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A Survey on Various Handoff Methods in Mobile Ad Hoc Network Environment
Communication has never been the same since the advent of cellular phones and numerous applications with different functionalities seem to crop up on a daily basis. Various applications seem to crop up on a daily basis. Ad hoc networks were developed with the intent of creating networks made up of interconnected nodes, on-the-go. Ad hoc networks have numerous applications, the most popular being vehicular ad hoc networks (VANETs). In VANETs, moving vehicles are considered to be the mobile nodes and mobile vehicular nodes move at high speeds. Mobility of the nodes makes it difficult to maintain stable communication links between the nodes and the access points. A process known as handoff is used to bridge this gap and is considered to be one of the solutions for unstable communication links over larger distances. Handoff can usually be seen when the nodes are mobile and start to move away from the access points. This paper discusses and compares various handoff methods that were proposed by various researchers with an intent to increase positive attributes while negating the rest of the components that do not support in increasing the efficiency of the handoff process. 2020, Springer Nature Singapore Pte Ltd. -
On Combinatorial Handoff Strategies for Spectrum Mobility in Ad Hoc Networks: A Comparative Review
Technological advancements have made communication on-the-go seamless. Spectrum mobility is a networking concept that involves access technologies that allow highly mobile nodes to communicate with each other. Ad-hoc networks are formed between mobile nodes where fixed infrastructure is not used. Due to the lack of such fixed access points for connectivity, the nodes involved make use of the best network available to transmit data. Due to heterogeneous networks involvement, the mobile nodes may face trouble finding the most optimal network for transmission. Existing technologies allow the nodes to select available networks, but the selection process is not optimized, leading to frequent switching. This leads to packet loss, low data rates, high delay, etc. Many researchers have proposed optimal strategies for performing handoff in wireless networks. This paper reviews combinatorial strategies that make use of multiple techniques to perform a handoff. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
MuLSA-Multi Linguistic Sentimental Analyzer for Kannada and Malayalam using Deep Learning
Natural language Processing has been always a topic of interest in artificial intelligence. Opinion mining or Sentiment Analysis is an important application of Natural language Processing. Sentiment Analysis of text is to extract the sentiments underlined in the text. In this paper, a multi-linguistic sentimental analyzer (MuLSA), is implemented, a model that would address Malayalam, Kannada and English text. This model explores two languages in three categories of the text, its original script, transliterated script, and the combination of both along with English. Deep Learning, Recurrent Neural Network with LSTM is used as the basis for this model. The model exhibits 82% of prediction accuracy. 2021 IEEE. -
Non-linear Convection in Couple Stress Fluid with Non-classical Heat Conduction Under Magnetic Field Modulation
A theoretical examination of thermal convection for a couple stress fluid which is electrically conducting and possessing significant thermal relaxation time is explored under time dependent magnetic field. Fouriers law fails for a diverse area of applications such as fluids subjected to rapid heating, strongly confined fluid and nano-devices and hence a non-classical heat conduction law is employed. The heat transport in the system is examined and quantified employing the Lorenz model. The Nusselt number is deduced to quantitate the transfer of heat. 2021, Springer Nature Singapore Pte Ltd. -
Linear and non-linear magneto-convection in couple stress fluid with non-classical heat conduction law
A theoretical examination of the thermal convection for a couple stress fluid which is electrically conductive and possessing significant thermal relaxation time with an externally applied magnetic field is carried out. Fourier's law fails when fluids are subjected to rapid heating or when it is confined and in the case of nano-devices. A frame invariant constitutive equation for heat flux is considered. The linear analysis is carried out implementing a normal mode solution and the non-linear stability of the system is analyzed using a double Fourier series. The analysis of the transfer of heat is determined in terms of the Nusselt number. Published under licence by IOP Publishing Ltd. -
A Deep Learning Methodology CNN-ADAM for the Prediction of PCOS from Text Report
Text categorization is a popular piece of work in natural language processing (NLP) and machine learning, and Convolutional Neural Networks (CNNs) can be used effectively for this purpose. Although CNNs are traditionally associated with computer vision tasks, they have been adapted and applied successfully to text classification problems. In the proposed study Convolutional Neural Networks (CNNs) with adam optimization algorithm plays a crucial role in detecting PCOS words from sonographic text reports. 2023 IEEE. -
Facilitating the New Normal: Challenges and Opportunities of Facility Management Companies in India
Covid 19 has brought the world to a standstill. The hospitality, Travel and tourism being affected the most due to travel restrictions across the world and within India. One of the sectors that have been affected majorly are the industries providing Facility Management services to core businesses. FM is dependent on the type of client business, the client organisation's structure and the market sector. This paper aims to gauge the impact of the pandemic on the Facility management companies in India. This also captures the positive and negative aspect of pandemic on the FM. An empirical research design has adopted to address the study objectives. This research involves both primary and secondary data. Primary data for the study have been collected in the form of structured questionnaire distributed among 300 respondents who are senior executives heading selected Facility Management companies in India. The target respondents have been selected based on simple random sampling to ensure the normal distribution of data. The Electrochemical Society -
Porous medium convection in a chemically reacting ferrofluid with lower boundary subjected to constant heat flux
The effect of exothermic chemical reaction of zero-order on Bard-Darcy ferroconvection is investigated using the technique of small perturbation. The eigenvalues associated with an adiabatic lower wall are determined by employing the Galerkin method. The Darcy-Rayleigh number is computed in terms of the parameters pertaining to chemical reaction and ferromagnetic fluid. It is established that, when chemical reaction escalates, there is a considerable shift from linearity and occurrence of asymmetry in the basic temperature profiles. It is ascertained that the threshold of Bard-Darcy ferroconvection is augmented through the stresses of both mechanisms due to chemical reaction and magnetization, and the ferroconvective instability due to nonlinearity of magnetization is rather inconsequential when chemical reaction is present. It is also shown that the destabilizing feature of magnetic forces resulting from the fluid magnetization is less pronounced when chemical reaction is present. Published under licence by IOP Publishing Ltd. -
Chemical Reaction-Driven Ferroconvection in a Porous Medium
The effect of chemical reaction on the outset of convection of a ferromagnetic fluid in a horizontal porous layer which is heated from below is studied using small perturbation method. Assuming an exothermic zero-order chemical reaction, the eigenvalues are found by employing the Galerkin method. The effect of magnetic parameters and Frank-Kamenetskii number is discussed. It is established that both magnetic forces and chemical reaction accelerate the threshold of ferroconvection. Further, the fluid layer is destabilized marginally when the nonlinearity of magnetization is strong enough. 2021, Springer Nature Singapore Pte Ltd. -
Gravity modulation effect on ferromagnetic convection in a Darcy-Brinkman layer of porous medium
The influence of a timedependent body force on the threshold of convective instability in a magnetic fluid filled horizontal porous layer is investigated. The gravity modulation effect is treated by employing a perturbation method. The correction Rayleigh number is computedas a function of the modulation frequency, porous and magnetic parameters. It is expounded that, for small and reasonable values of the modulation frequency, gravity modulation and magnetic mechanism have opposing influence on the stability. The study further explicates that, when the gravity modulation frequency increases beyond all bounds, manifestation of the disappearance of the magnetic and porous medium effects on the stability is highly likely. Published under licence by IOP Publishing Ltd. -
Metaheuristicsbased Task Offloading Framework in Fog Computing for Latency-sensitive Internet of Things Applications
The Internet of Things (IoT) applications have tremendously increased its popularity within a short span of time due to the wide range of services it offers. In the present scenario, IoT applications rely on cloud computing platforms for data storage and task offloading. Since the IoT applications are latency-sensitive, depending on a remote cloud datacenter further increases the delay and response time. Most of the IoT applications shift from cloud to fog computing for improved performance and to lower the latency. Fog enhances the Quality of service (QoS) of the connected applications by providing low latency. Different task offloading schemes in fog computing are proposed in literature to enhance the performance of IoT-fog-cloud integration. The proposed methodology focuses on constructing a metaheuristic based task offloading framework in the three-tiered IoT-fog-cloud network to enable efficient execution of latency-sensitive IoT applications. The proposed work utilizes two effective optimization algorithms such as Flamingo search algorithm (FSA) and Honey badger algorithm (HBA). Initially, the FSA algorithm is executed in an iterative manner where the objective function is optimized in every iteration. The best solutions are taken in this algorithm and fine tuning is performed using the HBA algorithm to refine the solution. The output obtained from the HBA algorithm is termed as the optimized outcome of the proposed framework. Finally, evaluations are carried out separately based on different scenarios to prove the performance efficacy of the proposed framework. The proposed framework obtains the task offloading time of 71s and also obtains less degree of imbalance and lesser latency when compared over existing techniques. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards Computation Offloading Approaches in IoT-Fog-Cloud Environment: Survey on Concepts, Architectures, Tools and Methodologies
The Internet of Things (IoT) provides communication and processing power to different entities connected to it, thereby redefining the way objects interact with one another. IoT has evolved as a promising platform within short duration of time due to its less complexity and wide applicability. IoT applications generally rely on cloud for extended storage, processing and analytics. Cloud computing increased the acceptance of IoT applications due to enhanced storage and processing. However, the integration does not offer support for latency-sensitive IoT applications. The latency-sensitive IoT applications had greatly benefited with the introduction of fog/edge layer to the existing IoT-Cloud architecture. The fog layer lies close to the edge of the network making the response time better and reducing the delay considerably. The three-tier architecture is still in its earlier phase and needs to be researched further. This paper addresses the offloading issues in IoT-Fog-Cloud architecture which helps to evenly distribute the incoming workload to available fog nodes. Offloading algorithms have to be carefully chosen to improve the performance of application. The different algorithms available in literature, the methodologies and simulation environments used for the implementation, the benefits of each approach and future research trends for offloading are surveyed in this paper. The survey shows that the offloading algorithms are an active research area where more explorations have to be done. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Dielectric performance of graphene nanostructures prepared from naturally sourced material
Cost-effective and environmentally benign approach was adopted for the synthesis of oxidized graphene nanostructures from the precursor coke via Improved Hummers' method. The surface states of oxygen functional groups provided strong polarization for enhanced dielectric properties. Occurrence of dipole and interfacial polarizations in the low frequency region contributed to the dispersive behaviour of ?', ?", and tand.The relaxation phenomenon of the structure lead to an augmented electrical conductivity with increase in frequency. Our finding reveals the advantageous fabrication of graphene nanostructure having high dielectric constant (1 0 5) but with low loss which can be used in advanced nanodielectrics. 2020 Elsevier Ltd. All rights reserved. -
Effect of sonication in enhancing the uniformity of MWCNT distribution in aluminium alloy AA2219 matrix
The present paper investigates the effect of premixing process on the distribution of 0, 0.5, 0.75, 1 and 2 wt.% multiwall carbon nanotubes (MWCNTs) and resultant properties of aluminium alloy AA2219 matrix. Premixing process consists of ultrasonication, magnetic stirring and mechanical stirring. FESEM was used for characterizing the distribution of reinforcement in the matrix. Ball milling with premixing was found to be effective in achieving better uniform distribution of the reinforcement than mere ball milling. Hardness testing of the composite revealed reinforcement of MWCNT enhances the matrix hardness. The thermal stability of composite as evidenced by DTA analysis proved the presence of MWCNT without any structural damages. 2019 Elsevier Ltd. All rights reserved. -
Comparative Analysis of Non-Destructive Silkworm Cocoon Sex Classification using Machine Learning Models Based on X-Ray and Camera Images
Silk production plays a vital role in global economies, with sericulture heavily dependent on efficient seed production processes. Traditional methods involve manually cutting cocoons to classify silkworm sex, which leads to silk damage, labor intensiveness, and potential inaccuracies. In response, non-destructive technologies like X-ray and camera imaging have emerged, enabling sex classification without cocoon damage, thereby enhancing efficiency and reducing manual errors. This study undertakes a comparative analysis of X-ray and camera imaging methods for silkworm sex classification. X-ray imaging demonstrates superior efficiency in extracting detailed features from silkworm pupae, crucial for accurate classification. In contrast, camera imaging excels in the rapid and cost-effective classification of silkworms based on extracted features. The results reveal significant findings: using X-ray imaging model achieves 97.1% accuracy for FC1 and 96.3% accuracy for FC2, employing ensemble learning technique like AdaBoost. Meanwhile, camera imaging achieves an accuracy above 98% for both FC1 and FC2 using XGBoost, showcasing its effectiveness in real-time classification scenarios. Computational time analysis indicates that X-ray imaging is faster in feature extraction, while camera imaging consumes less memory during classification. These findings underscore the practical advantages of non-destructive imaging technologies and machine learning in revolutionizing sericulture practices. By enhancing productivity and sustainability through accurate sex classification of silkworms, these methods contribute significantly to the growth and efficiency of the silk industry. 2024 IEEE. -
Effect of MWCNT concentration on microstructures, mechanical properties and sintering behaviour of spark plasma sintered AA2219-MWCNT composites
Uniform dispersion of nano tubes without any structural damage is still a challenge in processing of metal matrix nano composites. Effective dispersion of MWCNT (0, 0.5, 0.75, 1, 2 wt. %) in AA 2219 alloy powder has achieved with a combined effect of premixing process and ball milling. An effort is done using spark plasma sintering (SPS) to consolidate the composites and to investigate the effect of MWCNT concentrations on enhancement of the properties of the composites. Particle boundary clustering was observed on consolidated composites even after a uniform distribution is achieved in alloy powder. Significant improvement in mechanical property is observed by reinforcing with MWCNT. Preferable level of MWCNT for bulk sampling was selected as 0.75 wt. % and 1 wt.%. Addition beyond the limit will cause agglomeration and will act like a lubricant during ball milling. 2019 Elsevier Ltd. -
Self lubricating property of MWCNT in AA2219 composites during high energy ball milling
Revolutions in nanotechnology enabled the development of advanced nanocomposites with superior properties for engineering applications especially in automotive and aerospace industries. Among this carbonaceous nano materials like MWCNT have got more attention. Addition of MWCNT in metal matrix results in retardation of friction coefficient and improvement on other mechanical properties based on its dispersion. MWCNT won't have sufficient space to occupy over the powder surface, when the addition is beyond a limit and acts as a solid lubricant during milling. Investigations on self lubricating property during milling were done by using scanning electron microscope, X-ray diffraction and powder density. Uniform dispersion was the bottleneck to utilize their attractive properties of the reinforcement. An attempt had been done for a uniform dispersion during premixing process using a combination of ultra-sonication, magnetic and mechanical stirring followed by high energy ball milling. 2019 Elsevier Ltd. -
Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities
Deep convolutional neural networks (CNNs) have turn out to be one of the most advanced approaches trendy distinguishing snapshots in extraordinary fields. White blood cell classification is crucial for diagnosing anaemia, leukaemia, and a variety of other hematologic illnesses. Transfer learning with CNNs is frequently used in biological image categorization. Traditional methods for WBC classification is costly is terms of time and money. In the paper three convolutional neural network architectures are proposed which is based on transfer learning for microscopic image classification and compare the performance of models. The paper compares Transfer learning models like VGG-16, VGG-19, VGG-19 SVM hybrid and AlexNet. VGG-16 gives the best classification performance in comparison. VGG-16 model is which has a train accuracy of 0.9538 and train loss of 0.1322. 2022 IEEE. -
IMPACT OF ARTIFICIAL INTELLIGENCE ON E-BANKING AND FINANCIAL TECHNOLOGY DEVELOPMENT
Artificial intelligence has a significant impact on financial technologies. Machine learning is an important field of artificial intelligence. Machine learning is a subset of artificial intelligence. According to client knowledge gathered by machine learning, data structures may be more easily comprehended and changed. Machine learning, although still being employed in the IT business, has its own set of benefits. They are used by computer program to explain or solve a typical issue because they are a set of well-written instructions. Data inputs for factual research may be prepared by computers using master learning algorithms that can deliver results within a certain range. Computers are used to model test data, and frameworks are used to make automated decisions based on input data. Banks and financial institutions may benefit from the use of machine learning. This article discusses applications of machine learning in banking and finance sector. The Electrochemical Society -
An efficient cloud based architecture for integrating content management systems
The use of digital content is increasing day after day and now it is an essential element of our day today life. The amount of stored information is so huge that it is highly difficult to manage the content especially in a distributed cloud environment. There are many open source software solutions available in cloud to handle huge amount of digital data. However none of these solutions addresses all the requirements needed to manage the content spread out in multiple systems effectively. The user has to relay on multiple content management systems to do the work. This turns into ever more unwieldy, time consuming and leads to loss of data. Using robust and integrated content management systems, these issues could be solved effectively. In this paper we have identified various challenges of using the content management system in the cloud after surveying many Content Management System related article and proposed an integrated solution named Cloud based Architecture integrating Content Management System which is capable of interfacing with various unique features available at different content management system installations in the cloud. This maximizes the functionality and performance of any Content management systems. The Representational State Transfer (REST) protocol is used to integrate the best features of various open source content management systems. REST provides higher level of security compared to existing systems as it does not store the user sessions. The users can interact with the system with the help of an interface which abstracts the complexities of multiple content management systems running in the cloud. 2017 IEEE.