bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Watermarking Using FFT and Cordic QR Techniques
Authors: Vishwakarma Sunil Kumar , Sharma Birendra Kumar , Abbas Syed Qamar
Published: October 2023

Digital media sharing and access in today’s world of the internet is very frequent for every user. The management of digital rights may come into threat easily as the accessibility of data through the internet become wide. Sharing digital information under security procedures can be easily compromised due to the various vulnerabilities floating over the internet. Existing research has been tied to protecting internet channels to ensure the safety of digital data. Researchers have investigated various encryption techniques to prevent digital rights management but certain challenges including external potential attacks cannot be avoided that may give unauthorized access to digital media. The proposed model endorsed the concept of watermarking in digital data to uplift media security and ensure digital rights management. The system provides an efficient procedure to conduct over-watermarking in digital audio signals and confirm the avoidance of ownership of the host data. The proposed technique uses a watermark picture as a signature that has been initially encrypted with Arnold's cat map and cyclic encoding before being embedded. The upper triangular R-matrix component of the energy band was then created by using the Fast Fourier transform and Cordic QR procedures to the host audio stream. Using PN random sequences, the encrypted watermarking image has been embedded in the host audio component of the R-matrix. The same procedure has been applied to extract the watermark image from the watermarked audio. The proposed model evaluates the quality of the watermarked audio and extracted watermark image. The average PSNR of the watermarked audio is found to be 37.01 dB. It has also been seen that the average PSNR, Normal cross-correlation, BER, SSMI (structure similarity index matric) value for the extracted watermark image is found to be 96.30 dB, 0.9042 units, 0.1033 units, and 0.9836 units respectively. Further, the model has been tested using various attacks to check its robustness. After applying attacks such as noising, filtering, cropping, and resampling on the watermarked audio, the watermark image has been extricated and its quality has been checked under the standard parameters. It has been found that the quality of the recovered watermark image satisfying enough to justify the digital ownership of the host audio. Hence, the proposed watermarking model attains a perfect balance between imperceptibility, payload, and robustness.
An Improvements of Deep Learner Based Human Activity Recognition with the Aid of Graph Convolution Features
Authors: Srilakshmi N. , Radha N.
Published: October 2023

Many researchers are now focusing on Human Action Recognition (HAR), which is based on various deep-learning features related to body joints and their trajectories from videos. Among many schemes, Joints and Trajectory-pooled 3D-Deep Geometric Positional Attention-based Hierarchical Bidirectional Recurrent convolutional Descriptors (JTDGPAHBRD) can provide a video descriptor by learning geometric features and trajectories of the body joints. But the spatial-temporal dynamics of the different geometric features of the skeleton structure were not explored deeper. To solve this problem, this article develops the Graph Convolutional Network (GCN) in addition to the JTDGPAHBRD to create a video descriptor for HAR. The GCN can obtain complementary information, such as higher-level spatial-temporal features, between consecutive frames for enhancing end-to-end learning. In addition, to improve feature representation ability, a search space with several adaptive graph components is created. Then, a sampling and computation-effective evolution scheme are applied to explore this space. Moreover, the resultant GCN provides the temporal dynamics of the skeleton pattern, which are fused with the geometric features of the skeleton body joints and trajectory coordinates from the JTDGPAHBRD to create a more effective video descriptor for HAR. Finally, extensive experiments show that the JTDGPAHBRD-GCN model outperforms the existing HAR models on the Penn Action Dataset (PAD).
A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset
Authors: Gupta Nikita , Sinha Ripu Ranjan
Published: October 2023

The software systems of modern computers are extremely complex and versatile. Therefore, it is essential to regularly detect and correct software design faults. In order to devote resources effectively towards the creation of trustworthy software, software companies are increasingly engaging in the practise of predicting fault-prone modules in advance of testing. These software fault prediction methods rely on the thoroughness with which prior software versions' fault as well as related code has been retrievedTime, energy, and money are all saved as a result. Increases the company's initial success and bottom line greatly by satisfying its clientele. Numerous academics have poured into this area throughout the years in an effort to raise the bar for all software. Nowadays, The most often used approaches in this field are those based on machine learning (ML). The field of ML seeks to perfect software capable of evolving as well as adapting in response to fresh data. This paper introduces a fresh approach for doing ML by bringing together a number of different expert systems. In order to reach agreement on which aspects of a software system need to be tested, the proposed multi-classifier model pools the strengths of the most effective classifiers. Several top-performing classifiers for defect prediction are put through their paces in an experiential evaluation. We test our method on 16 publicly available datasets from the NASA Metric Data Programme (MDP) repository at the promise repository. Parameters of confusion, recall, precision, recognition accuracy, etc., are evaluated and contrasted with existing schemes in a software analysis performed with the help of the python simulation tool with findings. The experimental outcomes demonstrate that by combining LGBM, XGBoost, and Voting classifiers, using a multi classifier approach, we are capable to significantly improve software fault prediction performance. The results of the investigation show that the suggested method will lead to better practical outcomes in the prediction of device failures.
An Approach of AlexNet CNN Algorithm Model for Lung Cancer Detection and Classification
Authors: B. C. Kavitha , K. B. Naveen
Published: October 2023

As a reliable tool for identifying and classifying different illnesses, including lung cancer, deep learning has grown significantly in  popularity. It is crucial to quickly and accurately diagnose lung cancer because different treatment options depend on the type and stage of the  disease. Deep learning algorithms (DLA) are used to speed up the critical process of lung cancer detection and lessen the burden on medical  professionals. In this study, the feasibility of employing deep learning algorithms for the early detection of lung cancer is explored, using data  from the Lung Imaging Database Consortium (LIDC) database. The study introduces the VGG-16 and AlexNet models specifically to identify  the presence of cancer in lung images. The AlexNet model is chosen for additional classification tasks based on performance. The suggested  technique displays considerable increases in both the prediction and classification accuracy of cancer. The results from using the AlexNet model  show the highest levels of accuracy, with classification accuracy of 97.76% and prediction accuracy of 97.02%, both verified using a 5-fold  cross-validation method. Moreover, when classifying the forms of cancer, the model gets a remarkable area under the curve (AUC) value of 1  for the Adenocarcinoma class, signaling extraordinary performance. Notably, the proposed model achieves an accuracy exceeding 90% across  all classes.
Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches
Authors: Siddamsetti Swapna , Srivenkatesh Muktevi
Published: October 2023

Background: This paper tackles the critical challenge of detecting fraudulent transactions within the Ethereum blockchain using machine learning techniques. With the burgeoning importance of blockchain, ensuring its security against fraudulent activities is crucial to prevent significant monetary losses. We utilized a public dataset comprising 9,841 Ethereum transactions, characterized by attributes such as gas price, transaction fee, and timestamp.Methods: Our approach is bifurcated into two core phases: data preprocessing and predictive modeling. In the data preprocessing phase, we meticulously process the dataset and extract pivotal features from transactions, setting the stage for efficient predictive modeling.Findings: For predictive modeling, we employed several machine learning algorithms to discern between fraudulent and legitimate transactions. Our evaluation encompassed algorithms like decision trees, logistic regression, gradient boosting, XGBoost, and an innovative hybrid model that melds random forests with deep neural networks (DNN).Novelty: Our findings underscore that the proposed model boasts a precision rate of 97.16%, marking a substantial leap in fraudulent transaction detection on the Ethereum blockchain in comparison to prevailing methodologies. This paper augments the current efforts aimed at bolstering the security of blockchain transactions using sophisticated analytical strategies..
Enhancing Breast Cancer Prediction through Deep Learning and Comparative Analysis of Gene Expression and DNA Methylation Data using Convolutional Neural Networks
Authors: Vivek Bandi , Sangeetha Tupili , Nawaz S. M. Mustafa , Jemina S. Lincy , Nihal S.
Published: October 2023

Recent advances in the production of statistics have resulted in an exponential increase in the number of facts, ushering in a whole new era dominated by very large facts. Conventional machine-learning algorithms are unable to handle the most recent aspects of huge data. This is a fact.  In order to make an accurate prognosis of breast cancer, researchers employ and evaluate three distinct computer programmes called Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). Within the context of huge statistics, we explore the question of how breast cancer may be predicted in this particular research. Gene expression and DNA methylation are both taken into consideration as part of the analysis (GE and DM, respectively). The purpose of the work that we are doing is to increase the capacity of the Deep Learning algorithms that are now being used for typing by applying each dataset individually and together. As a result of this decision, the platform of choice is MATLAB. In the process of breast cancer prediction, the Convolutional Neural Network (CNN) algorithm is used. Comparisons of GE, DM, and GE and DM are carried out with the help of this method. The results of the CNN algorithm are compared to those of the RF algorithm. According to findings of the experiments, the scaled system that was presented works better than the other classifiers. This is due to the fact that using the GE dataset; it acquired the best accuracy at the lowest cost.
Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN
Authors: Vanitha G. , Amudha P.
Published: October 2023

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control  and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving   throughput, but not considering  delay. However, delay is the main factors for trasnmiitng NRT packets.  This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.
Psoriasis Skin Disease Classification based on Clinical Images
Authors: Bhaswanth N. , Sekhar D. Venkata , Reddy M. Purushotham
Published: October 2023

Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performance levels comparable to, if not better than, those of humans. In the paper, a Dermnet dataset is used. Image preprocessing, fuzzy c-mean-based segmentation, MobileNet-based feature extraction, and a support vector machine (SVM) classification are used for skin disease classification. Dermnet's dataset was investigated for images of skin conditions using three classes Psoriasis, Dermatofibroma, and Melanoma are studied. The performance metrics such as accuracy, precision-recall, and f1-score are evaluated and compared for three classes of skin diseases. Despite working with a smaller dataset, MobileNet with Support Vector Machine outperforms ResNet in terms of accuracy (99.12%), precision (98.65%), and recall (99.66%).
Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects
Authors: T Saraswathi , M Karthikeyan , Balakrishnan C. , Nithya T. , Maheswari B. , Subramanian.R Siva
Published: October 2023

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future.
End to End Encrypted Smart Lock Using RSA based on Opinion from Social Media Review Comments
Authors: Revathi S. , Shreedevi Shreedevi , Monica K. M. , G Bindu , Menaga D.
Published: October 2023

In the modern era, from big apartments to small houses, startups to corporate buildings, protecting assets or preventing unauthorized persons are crucial problems. Often traditional locks like padlocks are prone to security risks since they can be easily bypassed. Existing smart lock systems are prone to Man in middle Attacks where digital keys can easily be duplicated. The review comments about prevailing smart locks technologies have been collected from various sources such as blogs and microblogs.  The data set is analyzed to discover the opinion of the people about the smartlock product. In this proposed system, an innovative smartlock system prototype is designed using current technologies. A smart lock system has been proposed which is encrypted end-to-end using the RSA algorithm. This system uses a one-time password sent to registered users combined with the master code to unlock the door. This system is designed as such only the users who are connected to a wireless local area network are able to access the smart lock system, this adds an additional layer of security. It is connected to the cloud and logs all the activity from booting to shutting down. The breach detection system along with image capture is also included to detect forced intrusions. The client functionality can be easily ported to any platform which supports HTTP protocol which tends to be the major advantage of the proposed work.
Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles
Authors: Kayarga Tanuja , Kumar S Ananda
Published: October 2023

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians.  Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics.  In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works.
Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text
Authors: Hussien Omar Kamal Eldien , Aboutabl Amal Elsayed , Haggag Riham Mohamed Younis
Published: October 2023

Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of  Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf  Vectorizer the best to all results than count Vectorizer .
Wave-Atom and Cycle-Spinning-Based Noise Reduction in Mammography Images
Authors: Sarada Ch. , Ashwini P. , Srividya K.
Published: October 2023

Image denoising is crucial in medical image processing. Digital mammography depends significantly on de-noising for computer-aided-detection of malignant cells like Microcalcifications. In this work, we proposed an unique hybrid approach to reduce Gaussian noise in digital mammograms by combining the wave-atom translation and cycle spinning methods. Pictures denoised by thresholding of coefficients would produce pseudo-Gibbs events because wave atoms are not translationally invariant. Circular motion is applied to keep away the artefacts. Experimental results clearly establish that the method is effective at filtering out background noise while maintaining the integrity of edges and enhancing picture quality. Mini-Mias pictures with variable quantities of Gaussian Noise are used to evaluate and analyse the performance using peak signal-to-noise ratio and structural similarity index.  The provided technique outperforms several current filters in terms of evaluated results of peak signal-to-noise ratio and structural similarity index.
Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset
Authors: M Vinayababu , Sreedevi M
Published: October 2023

Market basket analysis (MBA) is a well-liked method for identifying relationships between products that people purchase in a database. It is predicated on association rule mining (ARM), a data mining technique that pulls valuable data from huge databases. Due to consumers using internet applications for online shopping and insurance, an ever-increasing amount of data is generated online. It produces large amounts and, if mined effectively, will greatly benefit society as a whole as well as individuals. So, numerous data science and machine learning-related techniques have been created to gradually unlock the potential. The Clustering of Non-Associated Item Sets (CNAIS) of the Sales dataset used in the Showroom for choosing customers for benefits and web application design is discussed in this study. The CNAIS algorithm implementation process and dataset for this study are discussed.
Spectrum Efficient Cognitive Radio Sensor Network for IoT with Low Energy Consumption
Authors: Jaronde Pravin , Vyas Archana , Gaikwad Mahendra
Published: October 2023

Cognitive Radio Sensor Networks (CRSNs) have emerged as a promising solution for efficient utilization of the limited frequency spectrum. One of the key challenges in CRSNs is achieving spectrum efficiency by avoiding interference and maximizing the use of the available spectrum. Particle Swarm Optimization (PSO) techniques have been widely used to optimize the spectrum allocation and improve the spectrum efficiency of CRSNs. In this paper the study provides an overview of the research on spectrum efficiency in CRSNs using PSO techniques and also discussed the key parameters that affect the spectrum efficiency, such as the swarm size, sensor's threshold and maximum number of iterations and highlights the importance of identifying the optimal combination of these parameters. This paper also emphasizes the need for further research and development in this area to improve the efficiency and effectiveness of PSO-based optimization techniques for CRSNs and to adapt them to various real-world scenarios. Achieving spectrum efficiency in CRSNs is critical for enabling effective wireless communication systems and improving the overall utilization of the available frequency spectrum.
Performance Analysis of Sensing-based Semi-Persistent Scheduling (SB-SPS) MAC Protocol for C-V2X
Authors: Alsisi Rayan Hamza , Hussain Mushahid , Javed Muhammad Awais , Khan Mohammad Zubair , Alahmadi Abdulrahman , Alahmadi Ahmed H.
Published: October 2023

Sensing-based Semi-Persistent Scheduling (SB-SPS) MAC protocol is proposed as part of the latest cellular vehicle to everything (C-V2X) standard for medium access between vehicles. As C-V2X uses LTE based frame structure, mode 4 of the C-V2X standard uses SB-SPS to allocate resource blocks effectively. C-V2X shows great potential for the future as it brings many improvements such as enhanced range, reliability, and the ability to support and evolve with emerging technologies such as 5G. In this article, the SB-SPS protocol’s performance was analyzed in different scenarios using OMNET++, SUMO, and Veins simulator. Different vehicle speeds and densities were used to observe the effect on packet loss and throughput. It was found that as packet loss decreased, throughput increased when the mobility of vehicles decreased. The effects of changing some important parameters of SB-SPS were also observed. The results showed that while parameters such as increasing the number of subchannels increased the packet delivery ratio (PDR), the change in the probability of resource reselection parameter did not affect the PDR.
Automated Video and Audio based Stress Detection using Deep Learning Techniques
Authors: Godse Deepali , Mulla Nilofar , Jadhav Rohini , Gayakwad Milind , Joshi Rahul , Kadam Kalyani , Jadhav Jayashree
Published: October 2023

In today's world, stress has become an undoubtedly severe problem that affects people's health. Stress can modify a person's behavior, ideas, and feelings in addition to having an impact on mental health. Unchecked stress can contribute to chronic illnesses including high blood pressure, diabetes, and obesity. Early stress detection promotes a healthy lifestyle in society. This work demonstrates a deep learning-based method for identifying stress from facial expressions and speech signals.An image dataset formed by collecting images from the web is used to construct and train the model Convolution Neural Network (CNN), which then divides the images into two categories: stressed and normal. Recurrent Neural Network (RNN), which is used to categorize speech signals into stressed and normal categories based on the features extracted by the MFCC (Mel Frequency Cepstral Coefficient), is thought to perform better on sequential data since it maintains the past results to determine the final output.
Exploring Public Sentiment: A Sentiment Analysis of GST Discourse on Twitter using Supervised Machine Learning Classifiers
Authors: Srivastava Anima , Srivastava Amit , Siddiqui Tanveer J.
Published: October 2023

A key economic move that resulted in heated disputes was India's introduction of the Goods and Services Tax (GST). Social media channels offered a widely used forum for the people to express their views on the GST, providing insightful data for gauging mood and guiding next revisions. The emotion of 5629 GST-related tweets was assessed using the VADER lexicon after being obtained using the Twitter Developer API. The tf-idf feature was used for text vectorization, with 80% of the data going toward training and the remaining 20% going toward testing. In this study, six well-known classifiers—the Ridge Classifier, Logistic Regression, Linear SVC, Perceptron, Decision Tree, and K-Nearest Neighbor—were thoroughly compared to evaluate their performance in a range of circumstances. Accuracy, precision, recall, f-score, training, and testing times were all included in the performance measurements. The study presented novel pre-processing methods and examined the training/testing times before coming to the conclusion that the Ridge Classifier performed better than the others in terms of accuracy, precision, and efficiency. In this study, six well-known classifiers—the Ridge Classifier, Logistic Regression, Linear SVC, Perceptron, Decision Tree, and K-Nearest Neighbor—were thoroughly compared to evaluate their performance in a range of circumstances. Accuracy, precision, recall, f-score, training, and testing times were all included in the performance measurements. The study presented novel pre-processing methods and examined the training/testing times before coming to the conclusion that the Ridge Classifier performed better than the others in terms of accuracy, precision, and efficiency.
Performance and Analysis of a U-Net Model for Automated Skin Lesion Segmentation
Authors: Patil Sheetal Nana , Patil Hitendra D.
Published: October 2023

A greater proportion of people are affected by skin cancer, particularly melanoma, which has a higher tendency to metastasize. For Dermatologist, Visual inspections are most challenging & complex task for melanoma detection. To solve this problem, dermoscopic images are analyzed and segmented. Due to the sensitivity involved in surgical operations, existing techniques are unable to achieve higher accuracy. As a result, computer-aided systems are essential to detect & segment dermoscopic images.

    In this paper, for segmentation 5000 skin images were taken from the HAM10000 dataset. Prior to segmentation, preprocessing is done by resizing images. A novel U Net structure is a fully convolutional network is presented & implemented using up-sampling and down-sampling technique with Rectified Linear Units (ReLU) for activation functions. The outcomes of proposed methodology shows performance improvement for skin-lesion segmentation with 94.7 % pixel accuracy & 89.2 % dice coefficient compared with existing KNN & SVM techniques.
Reactive Power Management at PCC with DFIG Based Wind Energy System
Authors: Meda Sreenivasulu , Muni Bishnu Prasad , Kolli Ramesh Reddy
Published: October 2023

Reduction in availability of conventional energy sources and their scarcity in the future has led to the research on adoption of renewable energy sources for electricity generation. Wind is recognized as the prominent renewable energy source for electricity generation. Due to the erratic pattern of wind, variable speed generators such as doubly fed induction generator (DFIG) are preferred over fixed speed wind generators which use synchronous generators. Due to the increase in contribution of electricity generation through renewable energy sources, power quality of the system is deteriorating. In the present paper, a new economical and tangible scheme of DFIG is proposed to compensate for the reactive power at PCC, thus improving the power quality of the grid. The effectiveness of the scheme in compensating for the reactive power is evaluated using MATLAB / Simulink environment.
Optimized Hand Geometry-Based Biometric Recognition System
Authors: Dhole Sampada Abhijit , Patil Jayamala Kumar , Jagdale S.M. , Sakhare Dipti , Gowda V Dankan
Published: October 2023

In an era characterized by digital interactions and security needs, biometric systems, especially hand geometry-based recognition, offer an advantageous solution. Biometric identification through hand geometry is ideal for low-security applications due to its non-invasive nature and user-friendly features. This research discusses personal identification leveraging hand geometry features, notably without the use of pegs. Such features encompass finger length and width, palm dimensions, deviations, and angles. Image capturing was conducted without pegs. The study contrasts the use of 12 versus 21 hand geometry features. Identification was achieved using the Euclidean distance measure. The outcomes were validated on both a local and a standard database.
Unmanned Aerial Vehicle Design for Smart City Application
Authors: Doraswamy Birasalapati , Giriprasad M. N. , Krishna K. Lokesh
Published: October 2023

Nowadays, Unmanned Aerial Vehicles (UAVs) or drones are also one of the applications to provide the required services and to gather information from the target location.  Because smart city applications effectively deal the drone interaction and enhance the human lifestyle with drones. Moreover, UAVs are generally utilized due to their privacy threats, lower cost, pose security, and versatility, which request dependable detection at lower altitudes. However, the less sensing module in the drone has earned the low sensing accuracy of location tracking. So, this paper aims to develop a novel Firefly-based Recurrent Neural Mechanism (FRNM) to enrich the sensing capacity of the drone vehicle. In addition, the sound of the research is medicine delivery through UAVs in emergencies. This UAV system is one of the most crucial features to delivering essential medical items aids by reaching properly correspondent patients.  Moreover, the client's needs are stored in the FRNM cloud then that stored data is trained to the UAV machine. Hereafter, based on the trained details, the drone can reach the destination and has delivered the requested medicine to the specific clients. The planned design is drawn in Network Simulator (NS2) environment, and the robustness of the projected replica is valued by calculating the chief parameters. Hereafter, the improvement score was valued by the comparison assessment. Hence, the FRNM has reported the finest performance by earning less location finding duration, running period, and error rate.
Chaser Priori Wolf (CPW) Optimization an Improved Optimization Technique Video Content Classification and Detection
Authors: Yadav Sita , Chaware Sandeep
Published: October 2023

Optimizers play a crucial role in video object detection by promoting the training and improving the performance of the model. Optimizers are responsible for minimizing the loss function during training. The parameters of models are updated iteratively based on the gradients of the loss parameters. By continuously adjusting the parameters in the direction of the steepest descent, optimizers guide the model towards convergence, reducing the loss and improving the object detection performance. In the proposed paper hybrid optimizer named chaser priori wolf optimizer is proposed. The chaser priori wolf optimization is based on the hybridization of cat swarm optimization and coyote optimization. Well-known optimizers like SGD, ADAM, adagrad, adadelta and RMSprop are used as default optimizers by researchers. The proposed work introduced CPW optimizer which works for classification to improve the convergence and feature selection. The comparative result showed an increase in the performance of CNN based YOLO model. The results are compared concerning sensitivity, specificity and accuracy. Results clearly showed improvement in all performance metrics and the average improvement in comparison with state of art architecture is 10.3%.
Evaluation of Transfer Learning Techniques for Fault Classification in Radial Distribution Systems: A Comparative Study
Authors: Tiwari Garima , Saini Sanju
Published: October 2023

Transfer learning has recently had a detectable impact on the state of the art in a wide variety of applications, and this trend is expected to continue in the near future. Both transfer learning and deep learning algorithms make use of a number of network layers, each of which may be intellectually learned and typically represents the data in a hierarchical fashion with increasing levels of abstraction. Convolutional neural networks have been proven to be exceptionally successful machine learning and deep learning techniques for a number of computer vision problems. These networks were developed by companies such as Alexa, Google, and Squeeze. Fault diagnostic strategies that are based on deep learning techniques are currently a topic of intense investigation due to their higher performance. Using transfer learning technology to carry out fault categorization in a power distribution system in a manner that is both accurate and efficient The work at hand employs a fault classification model for a radial power distribution system that is based on transfer learning and deep learning. Images of time series of three-phase fault currents are acquired via simulation with the assistance of PSCAD software as part of the proposed approach for doing so. In the next step, CNN models that are based on Alex Net, Google Net, and Squeeze Net are utilized to extract fault features from defective photos in order to categorize eleven distinct defects (using the MATLAB platform). For the categorization of defects in a radial distribution system, Alex Net, Google Net, and SqueezNet each offer accuracy of approximately 98.92%, 97.48%, and 99.82%, respectively. In this study, the classification of faults in a distribution system is accomplished with the help of AlexNet, GoogleNet, and SqueezNet. According to the findings of the simulations, the test accuracy for SqueezeNet is the highest it can be, coming in at 99.82%. Because of this, selecting it as the solution to the issue of fault classification in the test distribution system is your best option.
Exploratory Data Analysis on Blueberry yield through Bayes and Function Models
Authors: Raj S.Britto , Shankar G. , Murugesan S. , Raju M Narasimha , Mohan E. , Rani P. Jona Innisai
Published: October 2023

Agricultural researchers are using machine learning to predict crop yield. Many machine learning algorithms need lots of data. One of the major challenges in training and experimenting with machine learning algorithms is the availability of training data in sufficient quality and quantity remains a limiting factor. The Linear Discriminant Analysis produces 95.88% of accuracy which is most efficient of selected models; The Nave Bayes Multinomial has 69.88% accuracy, while the Linear Discriminant Analysis has 0.96 precision. The NBM has 0.71 precision, while Linear Discriminant Analysis has 0.95 recall. The Linear Discriminant Analysis produces 0.99 of ROC, which is the most efficient outcome of selected models. The NBM gives least ROC, which is 0.80. The Linear Discriminant Analysis produces 0.99 of PRC, which is the most efficient outcome of selected models. The NBM gives least PRC, which is 0.72. The LDA explores efficient outcome with low deviations. Four machine-learning-based predictive models were then built using the simulated dataset. This simulated data provides researchers with actual field observation data and those who want to test machine learning algorithms' response to real data with crop yield prediction models.
Protein Kinase C Epsilon Overexpression in Prostate Adenocarcinoma is Associated with Oncogenesis
Authors: Trehan Deepika , Rehman Sayeed ur , Batra Janendra K , Agrawal Usha
Published: October 2023

Background: PKCε, an isozyme of serine-threonine kinase, has been implicated in epithelial cancer metastasis and progression. This study investigates the impact of the oncogenic PKCε, overexpressed abnormally in human Prostate tumor samples and cell lines, to understand its efficacy. Methods: The microarray dataset, GSE86257, was processed for normalization. The identification of upregulated and downregulated genes was based on FDR >1 and p <0.05 values. Cytoscape analysis and functional enrichment of significant genes were done. The identified genes were validated on the TCGA dataset and survival analysis was performed by Kaplan-Meier analysis. Results: A total of 1524 DEGs were identified with 728 upregulated genes and 818 downregulated genes. The two significant modules with MCODE score:9.0 and Venn analysis provided cyclin-dependent kinase inhibitor protein (CDK1), Cyclin B1 (CCNB1), Phospholipase C Gamma 1 (PLCG1), Cyclin Dependent Kinase 9 (CDK9), Phosphoinositide-3-Kinase Regulatory Subunit 3 (PIK3R3), H4 Clustered Histone 6 (H4C6), Phospholipase C Gamma 2 (PLCG2) as most interacting genes. TCGA data analysis and Prognostic analysis revealed CCNBI, CDK9, and PLCG1 associated with poor prognosis. Conclusion: PKCε regulates genes that are responsible for cancer progression. Therefore, targeting PKCε in Prostate cancer may serve as an important regulatory effect and may improve the prognosis of the disease.
A Review on Phyto-Pharmacological Aspects of Apamarg (Achyranthes aspera Linn.)
Authors: Pal Akriti , Vishal Gupta  , Gaurav Tiwari  , Ashish Manigaunha
Published: October 2023

Achyranthes aspera is very important ayurvedic medicinal plant. It is known as Apamarg Sanskrit name, prickly chaff flower in English and Naayuruvi in Tamil. It belongs to the family Amaranthaceae. This medicinal plant found as a weed thrrought India up to 900 m. Though almost all of its parts are used in traditional system of medicines, seeds, roots, and shoots are the most important parts, which are used medicinally. The major phytoconstituents are carbohydrate, protein, glycosides, alkaloids, tannins, flavonoids, and lignin. It also contains the phytochemicals like oleanolic acid, Saponin A and saponin B. A large number of phytochemical constituents have been isolated from the plant which possesses several pharmacological activities like diuretic, purgative, laxative, antiasthmatic, hepatoprotective, anti-allergic, hepatitis, renal disorders, dermatological disorders, gynecological disorders, gonorrhea, malaria, fever, cough, diabetes. The juice of the plant is used in the treatment of boils, diarrhea, dysentery, hemorrhoids, rheumatic pains, itches and skin eruption.
Deep Cervical Muscle Strengthening Exercise on Neck Pain Disability: A Randomized Controlled trail study
Authors: Krishnan Dr Madhan , Paluchamy Thenmozhi
Published: October 2023

Objective: The study aimed to investigate the effect of deep cervical flexor muscle strengthening exercises on neck pain disability among undergraduate students. Materials and Methods: A randomized controlled trial was adopted with 60 participants who met the inclusion and were randomly allocated into the study group (n=30) and the control group (n=30). Neck pain disability was evaluated before, one month after the initiation of deep cervical flexor muscle strengthening exercise. Results: Majority (>70%) of the participants were female, with a mean age group of 18.72, and used mobile phones and computers with improper ergonomics. There were significant (p<0.001) changes after one month in the overall neck disability index within the study group and between the study and control groups compared by paired t and independent t-test. Conclusion: This finding concludes that the outcome effectively reduces neck pain and its associated disability and could be incorporated into routine day-to-day practice to reduce neck pain, thereby minimizing the disability.
Purification of gas mixtures based on the removal of small impurities from a counterflow membrane module: engineering calculation method
Authors: Okunev Alexandr   Yu.
Published: October 2023

One of the tasks of the membrane gas separation process is the extraction of low-content highly penetrating impurities from gas mixtures.

The gas separation process is efficiently organized using hollow fiber-type membrane modules.

In this study, an attempt was made to develop an engineering method for calculating the purification of gas mixtures based on the removal of small impurities from a countercurrent membrane module, which would enable the calculations related to the separation mode using analytical dependencies, without the use of numerical integration procedures and iterations.

A mathematical model representing the gas separation process from binary mixtures having low content of a highly penetrating component was analyzed to address the challenges related to cleaning, purifying, and drying gas mixtures. Parametric studies related to the process were carried out and an engineering calculation method that enabled calculating separation in the module without using complex, resource-intensive algorithms having analytical formulas was developed herein. During the parametric study, the separation of a binary gas mixture was considered a limiting case. The proposed method was tested with respect to the example of air drying in a module comprising a (polyvinyltrimethylsilane) PVTMS membrane, which was an approximation of a binary mixture. The numerical and analytical results of the calculation are consistent with each other, and the calculation herein based on the developed method requires several orders of magnitude fewer calculations, that can even be performed manually.
Procedure for calculation and analysis of transcritical cycle CO2 with parallel compression
Authors: Talyzin Maksim S.
Published: October 2023

The use of CO2 as a refrigerant in transcritical cycles requires additional measures to increase the efficiency of the refrigeration system. Transcritical refrigeration systems are widely used at food processing facilities
Different modifications of the base cycle are used to increase efficiency, but the methods for calculating and analyzing such cycles are not sufficiently presented in the literature.
Along with the calculation of cycles and the determination of parameters at the base points, it is necessary to conduct an efficiency analysis in order to determine the optimal parameters.
The purpose of the study was to develop a method for calculating and analyzing the transcritical cycle of parallel compression CO2.
The calculation of the transcritical cycle is based on the fundamental laws of thermodynamics, the analysis method is based on the entropy-statistical method of thermodynamic analysis. Cycle calculation includes analysis of compression loss by system components.
The operation of the transcritical cycle of the parallel compression CO2 with two temperature levels is described, the procedure for calculating and analyzing losses in the elements of the refrigerating unit operating according to the transcritical cycle of the parallel compression CO2 is given.
The use of this method allows you to identify the elements and processes with the greatest losses and take further measures to improve the efficiency of the refrigeration system.