance of a wireless resource utilization algorithm for a bi‐partite scatternet is presented. This algorithm is focused to enhance the bandwidth allocation and power utilization of wireless scatternets. Every node can communicate with a single neighbor at a time with minimum resources. Finally, the performances of the RUBI algorithm are shown. This algorithm is compared with the existing algorithms such as the load adaptive scheduling algorithm and pseudorandom coordinated scheduling scheme in terms of various parametric metrics like reliability, throughput, collision probability, transmission probability, and signal‐to‐noise ratio (SINR). The proposed L‐RUBI achieves 93.4% of reliability, 93.6% of transmission probability, 91.4% of throughput, 76.8% of collision performance, and 72.2% SINR.

In general, the various medical systems currently available provide insights into changes in the tumor genome of patients with tumor sequencing. Most of the tumor DNA sequencing can also be referred to as genetic specification or genetic testing. The sequence results help clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumor rather than a one-size-fits-all treatment approach. The tumor sequencing also plays a major role in cancer research. In this paper, an improved method based on machine learning was proposed to analyze the sequencing and tumor sequencing patterns of the human gene. This proposed method analyzes the circulatory problems of patients with different tumor types for analysis in the public domain. It also constantly monitors large data sets of cancer or tumor genetic sequences to calculate tumor size and location. This allows the doctor to get an accurate report on the type of tumor and the problems it can cause to the patient. The Analysis of these datasets of cancer tumor gene sequences reveals that the genetic makeup of each patient is different and that no two cancers are the same.

In general, the number of diseases is increasing in the current era. There is also a growing fear among the patients about the nature of the growing number of new diseases and their consequences. Thus the patients are interested in getting treatment from healthcares for minor physical problems. But factors such as lack of space in healthcares and lack of time for doctors make patients uncomfortable. Sometimes doctors recommend that patients come to the healthcare only for emergency treatment. In this paper, a triangular method is proposed which takes into account the needs of the patients, calculates the time management of the doctors and analyzes the facilities available in the healthcares. Designed with the help of medical IoT devices, efficient sensors fitted to patients' bodies monitor their physical condition. Furthermore based on this sensor information the doctor can provide the patient with the necessary instructions from where they were. These sensors send information directly to the healthcare when further emergency treatment is needed. Thus healthcares can make the necessary arrangements to provide the necessary treatments to the patient immediately.

For wireless devices; information sharing from one device to another represents the most significant task for typically discovering the right devices and connecting them is extremely critical. In data communication networks, D2D represent a direct connecting path between two nodes. It cannot depend on Base Station (BS). In this type of D2D, communication links are constantly non transparent and it can arise on in-band or out-band of the spectrum. Data communication via Base Station (BS) can reduce data rate and provide proper resource utilization, which includes voice and text service. The direct communication between two nodes may not follow the resource strategy and utilize more power. In this paper, various enhancements of resource allocation scheme used in the Device to Device (D2D) communications are examined. These resource allocation schemes are compared for its merits and de-merits and to identify the areas of improvement for the future resource allocation schemes based on autonomous direct D2D communication.

Efficient clustering and routing is a main challenge in a wireless sensor network (WSN). To achieve better quality-of-service (QoS) performance, this work introduces k-medoids with improved artificial-bee-colony (K-IABC)-based energy-efficient clustering and the cross-layer-based Harris-hawks-optimization-algorithm (CL-HHO) routing protocol for WSN. To overcome the power-asymmetry problem in wireless sensor networks, a cross-layer-based optimal-routing solution is proposed. The goal of cross-layer routing algorithms is to decrease network-transmission delay and power consumption. This algorithm which was used to evaluate and select the effective path route and data transfer was implemented using MATLAB, and the results were compared to some existing techniques. The proposed CL-HHO performs well in packet-loss ratio (PLR), throughput, end-to-end delay (E2E), jitter, network lifetime (NLT) and buffer occupancy. These results are then validated by comparing them to traditional routing strategies such as hierarchical energy-efficient data gathering (HEED), energy-efficient-clustering routing protocol (EECRP), Grey wolf optimization (GWO), and cross-layer-based Ant-Lion optimization (CL-ALO). Compared to the HEED, EECRP, GWO, and CL-ALO algorithms, the proposed CL-HHO outperforms them.

When creating an antivirus, not only the latest security mechanisms are taken into account, but also the needs of users. That’s why this antivirus works together at high speed. The program interface allows it to choose the most optimal function. Free delivery allows a large number of users to rate this product. With the help of an intelligent deep learning model, a smart solution was proposed in this article to identify the security threats. It enables rapid detection and efficient treatment of polymorphic and encrypted viruses. Infected archives can now be detected when opened. This helps prevent its software from getting infected again. The program has the ability to create a confidence zone, which allows reducing the scanning time by creating a list of objects subject to scanning. This list should only contain sources that you are sure of. This application is designed to provide reliable protection for your computer. The proposed model allows it to protect its computer from all types of viruses and various Trojans, root kits, worms, and other malicious objects.

Industrial Wireless Sensor Networks (WSNs) are becoming increasingly popular due to their enhanced scalability and low cost of deployment. However, they also present new challenges, such as energy optimization and network maintenance, which industrial users must address. In order to meet the challenges, Machine Learning techniques have been used to create an enhanced energy optimization model for Industrial WSNs. This model utilizes knowledge-based learning to identify and optimize the energy consumption of the nodes, allowing Industrial WSNs to consume the least amount of energy for the given tasks. In addition, the model also evaluates the effectiveness of feedback control schemes and predicts the best possible outcomes for its application in Industrial WSNs to ensure higher efficiency and longer network lifetime. The model also enables the exploration of potential trade-offs between power consumption and communication performance to ensure a better energy-efficient solution. The proposed EEOM obtained 64.72% transmission energy consumption, 35.28% transmission energy saving, 67.27% received energy consumption, 32.73% received energy storage, 52.16% idle-mode energy consumption, 47.84% idle-mode energy storage, 66.31% sleep-mode energy consumption, and 33.69% sleep-mode energy storage. It also obtained 90.44% prevalence threshold, 90.33% critical success index, 93.93% Delta-P, 90.06% MCC and 92.17% FMI rates. It also provides the ability to identify the best selection of nodes and paths for data transmission to reduce network traffic. When applied in conjunction with manual intervention, these automated knowledge-based techniques will make Industrial WSNs more reliable, efficient, and energy-cost effective.

Programmed Face Identification (PFI) of images more reliable even under unstable lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, multiple feature fusion. Additionally we propose Phase Congruency Features which is an approach for detects points of order in the phase spectrum within images. By combining the results produced from both the above mentioned approaches, we try to increase the accuracy of recognizing the given image from group images even with difficult lighting conditions.

In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of nRMSE (Normalized Root Mean Square Error), 48.04% of MaxAE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).

In general, the company sometimes uses unregistered functions in database, which significantly improves performance, but does not leave the possibility of recovery except for backup. That is, actions must be performed immediately after passing the session. A queue problem is likely to cause data loss and downtime of about a week. In modern conditions, this can lead to the bankruptcy of the company. It can be seen that backup systems have been installed and configured, but despite this, they have not succeeded in restoring within the time frame specified in the SLA. In this study, a secured database monitoring method was proposed to improve data backup and recovery operations in cloud computing. In this proposed method, the backup speed is directly proportional to the amount of data, while having at least 30% annual data growth. In 3–4 years, the data at least doubled, but for some companies, this number is even higher, while the backup speed does not change. Those terms and those SLAs that were relevant 3–4 years ago now need to be at least doubled. At the same time, business requirements for data recovery (recovery point objective/recovery time objective) continue to grow

Sustainable energy harvesting plays a crucial role in advancing the goals outlined in the United Nations Sustainable Development Goals (SDGs). Solar flat plate collectors (FPC) are integral to sustainable energy harvesting, aligning closely with several Sustainable Development Goals (SDGs) particularly goals 7, 8, 9, 13 and 17. The heat transfer fluid is one of the most important inputs in the solar flat plate collector system. The improvement in the properties of heat transfer fluid significantly improves the collector efficiency. Though Nanofluid is one of the solutions, this investigation is motivated by the use of Al2O3 and SiO2 along Syltherm 800 in the form of nanofluids and hybrid nanofluid to improve the efficiency of FPC. The Syltherm 800 fluid (BF), Syltherm 800/Al2O3 nanofluid (ANF), Syltherm 800/SiO2 nanofluid (SNF), and Syltherm 800/Al2O3/SiO2 hybrid nanofluid (HNF) were tested and analysed to improve FPC performance. The results exemplified that using HNF improved the outlet temperature to the high of 126.9 °C, the average heat absorption of 4734.5 W, and the heat transfer coefficient (HTCO) of 166.3 W/m2K. The maximum average thermal efficiency and exergy efficiency of about 76.3%, and 49.8% respectively. Based on the experimental outcomes of the Syltherm 800/Al2O3/SiO2 hybrid nanofluid is recommended to improve the FPC thermal performances.

The present research work reports the fabrication and evaluation of the mechanical properties of hybrid aluminium matrix composites (HAMC). Aluminium 7075 (Al7075) alloy was reinforced with particles of boron carbide (B4C) and coconut shell fly ash (CSFA). Al7075 matrix composites were fabricated by stir casting method. The samples of Al7075 HAMC were fabricated with different weight percentages of (0, 3, 6, 9 and 12wt.%) B4C and 3wt.% of CSFA. The mechanical properties discussed in this work are hardness, tensile strength, and impact strength. Hardness of the composites increased 33% by reinforcements of 12wt.% B4C and 3wt.% CSFA in aluminium 7075 alloy. The tensile strength of the composites increased 66% by the addition of 9wt.% B4C and 3wt.% CSFA in aluminium 7075 alloy. Further addition of reinforcements decreased the tensile strength of the composites. Elongation of the composites decreased while increasing B4C and CSFA reinforcements in the matrix. The impact energy of the composites increased up to 2.3 J with 9wt.% B4C and 3wt.% CSFA addition in aluminium alloy. Further addition of reinforcement decreased the impact strength of the composites. The optical micrographs disclosed the homogeneous distribution of reinforcement particles (B4C and CSFA) in Al7075 matrix. The homogeneously distributed B4C and CSFA particles added as reinforcement in the Al7075 alloy contributed to the improvement of hardness, tensile strength, and impact strength of the composites.

A modern telephone can only be used if it is a dual-band transceiver. Also, an indispensable condition is the availability of Internet access. Modern cell phones can only be used for their intended purpose: making calls. Due to the fact that the operating system is preinstalled on devices, the list of possibilities for gadgets could be expanded almost indefinitely. So you can even do a full-fledged dual-band transceiver from a cell phone. In this paper, an innovation in the development of mobile radio models dual-band transceivers in wireless cellular communication is proposed. For the dual-band transceiver in the phone to work, you need an Internet connection. Progress in the development of technologies for mobile networks does not stand still, and with each new standard and technology for mobile networks, new opportunities for using the network open up for end subscribers. It is based on packet voice data transmission called push-to-talk.

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

Currently, studies have shown that one in three people infected with coronavirus disease-19 (COVID-19) is likely to have had long-term exposure to COVID-19, known as long-term COVID-19. Clinical studies indicate that many people infected with the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) COVID-19 pandemic have long-term COVID-19 exposure. According to the study, it has been said that people with diabetes and obesity, and people who have received organ transplants, are more likely to suffer from this long-term effect of COVID-19. In this article, the effects of long-term COVID-19 exposure on neurological disability patients are analyzed with the help of a neuromachine learning model. The proposed model also shows that this long-term COVID problem does not depend on the factors such as race, age, gender, and socioeconomic status of those people. According to the proposed model, people suffering from long-term COVID problems continue to suffer from physical fatigue and shortness of breath and are regularly monitored and classified as per the proposed instructions. Even after they recover from the disease, various side effects are seen.

In this work, the existence of Hilfer fractional integro-differential equations with nonlocal conditions are discussed. To obtain such result, we use Mo¨nch fixed point theorem and the techniques of noncompactness. An application is presented to validate the theoretical results.

The mobile networks seem to have a steady future in the direction of the recent emergence of socially aware cognitive mobile networks. Their style and design are specifically made in improving shared spectrum space access, in cooperative spectrum sensing, and in enhancing device-to-device communications. Socially aware mobile networks do have enough potential to amass sufficient returns in the efficacy of the spectrum and also to march and gain a considerable amount of increase in the capacity of the network. Even though there are lot of gains in its potency to be reaped yet, still there seems to be enough challenges that are both business- and technical-related that have to be taken care of. This chapter delves into the cognitive radio (CR) and its social relations and also makes sufficient exploits in establishing a scheme that will be based on social-based cooperative sensing scheme (SBC).