bluuebunny/crossref_metadata_2025 huggingface dataset:
Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm
Authors: Kadam Sanjay Shahaji , Deshmukh Jotiram Krishna , Gund Ankush Madhukar , Nikam Sudam Vasant , Mane Vijay Balaso , Ingle Dayanand Raghoba
Published: September 2023

The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced with an SDN device in the service provider network. With the moving away from older networks to SDN, hybrid SDN includes both legacy and SDN switches. Existing models of SDN have limitations such as overfitting, local optimal trapping, and poor path selection efficiency. This paper proposed a Deep Kronecker Neural Network (DKNN) to improve its efficiency with a moderate optimization method for multipath selection in SDN. Dynamic resource scheduling is used for the reward function the learning performance is improved by the deep reinforcement learning (DRL) technique. The controller for centralised SDN acts as a network brain in the control plane. Among the most important duties network is selected for the best SDN controller. It is vulnerable to invasions and the controller becomes a network bottleneck. This study presents an intrusion detection system (IDS) based on the SDN model that runs as an application module within the controller. Therefore, this study suggested the feature extraction and classification of contractive auto-encoder with a triple attention-based classifier. Additionally, this study leveraged the best performing SDN controllers on which many other SDN controllers are based on OpenDayLight (ODL) provides an open northbound API and supports multiple southbound protocols. Therefore, one of the main issues in the multi-controller placement problem (CPP) that addresses needed in the setting of SDN specifically when different aspects in interruption, ability, authenticity and load distribution are being considered. Introducing the scenario concept, CPP is formulated as a robust optimization problem that considers changes in network status due to power outages, controller’s capacity, load fluctuations and changes in switches demand. Therefore, to improve network performance, it is planned to improve the optimal amount of controller placements by simulated annealing using different topologies the modified Dragonfly optimization algorithm (MDOA).
Data Quality Optimization for Decision Making Using Ataccama Toolkit: A Sustainable Perspective
Authors: Jamal Aatif , Quadri Mohatesham Pasha , Rafeeq Mohd
Published: September 2023

The world of internet has given us the access to explore the different domains of datasets and its usage. We have lots of heterogeneous data available on the digital platform which is meaningless unless we do not make the valuable use of it. What if we say that we can use these datasets in our need for the business requirements? The critical data can be delivered evenly and shared through master data management and integration techniques. However, given the cross-domain and heterogeneous nature of the data, it is still difficult to assess effectiveness and rationality. In this paper we have developed a pipeline using Ataccama and implemented the way of how we can yield the data, synthesize and optimize it using integration and Master data management (MDM) tools. These tools were assessed based on the performance characteristics and types of data quality problems addressed. We have tried to simplify the complexity and used various dictionaries and lookup along with the ruleset to fetch the required data from the dataset via the MDM application. Profiling the dataset and its validation based on different parameters. In results, it is found that the efficiency and the quality of data has been improved and optimized after using the integration techniques.
Dissection of Mobility Model Routing Protocols in MANET on QoS Criterion
Authors: Sivapriya N , Mohandas R , Kumar Padakanti Kiran , Vaigandla Karthik Kumar
Published: September 2023

Essential difficulties in Mobile Ad Hoc Networks (MANET) are routing selection and Quality of Service(QoS) support. Several different approaches have been described in the literature, and a number of performance simulations have been produced, in an attempt to tackle this challenging problem. In this study, we take a close look at the relative merits of several popular routing protocols. In this research, we looked into how changing QoS parameters in tandem with routing protocol choices affected network throughput. Typical measures for measuring network efficiency include average throughput, packet delivery ratio (PDR), average delay, and power usage. NS-3 is used to run the simulations.
An Optimised Shortest Path Algorithm for Network Rotuting & SDN
Authors: Saxena Mohit Chandra , Sabharwal Munish , Bajaj Preeti
Published: September 2023

Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands.
Leveraging Sentiment Analysis for Twitter Data to Uncover User Opinions and Emotions
Authors: Sharma Tushar , Kaushik Priyanka
Published: September 2023

Huge amounts of emotion are expressed on social media in the form of tweets, blogs, and updates to posts, statuses, etc. Twitter, one of the most well-known microblogging platforms, is used in this essay. Twitter is a social networking site that enables users to post status updates and other brief messages with a maximum character count of 280. Twitter sentiment analysis is the application of sentiment analysis to Twitter data (tweets) in order to derive user sentiments and opinions. Due to the extensive usage, we intend to reflect the mood of the general people by examining the thoughts conveyed in the tweets. Numerous applications require the analysis of public opinion, including businesses attempting to gauge the market response to their products, the prediction of political outcomes, and the analysis of socioeconomic phenomena like stock exchange. Sentiment classification attempts to estimate the sentiment polarity of user updates automatically. So, in order to categorize a tweet as good or negative, we need a model that can accurately discern sarcasm from the lexical meaning of the text. The main objective is to create a practical classifier that can accurately classify the sentiment of twitter streams relating to GST and Tax. Python is used to carry out the suggested algorithm.
Keyless Security
Authors: Thopate Kaushalya , Shinde Sandip , Mahajan Rupali , Bhagat Rajkumar , Joshi Piyush , Kalbhor Akshata , Kulkarni Amey , Jadhav Swayam
Published: September 2023

This research paper introduces a novel approach to improve security and access control in smart door lock systems. The proposed system incorporates multi-factor authentication, combining a numerical keypad, mobile app, and RFID sensor. Each authentication method is associated with a specific lock, offering differentiated access control. Notably, the system requires the simultaneous use of the mobile app and RFID sensor to unlock a designated lock, adding an extra layer of security. To assess the system's effectiveness, a comprehensive study was conducted, considering security, usability, and user satisfaction. The study involved simulated attacks, user surveys, and usability tests, with data analysis providing insights into the system's resilience and user acceptance. This research contributes to the field of smart door lock systems by offering an innovative approach to enhance security through multi-factor authentication. The findings provide valuable insights for designing advanced access control mechanisms, empowering users with increased confidence in their security measures while maintaining a user-friendly experience.
An Approach of Stipulation Change Management Using Cloud Computing
Authors: Suresh Y. , Prasad D Durga , Srinivasarao Nidamanuru , Nagendram Sanam , Tinnaluri V S Narayana
Published: September 2023

Every technology project's successful implementation depends on the requirements. Changes in stipulations at any point of the software development life cycle are considered a healthy operation. Nevertheless, this transition is a little simpler in a co-located setting than in a decentralized system in which participants are spread over more than one area. This presents numerous challenges, such as coordination, communication & control, effective and efficient management of changes and the management of central repositories. Cloud computing can therefore be used to mitigate these stakeholder problems. We used a case study to test the system of cloud computing.
Heart Disease Prediction Using Ensemble Model and Hyperparameter Optimization
Authors: Ivan Jonathan , Prasetyo Simeon Yuda
Published: September 2023

Heart disease is a major global health concern that responsible for significant mortality rates, killing 17.9 million people each year on average. To overcome this problem, machine learning can assist in forecasting the occurrence of heart disease, aiding in its prevention and treatment. This paper explores several classification models to forecast heart disease. This paper also utilizes the hyperparameter tuning method via grid search cv to enhance the accuracy of the models. Finally, the experiment concludes with an ensemble vote on all hyperparameter-tuned classification models. The x-gradient boost and random forest classifier deliver the best outcomes, with an accuracy of 88.04% and 89.13% before hyperparameter optimization, and 92.39% after hyperparameter optimization. These results show that machine learning models are capable of forecasting the risk of heart disease. These models may assist healthcare professionals in identifying individuals at risk of heart disease, enabling preventative measures to be taken. It is essential to note that this study focuses solely on classification models and may not represent the entire population. Further research is required to determine the predictability of heart disease in diverse populations.
Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy
Authors: Venkatarathinam R. , Sivakami R. , Christodoss Prasanna Ranjith , T R Mahesh , Mohan E. , Kumar V Vinoth
Published: September 2023

Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD.
Cardiovascular Disorder Detection with a PSO-Optimized Bi-LSTM Recurrent Neural Network Model
Authors: Singirikonda Bhagyalaxmi , Srivenkatesh Muktevi
Published: September 2023

The medical community is facing ever-increasing difficulties in identifying and treating cardiovascular diseases. The World Health Organization (WHO) reports that despite the availability of numerous high-priced medical remedies for persons with heart problems, CVDs continue to be the main cause of mortality globally, accounting for over 21 million deaths annually. When cardiovascular diseases are identified and treated early on, they cause far fewer deaths. Deep learning models have facilitated automated diagnostic methods for early detection of these diseases. Cardiovascular diseases often present insidious symptoms that are difficult to identify in a timely manner. Prompt diagnosis of individuals with CVD and related conditions, such as high blood pressure or high cholesterol, is crucial to initiate appropriate treatment. Recurrent neural networks (RNNs) with gated recurrent units (GRUs) have recently emerged as a more advanced variant, capable of surpassing Long Short-Term Memory (LSTM) models in several applications. When compared to LSTMs, GRUs have the advantages of faster calculation and less memory usage. When it comes to CVD prediction, the bio-inspired Particle Swarm Optimization (PSO) algorithm provides a straightforward method of getting the best possible outcomes with minimal effort. This stochastic optimization method requires neither the gradient nor any differentiated form of the objective function and emulates the behaviour and intelligence of swarms. PSO employs a swarm of agents, called particles, that navigate the search space to find the best prediction type.This study primarily focuses on predicting cardiovascular diseases using effective feature selection and classification methods. For CVD forecasting, we offer a GRU model built on recurrent neural networks and optimized with particle swarms (RNN-GRU-PSO). We find that the proposed model significantly outperforms the state-of-the-art models (98.2% accuracy in predicting cardiovascular diseases) in a head-to-head comparison.
A Comparative Analysis of OLSR Routing Protocol based on PSO and Cuckoo Search Optimization (CSO) in Manets
Authors: Addanki Udaya Kumar , Kumar B. Hemantha
Published: September 2023

New developments in wireless communication have enabled the use of highly efficient and inexpensive wireless receivers in a variety of portable applications. Each node in a mobile network is a mobile device that independently organizes its own connection to the others and manages its own data transmissions. The adaptability, scalability, and cost reduction of mobile networks have attracted considerable attention. Because mobile networks are constantly changing, problems with routing and power usage are common. High error rates, energy limitations, and inadequate bandwidth are just a few of the issues plaguing mobile ad hoc networks. The relevance of routing protocols in dynamic multi-hop networks like Mobile Ad hoc Networks (MANET) has drawn the attention of many scholars. In this paper, we focus on  implementing an OLSR(Optimised Link State  Routing) protocol and evaluates its performance using two optmisation algorithm: Particle Swarm Optimization(OLSR) and Cuckoo Search Optimization (CSO). The simulation result suggests that PSO is superior to both CSO and the conventional OLSR routing technique. We implemented using NS-2 simulator for simulation and NAM for network animation.
Attack Classification and Detection for Misbehaving Vehicles using ML/DL
Authors: Saudagar Saleha , Ranawat Rekha
Published: September 2023

Vehicle ad hoc networks are a crucial component of the next Intelligent Transportation System created to build a reliable and secure connection between various network components to establish a safe and effective transportation network. Because of open nature of VANETs become vulnerable to numerous assaults such forgery, Denial-of-Service (DoS), and false reports, which can ultimately cause traffic jams or accidents The earlier study concentrated on misbehaving vehicles rather than RSUs. Proposed method integrates data from two subsequent BSMs for testing and training by employing machine learning (ML) methods. The framework merges the data from two BSMs in the right manner and utilizes machine learning/Deep learning methodology which identify the running vehicle as a legal or hostile one.
Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection
Authors: Prabha D. Padma , Priscilla C. Victoria
Published: September 2023

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions.
Comparative Analysis of Functionality and Aspects for Hybrid Recommender Systems
Authors: Shrivastava Vineet , Kumar Suresh
Published: September 2023

Recommender systems are gradually becoming the backbone of profitable business which interact with users mainly on the web stack. These systems are privileged to have large amounts of user interaction data used to improve them.  The systems utilize machine learning and data mining techniques to determine products and features to suggest different users correctly. This is an essential function since offering the right product at the right time might result in increased revenue. This paper gives focus on the importance of different kinds of hybrid recommenders. First, by explaining the various types of recommenders in use, then showing the need for hybrid systems and the multiple kinds before giving a comparative analysis of each of these. Keeping in mind that content-based, as well as collaborative filtering systems, are widely used, research is comparatively done with a keen interest on how this measures up to hybrid recommender systems.
Evaluating Text-to-Image GANs Performance: A Comparative Analysis of Evaluation Metrics
Authors: Kumar K. Dinesh , Srang Sarot , Valy Dona
Published: September 2023

Generative Adversarial Networks (GANs) have emerged as powerful techniques for generating high-quality images in various domains but assessing how realistic the generated images are is a challenging task. To address this issue, researchers have proposed a variety of evaluation metrics for GANs, each with its own strengths and limitations. This paper presents a comprehensive analysis of popular GAN evaluation metrics, including FID, Mode Score, Inception Score, MMD, PSNR, and SSIM. The strengths, weaknesses, and calculation processes of these metrics are discussed, focusing on assessing image fidelity and diversity. Two approaches, pixel distance, and feature distance, are employed to measure image similarity, while the importance of evaluating individual objects using input captions is emphasized. Experimental results on a basic GAN trained on the MNIST dataset demonstrate improvement in various metrics across different epochs. The FID score decreases from 497.54594 at Epoch 0 to 136.91156 at Epoch 100, indicating improved differentiation between real and generated images. In addition, the Inception Score increases from 1.1533 to 1.6408, reflecting enhanced image quality and diversity. These findings highlight the effectiveness of the GAN model in generating more realistic and diverse images with training progression.  However, when it comes to evaluating GANs on complex datasets, challenges arise, highlighting the need to combine evaluation metrics with visual inspection and subjective measures of image quality. By adopting a comprehensive evaluation approach, researchers can gain a deeper understanding of GAN performance and guide the development of advanced models.
A Comprehensive Survey of Automatic Dysarthric Speech Recognition
Authors: Pophale Chandarani , Chavan Shankar
Published: September 2023

Automatic dysarthric speech recognition (DSR) is very crucial for many human computer interaction systems that enables the human to interact with machine in natural way. The objective of this paper is to analyze the literature survey of various Machine learning (ML) and deep learning (DL) based dysarthric speech recognition systems (DSR). This article presents a comprehensive survey of the recent advances in the automatic Dysarthric Speech Recognition (DSR) using machine learning and deep learning paradigms. It focuses on the methodology, database, evaluation metrics and major findings from the study of previous approaches.The proposed survey presents the various challenges related with DSR such as individual variability, limited training data, contextual understanding, articulation variability, vocal quality changes, and speaking rate variations.From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR.
Deep Reinforcement Learning DDPG Algorithm with AM based Transferable EMS for FCHEVs
Authors: Wankhede Yogesh , Rana Sheetal , Kazi Faruk
Published: September 2023

Hydrogen fuel cell is used to run fuel cell hybrid electrical vehicles (FCHEVs). These FCHEVs are more efficient than vehicles based on conventional internal combustion engines due to no tailpipe emissions. FCHEVs emit water vapor and warm air. FCHEVs are demanding fast dynamic responses during acceleration and braking. To balance dynamic responsiveness, develop hybrid electric cars with fuel cell (FC) and auxiliary energy storage source batteries. This research paper discusses the development of an energy management strategy (EMS) for power-split FC-based hybrid electric cars using an algorithm called deep deterministic policy gradient (DDPG) which is based on deep reinforcement learning (DRL). DRL-based energy management techniques lack constraint capacity, learning speed, and convergence stability. To address these limitations proposes an action masking (AM) technique to stop the DDPG-based approach from producing incorrect actions that go against the system's physical limits and prevent them from being generated. In addition, the transfer learning (TL) approach of the DDPG-based strategy was investigated in order to circumvent the need for repetitive neural network training throughout the various driving cycles. The findings demonstrated that the suggested DDPG-based approach in conjunction with the AM method and TL method overcomes the limitations of current DRL-based approaches, providing an effective energy management system for power-split FCHEVs with reduced agent training time.
Skin Cancer Detection using CNN (VGG16) inculcated with CLAH and Gaussian Filter
Authors: Mallick Shweta , Mishra S P
Published: September 2023

Many techniques related to image analysis have been proposed by researchers which are being used to detect a large number of diseases.  These images are carefully analyzed by radiologists and doctors, and after careful interpretation, the results are obtained which finally help in making an appropriate diagnosis. This is a complicated and time consuming task, which requires high levels of concentration.  Therefore, the experts who analyze the images mustn't suffer from fatigue or other common problems that can impair their performance. The present study attempts to reveal how a deep learning model using CNN with VGG16 is effective for the diagnosis and detection of skin cancer at its early stages. Therefore under the scheme, the 4000 images of raw skin cancer tissues are evaluated. The diagnostic model starts with pre-processing of images using the CLAHE along with the inculcation of the Gaussian filter. Thereafter, using hyper-parameter optimizer stochastic gradient descent, along with the effective learning rate 0.001, incorporating the training epochs of 50 nos. and pertaining the batch size 32 is formed. Consequently, as a result, the accuracy achieved is 99.70%, with a loss value of 0.0055%, a precision of 99.75%, a recall of 99.75%, and an f1-score of 99.50% respectively.
Diagnosis of Rice Diseases using Canny Edge K-means Clustering and Convolutional Neural Network based Transfer Learning
Authors: Seelwal Pardeep , Rohilla Tilak Raj
Published: September 2023

Recent breakthroughs in deep learning-based convolutional neural networks have significantly improved image categorization accuracy. Deep learning-based techniques for diagnosing illnesses from rice plant images have been created in this work, inspired by the realisation of CNNs in image classification. Smart monitoring technologies for the automatic identification of plant diseases are extremely beneficial to sustainable agriculture. Despite the fact that various mechanisms for plant disease categorization have been created in recent years, an inefficient technique based on evidence from picture samples is of concern for ground environments. In this study, an image processing technique for pre-processing and segmentation was used, as well as a multi-class convolutional neural network with transfer learning, to classify rice plant leaf diseases such as brown spot, hispa, leaf blast, and healthy class. The contaminated area was automatically separated from the healthy areas of the image using canny edge detection and k-means clustering, and the features were retrieved using the CNN model. In the experimental results, the CNN model without transfer learning is compared to the transfer learning model. VGGNet transfer learning is used to construct a multi-classification framework for each class of rice illness. The overall accuracy acquired by the CNN model without transfer learning is 92.14%, whereas the accuracy obtained by the transfer learning model is 94.80%.The current work demonstrates that the proposed technique is compelling and capable of recognizing rice plant illness for four classes.
Security Aware Virtual Machine Allocation Policy to Improve QoS
Authors: Bhonde Aparna Prashant , Devane Satish
Published: September 2023

Cloud service providers find managing the energy consumption for datacentres as a critical operation. Significant energy is being used by a rising spike in the number of data centres. To overcome this challenge datacentres, attempt to reduce the number of active physical servers by carrying out virtual machine consolidation process. However, due to inadequate security measures to verify hostile cloud users, the security threats on cloud multitenancy platform have escalated.  In this paper we propose energy efficient virtual machine consolidation using priority-based security aware virtual machine allocation policy to improve datacentre security. The proposed security solution considers the host threat score before virtual machine placement, which has reduced the security threats for co-residency attacks without impacting datacentre energy consumption.
An Adaptive Technique to Predict Heart Disease Using Hybrid Machine Learning Approach
Authors: Rao V. Chandra Shekhar , Pavani Gurrapu , Srinivas C.
Published: September 2023

cardiovascular disease is amongby far prevalent fatalities in today's society. Cardiovascular disease is extremely hard to predict using clinical data analysis. Machine learning (ML) hasproved to be useful for helping in judgement and predictions with the enormous amount data produced by the healthcare sectorbusiness. Furthermore, latest events in other IoT sectors have demonstrated that machine learning is used (IOT). Several studies have examined the use of MLa heart disease prediction. In this research, we describe a novel method that, by highlighting essential traits, can improvethe precision of heart disease prognosis. Numerous data combinations and well-known categorization algorithms are used to create the forecasting models. Using a decent accuracy of 88.7%, we raise the level of playusing a heart disease forecasting approach that incorporates a88.7% absolute certainty in a combination random forest and linear model. (HRFLM).
Secured Framework for Smart Farming in Hydroponics with Intelligent and Precise Management based on IoT with Blockchain Technology
Authors: Mehare Jayant , Gaikwad Amit
Published: September 2023

Hydroponics is a type of soil-free farming that uses less water and other resources than conventional soil-based farming methods. However, due to the simultaneous supervision of multiple factors, nutrition advice, and plant diagnosis system, monitoring hydroponics farming is a difficult task. Hydroponic techniques utilizing the IoT show to deliver the finest outcomes, despite the usage of various artificial culture methods. Though, the usage of smart communication technologies and IoT exposes environments for smart farming to a wide range of cybersecurity risks and weaknesses. However, the adoption of intelligence-based controlling algorithms in the agricultural industry is a good use of current technical advancements to address these issues. This paper presented a secured framework for smart farming in hydroponics system. The proposed architecture is characterized into four-layer IoT based framework, sensor, communication, fog and cloud layer. Data analytics is performed using supervised machine learning techniques with intelligent and precise management and is applied at the fog layer for efficient computation over the cloud layer. The data security over channel is protected by using Blockchain Technology. The experimental results are evaluated and analyzed for several statistical parameters in order to improve the system efficacy.
An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases
Authors: Karumanchi Mani Deep , Sheeba J. I. , Devaneyan S. Pradeep , Kodavali Lakshminarayana
Published: September 2023

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases.
An Integrated Framework for the Detection of Lung Nodules from Multimodal Images Using Segmentation Network and Generative Adversarial Network Techniques
Authors: C. Vinod , D. Menaka
Published: September 2023

Medical imaging techniques are providing promising results in identifying abnormalities in tissues. The presence of such tissues leads to further investigation on these cells in particular. Lung cancer is seen widely and is deadliest in nature if not detected and treated at an early stage. Medical imaging techniques help to identify the presence of suspicious tissues like lung nodules effectively. But it is very difficult to know the presence of the nodule at an early stage with the help of a single imaging modality. The proposed system increases the efficiency of the system and helps to identify the presence of lung nodules at an early stage. This is achieved by combining different methods for reaching a common outcome. Multiple schemes are combined and the extracted features are used for obtaining a conclusion. The accuracy of the system and the results depend on the quality and quantity of the authentic training data. But the availability of the data from an authentic source for the study is a challenging task. Here the generative adversarial network (GAN), is used as a data source generator. It helps to generate a huge amount of reliable data by using a minimum number of real time and authentic data set. Images generated by the GAN are of resolution 1024 x 1024.Fine tuning of the images by using the real images increases the quality of the generated images and thereby improving the efficiency.   Luna 16 is the primary data source and these images are used for the generation of 1000000 images. Training process with the huge dataset improves the capability of the proposed system. Various parameters are considered for evaluating the performance of the proposed system. Comparative analysis with existing systems highlights the strengths of the proposed system.
Hybrid Dynamic Source Routing Technique and Security Implementation in Adhoc Network Topology
Authors: Patnala Eswar , Giduturi Srinivasa Rao
Published: September 2023

Routing protocols that are developed with the error-prone environment and resource constraints of mobile nodes in mind perform better in wireless Ad Hoc environments. A good and dependable routing method is required to meet the packet transition aim. When the network is small, most existing routing algorithms operate fine, but when the network is large, there is a problem with link breakage in multipath routing algorithms, especially on demand routing algorithms. As a result, we investigated many forms of routing protocols in order to identify all of the existing algorithms' flaws. In this study, we examine existing protocols in terms of network factors such as delay, throughput, energy consumption, control overhead, and so on.
Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning
Authors: Deshpande Guruprasad , P. K. Rajani , Khandagle Vishal , Kolhe Jayesh
Published: September 2023

Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning.
Machine Learning Based Fluid-Transportation Monitoring and Controlling
Authors: Bhoite Kiran , Bhosle S. M. , Mirajkar Riddhi , More Rahul , Kolekar Sachin
Published: September 2023

The discipline of fluid mechanics is developing quickly, propelled by previously unheard-of data volumes from experiments, field measurements, and expansive simulations at various spatiotemporal scales. The field of machine learning (ML) provides a plethora of methods for gleaning insights from data that can be used to inform our understanding of the fluid dynamics at play. As an added bonus, ML algorithms can be used to automate duties associated with flow control and optimization, while also enhancing domain expertise. This article provides a review of the background, current state, and potential future applications of ML in fluid mechanics. We provide an introduction to the most fundamental ML approaches and describe their applications to the study, modelling, optimization, and management of fluid flows. From the standpoint of scientific inquiry, which treats data as an integral aspect of modelling, experiments, and simulations, the benefits and drawbacks of these approaches are discussed. Since ML provides a robust information-processing framework, it can supplement and potentially revolutionize conventional approaches to fluid mechanics study and industrial applications.
Transfer and Ensemble Approach for Breast Cancer Detection and Classification Using Deep Learning
Authors: Shelke M. V. , Deshmukh Jyoti Yogesh , Ajalkar Deepika Amol , Dhumale R. B.
Published: September 2023

Breast cancer is a serious disease that can cause significant health problems for women worldwide. It is crucial to detect and classify breast cancer early stage so that doctors can promptly treat it and aid patients in their recovery. Many investigators have used various deep learning (DL) strategies to detect and classify breast cancer. However, due to the complexity of the problem, relying on a single DL model may not suffice to achieve high accuracy. Therefore, this study suggests a transfer and ensemble deep model for breast cancer detection and classification. The suggested model involves using pre-trained models such as Sequential, Xception, DenseNet201, VGG16, and InceptionResNetV2. The top three models are selected to collaborate and deliver the most accurate results. The proposed DL model was tested on publicly available breast BUSI datasets, demonstrating its superiority over single DL models, achieving an accuracy of 87.9% on the BUSI dataset. Additionally, the model proved to be adapTABLE to different amounts of data, making it potentially valuable in hospitals and clinics.
LFM based Wideband DOA Estimation using Deep Neural Network at Low SNR
Authors: L Nagaraju , Theja ch Lokesh Dharma , Kumar Puli Kishore
Published: September 2023

This work focuses on deep learning-based wideband direction-of-arrival (DoA) estimation for a wideband in particular LFM in case of extreme noise. We propose a convolutional neural network (CNN) that utilizes the correlation matrix to estimate and trained using multi-channel data in low SNR conditions. By using a systematic approach and treating the problem as a way to identify multiple possible DoAs, the CNN is trained to predict DoAs under different SNR conditions. This allows the CNN to accurately estimate the directions from which signals are coming, regardless of the level of noise in the environment. The architecture proposed exhibits robustness to noise, works effectively with a small number of snapshots, and achieves high resolution in angle estimation. Experimental findings demonstrate notable enhancements in performance under low SNR conditions when compared to existing methods, without the need for parameter tuning for correlated and uncorrelated sources. The enhanced robustness of our solution has broad applications in various fields, including wireless array sensors, acoustic microphones, and sonars.
Deep Learning Multi-Agent Model for Phishing Cyber-attack Detection
Authors: Kaushik Priyanka , Rathore Saurabh Pratap Singh
Published: September 2023

Phishing attacks have become one of the most prominent cyber threats in recent times, which poses a significant risk to the security of organizations and individuals. Therefore, detecting such Cyber attacks has become crucial to ensure a secure digital environment. In this regard, deep learning techniques have shown promising results for the detection of phishing attacks due to their ability to learn and extract features from raw data. In this study, we propose a deep learning-based approach to detecting phishing attacks by using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our proposed model extracts features from the URL and email content to detect phishing attempts. We evaluate the proposed approach on a real-world dataset and achieve an accuracy of over 95%. The results indicate that the proposed approach can effectively detect phishing attacks and can be utilized in real-world applications to ensure a secure digital environment.
Comprehensive Study of Automatic Speech Emotion Recognition Systems
Authors: Kawade Rupali , Jagtap Sonal
Published: September 2023

Speech emotion recognition (SER) is the technology that recognizes psychological characteristics and feelings from the speech signals through techniques and methodologies. SER is challenging because of more considerable variations in different languages arousal and valence levels. Various technical developments in artificial intelligence and signal processing methods have encouraged and made it possible to interpret emotions.SER plays a vital role in remote communication. This paper offers a recent survey of SER using machine learning (ML) and deep learning (DL)-based techniques. It focuses on the various feature representation and classification techniques used for SER. Further, it describes details about databases and evaluation metrics used for speech emotion recognition.
CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs
Authors: Misal Ashish Kumar , Choubey Abha , Choubey Siddharth , Pandey Anil Kumar
Published: September 2023

This Gait identification assists in recognition of human body components from temporal image sequences. Such components consist of connected-body entities including head, upper body, lower body regions. Existing Gait recognition models use deep learning methods including variants of Convolutional Neural Networks (CNNs), Q-Learning, etc. But these methods are either highly complex, or do not perform well under complex background conditions. Moreover, most of these models are validated on a specific environmental condition, and cannot be scaled for general-purpose deployments. To overcome these issues, this text proposes design of a novel cross-domain bioinspired model for identification of gait components via Iterated Generative Adversarial Networks (IGANs). The proposed model initially extracts multidomain pixel-level feature sets from different images. These include frequency components via Fourier analysis, entropy components via Cosine analysis, spatial components via Gabor analysis, and window-based components via Wavelet &Convolutional analysis. These feature sets are processed via a Grey Wolf Optimization (GWO) Model, which assists in identification of high-density & highly variant features for different gait components. These features are classified via an iterated GAN, which comprises of Generator & Discriminator ssModels that assist in evaluating connected body components. These operations generate component-level scores that assist in identification of gait from complex background images. Due to which, the proposed model was observed to achieve 9.5% higher accuracy, 3.4% higher precision, and 2.9% higher recall than existing gait identification methods. The model also uses iterative learning, due to which its accuracy is incrementally improved w.r.t. number of evaluated image sets.
Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural Networks
Authors: Saxena Akash , Johri Prashant , Ihechiluru Winner Chukwuemeka , Sharma Vivek , Rathore Megha , Yadav Dharmendra
Published: September 2023

In this research, we provide a state-of-the-art method for semantic segmentation that makes use of a modified version of the U-Net architecture, which is itself based on deep convolutional neural networks (CNNs). This research delves into the ins and outs of this cutting-edge approach to semantic segmentation in an effort to boost its precision and productivity. To perform semantic segmentation, a crucial operation in computer vision, each pixel in an image must be assigned to one of many predefined item classes. The proposed Improved U-Net architecture makes use of deep CNNs to efficiently capture complex spatial characteristics while preserving associated context. The study illustrates the efficacy of the Improved U-Net in a variety of real-world circumstances through thorough experimentation and assessment. Intricate feature extraction, down-sampling, and up-sampling are all part of the network's design in order to produce high-quality segmentation results. The study demonstrates comparative evaluations against classic U-Net and other state-of-the-art models and emphasizes the significance of hyperparameter fine-tuning. The suggested architecture shows excellent performance in terms of accuracy and generalization, demonstrating its promise for a variety of applications. Finally, the problem of semantic segmentation is addressed in a novel way. The experimental findings validate the relevance of the architecture's design decisions and demonstrate its potential to boost computer vision by enhancing segmentation precision and efficiency.