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Smart Vehicle Recognition System on Indian Roads Under Rainy Conditions
Recognition of vehicles under the different weather condition is very challenging. This work aims to recognize vehicles on Indian road in accordance with their visibility. It is important to recognize the surround roadside objects, particularly front and rare vehicles to avoid the accidents. Especially in raining conditions vehicle recognition is rate traffic surveillance cameras get decreases due to water droplets. Hence, we proposed a method for recognition of vehicles on road in rainy condition using image processing in computer vision techniques to improve the recognition rate. In the proposed method, an instance segmentation technique is used to segment the vehicles in Indian road scene and the visual noise and texture features are analysed and computed in the segmented images to recognize the vehicles more accurately in rainy conditions. By integrating the visual noise features with the texture feature and instance segmentation, the accuracy of vehicle recognition is improved. The experimental findings demonstrated that the suggested approach could more accurately predict the visibility of vehicles in rainy weather conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
PMFRO: Personalized Mens Fashion Recommendation Using Dynamic Ontological Models
There is a thriving need for an expert intelligent system for recommending fashion especially focusing on mens fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for mens recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the mens fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the mens fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending mens fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Arming Farmers with Smart Farming: The Future of Agriculture
Internet of Things (IoT) innovation is currently one of the growing fields across a diversity of industries, together with agriculture. IoT enhances our lives by making and promoting developments in a wide range of actions to encourage them to become more appropriate, practicality, and enhanced using suitable man-made recognition. Smart agricultural frameworks recognize a social trade toward more helpful, lower-cost agribusiness because of this innovation. The proposed work is to use IoT in the agriculture industry to collect real-time data (soil moisture, temperature, and so on) to help one look at a few climate scenarios from afar, efficiently, and greatly increase production. A global solution for monitoring and managing the agricultural field remotely has been proposed. Implementation of a local stand-alone field control unit that includes detection and activation capabilities. Developed a cloud solution for data storage, real-time monitoring, and historical data visualization based on the ThingSpeak cloud platform. Remote managing and control functions have been realized in both the local unit and the cloud using IoT infrastructure. 2022 IEEE. -
An Analytical Review on Data Privacy and Anonymity in 'Internet of Things (IoT) Enabled Services'
Nowadays, the Internet of Things (IoT) is an emerging technology, spreading all over the world so the number of devices is increasing day by day. So, the volume and data complexity has increased drastically in the past three years. The resultant system might contain a significant number of heterogeneous devices, posing integration and scalability issues that must be addressed. In such a situation, security and privacy are commonly regarded as a significant concern. On the other hand, user privacy, defined as the capacity to provide data protection and anonymity, must be protected, which is especially important when personal and/or sensitive information is involved. This paper presents the comprehensive survey, characteristics, and application of IoT and the immense number of challenges raced and faced during the implementation of IoT frameworks. 2021 IEEE. -
Experimenting with scalability of floodlight controller in software defined networks
Software Defined Network is the booming area of research in the domain of networking. With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of existing multiple controllers providing the SDN functionalities to the network, one of the stellar controllers is Floodlight Controller. This paper is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Floodlight Controller and iPerf. Floodlight Controller is tested in the simulation environment by observing throughput and latency parameters of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes. 2017 IEEE. -
Scalability of software defined network on floodlight controller using OFNet
Software Defined Network is the thriving area of research in the realm of networking. With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the competence and applicability to hold up the fault tolerance of the solution offered in the form of SDN Controllers. Out of the accessible multiple controllers with enabled the SDN functionalities to the network infrastructure, one of the best choice in controllers is Floodlight Controller. This research article is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing dual scenarios implemented, experimented and analyzed on the emulation tool of OFNet. Floodlight Controller is tested in the emulation environment by observing eight different parameters of the controller and checked its performance in scalable networking conditions over linear topology by gradually increasing the number of nodes. 2017 IEEE. -
Ryu controller's scalability experiment on software defined networks
Software defined networks is the future of Computer networks which claims that traditional networks are getting replaced by SDN. Considering the number of nodes everyday connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution as SDN to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of the existing multiple controllers providing the SDN functionalities to the network, one of the basic controllers is Ryu Controller. This paper is a contribution towards performance evaluation of scalability of the Ryu Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Ryu Controller and iPerf. Ryu Controller is tested in the simulation environment by observing throughput of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes until it supported tested on high end devices. 2018 IEEE. -
A Novel Technique for Magnetic Particle Separation Using Current-Carrying Slotted Plate
In this paper, a novel method for separating and trapping different magnetic particles is presented. Changes in the current-carrying structure yield disturbing the generated magnetic field. Here, slots were innovatively crafted on the current-carrying plate positioned beneath the microchannel, resulting in a non-uniform magnetic field distribution. This breakthrough enables the separation of different particle types using a constant and low electric current for the very first time, leading to a significant advancement in the field. More importantly, this proposed technique offers several advantages, including the generation of low levels of current and heat, ease of construction, and the ability to control the magnetic field produced by the electric current. In this study, the capability to effectively separate various particle types using a constant electric current was demonstrated with a remarkable separation efficiency of about 100%. By applying a 100[mA] electric current to the plate that carries electric current, the separation of two particle types M-450 and M-280 was achieved at a velocity of 2[?m/s]. 2024 IEEE. -
One Time Password-Based Two Channel Authentication MechanismUsing Blockchain
Using Fog Nodes, also known as IOT devices are increasing everyday with more and more home automation, industry automation, automobile automation, etc. Security threats for these devices are also increasing. One of the threats is impersonating one fog node, stealing data and taking control of the network which is also known as the Sybil attack. To provide security, most fog devices use one step or two step authentication and sometimes use encryption. With static passwords, there is a chance of compromise by password sharing and leaking. Some weak encryption algorithms used are also compromised. Data about fog nodes in the network is stored in a weak database and is tampered. OTP-based Two Channel Authentication Mechanism (OTPTAM) to authenticate the fog nodes with metadata stored in Blockchain Database and communicate using channels encrypted with Elliptical Ciphers can solve the majority of these problems. Metadata of the nodes like Bluetooth MAC address, network mac address, telephone number are all stored in the blockchain and the OTP is exchanged via these channels to ensure the authenticity of the fog nodes. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE. -
Depiction ofNifty Midcap Index Efficiency Using ARIMA
In recent years, the desirability of midcaps in Indian stock markets has received considerable attention from researchers, academicians, and financial analysts due to expectation of multi-bagger returns. The present study is undertaken to determine the market efficiency of Indian stock market using Nifty Midcap Index at High Frequency. The market efficiency of Nifty Midcap Index is determined using ARIMA technique. The fitted ARIMA model had a MASE value close to one. Hence, the findings suggest that the Nifty Midcap Index is inefficient. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Digitization of Monuments An Impact on the Tourist Experience with Special Reference to Hampi
The cultural heritage of India offers a deep examination of the country's political and historical evolution. Historical structures and monuments are among a nation's most valuable assets and a source of pride for Indian civilization. Monuments hold significant historical importance and exert a profound emotional influence on the community. Given the deterioration of culturally significant heritage monuments caused by factors such as weather, climate change, and human activity, as well as the threats these elements pose to numerous heritage sites of national and international significance, it is imperative to prioritize the recording, preservation, and conservation of these monuments. Events of cultural significance require comprehensive digital documentation and proper recording. As demonstrated by various programs and initiatives led by Prime Minister Narendra Modi, the government is committed to enhancing the visitor experience at monuments and museums. The primary aim of the current study is to better understand how cultural heritage sites are digitized and to assess the implications of this process for enhancing the tourist experience. To address the research objectives, a survey was conducted to analyze digital requirements. The digitization of significant cultural heritage sites is vital for the long-term sustainability of the tourism industry. Many methods will be adapted as resources permit, ensuring the industry's steady growth over time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence
The emergence of Web 3.0 has left very few tag recommendation structures compliant with its complex structure. There is a critical need for newer novel methods with improved accuracy and reduced complexity for tag recommendation, which complies with the Web 3.0 standard. In this paper, we propose KMetaTagger, a knowledge-centric metadata-driven hybrid tag recommendation framework. We consider the CISI dataset as the input, from which we identify the most informative terms by applying the Term Frequency - Inverse Document Frequency (TF-IDF) model. Topic modeling is done by Latent Semantic Indexing (LSI). A heterogeneous information network is formalized. Apart from this, the Metadata generation quantifies the exponential aggregation of real-world knowledge and is classified using Gated recurrent units(GRU). The Color Harmony algorithm filters out the initial feasible solutions into optimal solutions. This advanced solution set is finalized into the tag space. These tags are recommended along with the document keywords. When the suggested KMetaTagger's performance is compared to that of baseline techniques and models, it is found to be far superior. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management
Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. The annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately. 2023 IEEE. -
Classification of Disaster Tweets using Machine Learning and Deep Learning Techniques
Social networks provide a plethora of information for gathering extra data on people's behavior, trends, opinions, and feelings during human-affecting occurrences, such as natural catastrophes. Twitter is an inevitable communication medium during calamities. People mainly depend on Twitter to announce real-time emergencies. However, it is rarely straightforward if someone is declaring a tragedy. Sentiment analysis of disaster tweets aid in situational awareness and realizing the disaster dynamics. In our paper, we perform a sentimental analysis of disaster tweets using techniques based on machine learning and deep learning. The tweets are pre-processed before being converted into a structured form using Natural Language Processing (NLP) methods. Supervised learning techniques such as the Support Vector Machine and the Naive Bayes Classifier algorithm are used to develop the Classifier, which categorizes tweets into distinct catastrophes and selects the most appropriate algorithm. The chosen algorithm is further enriched with an emoticon detection algorithm for explicit elucidation. Our research would help disaster relief organizations and news agencies to conclude about the state of affairs and do the needful. 2022 IEEE. -
Extremal Trees oftheReformulated andtheEntire Zagreb Indices
The first reformulated Zagreb index of trees can take any even positive integer greater than 8, whereas the second reformulated Zagreb index of trees can take all positive integers greater than 47 with a few exceptional values less than 8 and 47, respectively. The entire Zagreb index is defined in terms of edge degrees and incorporates the idea of intermolecular forces between atoms along with atoms and bonds. This intricate significance of studying the entire Zagreb index led to the generalization of the first entire Zagreb index of trees. The inverse problem on the first entire Zagreb of trees gives the existence of a tree for any even positive integer greater than 46. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Sustainable Assessment of Advanced Machine Intelligence in Clinical Safety
There is growing acknowledgment that artificial intelligence (AI) is being used to evaluate complex and vast volumes of data, producing findings without human input, in a variety of healthcare contexts, including image analysis, bioinformatics and genomics. Although this technology can offer opportunities in the diagnostic and therapeutic process, various safety-related difficulties and traps can still exist. To shed light on these opportunities and challenges, this article addresses the use of AI in healthcare and its security consequences. To deliver safer technology through AI, this research explores the cost implications of all potential technological systems, while design safety, failure safety, procedural security, and safety margins are the primary methods for identifying risks & uncertainties. Additionally, the suggestion involves the identification and distribution of explicit instructions and procedures to all relevant parties, aiming to facilitate the creation and implementation of safer Al applications within healthcare settings. 2023 IEEE. -
Augmented Reality based Navigation for Indoor Environment using Unity Platform
This paper proposes an augmented reality (AR) navigation system developed for indoor environment. The proposed navigation system is developed using Unity platform which is usually used for developing gaming applications. The proposed navigation system without the aid of Global Positioning System (GPS) tracks users position and orientation accurately by making use of computer vision and image processing techniques. The user can navigate to the desired location using its user friendly and intuitive interface. The proposed system can be extended further to provide indoor navigational guidance within lager buildings such as malls, airports, universities and medical facilities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
7Li Photodisintegration withCircularly Polarized Photons
The study of photodisintegration of 7Li is of importance to Nuclear Physics, Particle Physics and Astrophysics. Primordial abundances of light elements such as D, 3He, 4He and 7Li are predicted by Big Bang theory of early universe and is of great interest to cosmologists. Lithium, being fragile gets destroyed easily at relatively low temperatures. WMAP measurements have inferred that 7Li abundance is two to three times more than that inferred by the low metallicity halo stars [1]. In recent years based on lithium isotopes series of experimental measurements are being carried out using High-Intensity Gamma-Ray Source (HIGS) at Duke Free Electron Laser Laboratory. Experiments [2, 3] were carried out, to measure the differential cross-section of the photoneutron reaction channel in photodisintegration of 7Li, where the progeny nuclei is in the ground state as well as in excited states. Theoretical study on photodisintegration of deuteron was carried out using a model-independent formalism [47] and in these studies, it was shown clearly that there could be 3 different E1? amplitudes leading to final relative n-p state. Subsequently, evidence for the existence of these three amplitudes was found in experimental studies [6] at slightly higher energies in different contexts. Using the same approach, model-independent formalism was developed for photodisintegration of 7Li [8] and an analysis was carried out to study the differential cross section with linearly polarized photons. Extending this study we propose to discuss the reaction channel 7Li+??6Li+n with initially circularly polarized photons. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Model independent approach to photodisintegration of 7Li at the range of energies of interest to BBN
One of the elements that was synthesized primordially in the standard Big Bang Nucleosynthesis is lithium. Lithium, being fragile gets easily destroyed at relatively low temperatures in the mixing process between stellar surface and hot internal layers. So that, at the end of the stellar lifetime the lithium content is believed to be depleted. Series of experimental measurements on lithium isotopes were carried out at High Intensity Gamma Ray Source (HIGS) at Duke Free Electron Laser Laboratory. More recently experiments [1]-[2] were performed, to measure the differential cross section of the photo-neutron reaction channel in photodisintegration of 7Li, where the progeny nuclei is in the ground state as well as in excited states. The purpose of present contribution is to study the reaction channel 7Li + ? ? 6Li + n using linearly polarized photons.The model independent irreducible tensor formalism [3]-[5] will be used to study the differential cross section of the reaction. We study the angular dependence of differential cross section by expressing differential cross section in terms of legendre polynomials. In view of the several theoretical and ongoing experimental studies, a detailed theoretical study of the spin structure of the amplitudes in 7Li+ ? ? 6Li+ n and their expansion in terms of'electric' and 'magnetic' amplitudes is needed to analyze the measurements of spin observables as well as differential cross section, which leads to a better understanding of the problem at astrophysical energies. 2022 Institute of Physics Publishing. All rights reserved.