bluuebunny/crossref_metadata_2025 huggingface dataset:
Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT
Authors: Krishnan V. Gokula , Siva D. , Hemamalini S. , Sivakumar N. , Vijayaraja V.
Published: September 2023

Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods.
Toxic Comment Classification using Deep Learning
Authors: Naidu B. Ramesh , Tangudu Naresh , Sekhar Chandra , Kavitha K. , Ramana B. V. , Reddy P.Venkateswarlu , Sahukaru Jayavardhanarao , Lopinti Raj Ganesh
Published: September 2023

Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes.
Effectiveness of Deep Feature Extraction Algorithm in Determining the Maturity of Fruits: A Review
Authors: Tamilarasi T. , Muthulakshmi P.
Published: September 2023

Intelligent farming technology helps farmers overcome tough obstacles in the farming process, such as increased sup-plier costs, a lack of labour, customer satisfaction, and more. Artificial Intelligence (AI) is a remarkable technology in smart farming because it deeply understands the issue and can help farmers make decisions. This article's main objective is to identify and examine the concepts and techniques of Convolutional Neural Networks (CNN) technology that could aid in classifying the ripeness stages of fruit in intelligent farming. This paper systematically reviews 18 previous works for classifying the ripeness stages of fruit. This review outlines the most commonly used algorithms, activation functions, optimisation functions, and platforms for algorithm implementation. In addition, found that not all algorithms are suitable for even near-equivalent processes. Therefore, this study suggests the intensity of the CNN algorithms concerning various metrics to find the suitability for the operations/applications. Finally, this paper offers some future research directions in the ripeness classification of fruits.
Visual Tracking Based on Human Feature Extraction from Surveillance Video for Human Recognition
Authors: G S Monisha , Krishnan M.Hari , Selvan M Vetri , Nirmala G. , G S Yogashree
Published: September 2023

A multimodal human identification system based on face and body recognition may be made available for effective biometric authentication. The outcomes are achieved by extracting facial recognition characteristics using several extraction techniques, including Eigen-face and Principle Component Analysis (PCA). Systems for authenticating people using their bodies and faces are implemented using artificial neural networks (ANN) and genetic optimization techniques as classifiers. Through feature fusion and scores fusion, the biometric systems for the human body and face are merged to create a single multimodal biometric system. Human bodies may be identified with astonishing accuracy and effectiveness thanks to the SDK for the Kinect sensor. To identify people, biometrics aims to mimic the pattern recognition process. In comparison to traditional authentication methods based on secrets and tokens, it is a more dependable and safe option. Human physiological and behavioral traits are used by biometric technologies to identify people automatically. These characteristics must fulfill many criteria, especially those that relate to universality, efficacy, and applicability.
Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews
Authors: Sharma Rahul Kumar , Dagur Arvind
Published: September 2023

To determine an author's emotional state from their written words is the focus of sentiment analysis, a subfield of NLP. This study focuses on the many techniques used to categorize the text reviews written in natural language according to the viewpoints expressed therein, in order to determine if the widespread behavior is positive, negative, or neutral. Streaming of thoughts and expression of opinion have been facilitated by the proliferation of debate forums, Weblogs, product review sites, e-commerce, and social networking sites. A lot of people's feelings, reviews, and assessments of others' opinions can be found on social media. This research ranks the top classifier for feelings using data derived from online product reviews posted to Twitter. Experimental work on polarity classification with well-known classifiers such as Naive byes, Support vector machine, and Logistic regression for anticipating testimonials was addressed.
Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization
Authors: Priyadarshini Aliva , Pradhan Sateesh Kumar , Pattnaik Saumendra , Laha Suprava Ranjan , Pattanayak Binod Kumar
Published: September 2023

Cloud computing efficiently allocates resources, and timely execution of user tasks is pivotal for ensuring seamless service delivery. Central to this endeavour is the dynamic orchestration of task scheduling and migration, which collectively contribute to load balancing within virtual machines (VMs). Load balancing is a cornerstone, empowering clouds to fulfill user requirements promptly. To facilitate the migration of tasks, we propose a novel method that exploits the synergistic potential of K-means clustering and Ant Colony Optimization (ACO). Our approach aims to maximize the cloud ecosystem by improving several critical factors, such as the system's make time, resource utilization efficiency, and workload imbalance mitigation. The core objective of our work revolves around the reduction of makespan, a metric directly tied to the overall system performance. By strategically employing K-means clustering, we effectively group tasks with similar attributes, enabling the identification of prime candidates for migration. Subsequently, the ACO algorithm takes the reins, orchestrating the migration process with an inherent focus on achieving global optimization. The multifaceted benefits of our approach are quantitatively assessed through comprehensive comparisons with established algorithms, namely Round Robin (RR), First-Come-First-Serve (FCFS), Shortest Job First (SJF), and a genetic load balancing algorithm. To facilitate this evaluation, we harness the capabilities of the CloudSim simulation tool, which provides a platform for realistic and accurate performance analysis. Our research enhances cloud computing paradigms by harmonizing task migration with innovative optimization techniques. The proposed approach demonstrates its prowess in harmonizing diverse goals: reducing makespan, elevating resource utilization efficiency, and attenuating the degree of workload imbalance. These outcomes collectively pave the way for a more responsive and dependable cloud infrastructure primed to cater to user needs with heightened efficacy. Our study delves into the intricate domain of cloud-based task scheduling and migration. By synergizing K-means clustering and ACO algorithms, we introduce a dynamic methodology that refines cloud resource management and bolsters the quintessential facet of load balancing. Through rigorous comparisons and meticulous analysis, we underscore the superior attributes of our approach, showcasing its potential to reshape the landscape of cloud computing optimization.
Smart Cradle: A Technology-Enabled Solution for Safer and Better Infant Sleep
Authors: Thopate Kaushalya , Gawade Mayuri , Savale Vaishali , Cholke Abhijeet , Musale Prajakta
Published: September 2023

The Smart Cradle using Internet of Things (IoT) is a novel and innovative approach to modernize traditional cradle systems by incorporating smart and connected technologies. This IoT-based cradle system offers enhanced safety, comfort, and convenience for both babies and caregivers. The Smart Cradle is equipped with various sensors such as temperature, humidity, motion, and sound sensors that continuously monitor the baby's environment. These sensors collect data in real-time and send it to a cloud-based server for processing and analysis. The caregivers can access this data through a mobile application or a web interface, allowing them to remotely monitor the baby's condition and receive alerts in case of any abnormalities. Furthermore, the Smart Cradle incorporates features like automated rocking, adjustable incline, and soothing lullabies, which can be controlled remotely through the mobile application. The caregivers can customize the cradle's settings based on the baby's preferences and needs, providing a personalized sleeping experience for the baby. Additionally, the Smart Cradle offers seamless integration with other smart home devices, such as smart cameras, smart lights, and smart thermostats, enabling caregivers to create a safe and conducive environment for the baby. The system can also generate insights and recommendations based on the collected data, helping caregivers to make informed decisions about the baby's sleep patterns, health, and well- being.
IOT Security Against Network Anomalies through Ensemble of Classifiers Approach
Authors: Verma Saurav , Prakash Chetana
Published: September 2023

The use of IoT networks to monitor critical environments of all types where the volume of data transferred has greatly expanded in recent years due to a large rise in all forms of data. Since so many devices are connected to the Internet of Things (IoT), network and device security is of paramount importance. Network dynamics and complexity are still the biggest challenges to detecting IOT attacks. The dynamic nature of the network makes it challenging to categorise them using a single classifier. To identify the abnormalities, we therefore suggested an ensemble classifier in this study. The proposed ensemble classifier combines the independent classifiers ELM, Nave Byes (NB), and the k-nearest neighbour (KNN) in bagging and boosting configurations. The proposed technique is evaluated and compared using the MQTTset, a dataset focused on the MQTT protocol, which is frequently utilised in IoT networks. The analysis demonstrates that the proposed classifier outperforms the baseline classifiers in terms of classification accuracy, precision, recall, and F-score.
Dog Breed Identification using ResNet Model
Authors: Reddy Yarram Abhilash , Kumar Yambaku Suneel , M Sankari , Mana Suja Cherukullapurath
Published: September 2023

As dogs are domestic animals due to the many numbers of dog breeds available around the world. It’s hard to find out the exact dog breed name for a common person. There are many techniques available to identify dog breed. But the proposed work introduced the new technique called RESNET which is the part of CNN to classify dog. RESNET is used to identify images. It helps to perform different tasks on larger datasets. Identification of different dogs is one of the important applications of Convolutional Neural networks. Since the identification of dog breeds is very difficult because they spread in a large number and it makes very hard for a person to identify or classify dogs. With the help of Keras and TensorFlow, a dataset is created, tested, and trained for the detection of dog breeds by using RESNET. Around 120 different dog breeds are present in the dataset which consist of 20600 images of dogs. From this paper, load these images and convert them into a NumPy array and normalize them. Then,100 epochs were used with a batch size of 128 to achieve the best accuracy. The model is saved for further process to create a web application to identify the dog.
Anemia Detection using a Deep Learning Algorithm by Palm Images
Authors: Dhanasekaran S. , Shanker N. R.
Published: September 2023

Our aim is to detect anemia through a comparative analysis of three convolutional neural network (CNN) models, namely EfficientNet B3, DenseNet121, and CNN AllNet. A collection of 3,000 microscopic palm pictures, including 1,500 anaemic and 1,500 non-anemic samples, was used to train and test the algorithms. The dataset was preprocessed to balance the classes, augment the images, and normalize the pixel values. The models were trained using transfer learning on the ImageNet dataset and fine-tuned on the anemia dataset. The performance of the models was evaluated based on accuracy, precision, recall, and F1-score. The results showed that CNN ALLNET achieved the highest accuracy of 96.8%, followed by DenseNet121 with 94.4%, and EfficientNet B3 with 91.2%. The precision, recall, and F1-score also followed a similar trend. The study concludes that CNN ALLNET is the optimal model for anemia detection due to its high accuracy and overall better performance when compared with the different models. The findings of this research could provide a basis for further studies on anemia detection using CNN models, ultimately improving the accuracy and efficiency of anemia diagnosis and treatment.
FOHC: Firefly Optimizer Enabled Hybrid approach for Cancer Classification
Authors: Pati Abhilash , Panigrahi Amrutanshu , Sahu Bibhuprasad , Sahoo Ghanashyam , Dash Manoranjan , Parhi Manoranjan , Pattanayak Binod Kumar
Published: September 2023

Early detection and prediction of cancer, a group of chronic diseases responsible for a large number of deaths each year and a serious public health hazard, can lead to more effective treatment at an earlier stage in the disease's progression. In the current era, machine learning (ML) has widely been used to develop predictive models for incurable diseases such as cancer, heart disease, and diabetes, among others, taking into account both existing datasets and personally collected datasets, more research is still being conducted in this area. Using recursive feature elimination (RFE), principal component analysis (PCA), the Firefly Algorithm (FA), and a support vector machine (SVM) classifier, this study proposed a Firefly Optimizer-enabled Hybrid approach for Cancer classification (FOHC). This study considers feature selection and dimensionality reduction techniques RFE and PCA, and FA is used as the optimization algorithm. In the last stage, the SVM is applied to the pre-processed dataset as the classifier. To evaluate the proposed model, empirical analysis has been carried out on three different kinds of cancer disease datasets including Brain, Breast, and Lung cancer obtained from the UCI-ML warehouse. Based on the various performance parameters like accuracy, error rate, precision, recall, f-measure, etc., some experiments are carried out on the Jupyter platform using Python codes. This proposed model, FOHC, surpasses previous methods and other considered state-of-the-art works, with 98.94% accuracy for Breast cancer, 95.58% accuracy for Lung cancer, and 96.34% accuracy for Brain cancer. The outcomes of these experiments represent the effectiveness of the proposed work.
Deep Learning-based Recognition of Devanagari Handwritten Characters
Authors: Kolhe Prashant Sopanrao
Published: September 2023

Numerous techniques have been used over many years to study handwriting recognition. There are two methods for reading handwriting, one of which is online and the other offline. Image recognition is the main part of the handwriting recognition process. Image recognition gives careful consideration to the picture's dimensions, viewing angle, and image quality. Machine learning and deep learning techniques are the two areas of focus for developers looking to increase the intelligence of computers. A person may learn to perform a task by repeatedly exercising it until they recall how to do it. His brain's neurons begin to work automatically, enabling him to carry out the task he has quickly learned. This and deep learning are fairly similar. It uses a variety of neural network designs to address a range of problems. The convolution neural network (CNN) is a very effective technique for handwriting and picture detection.
Performance Evaluation and Validation of Intelligent Security Mechanism in Software Defined Network
Authors: Bendale Shailesh , Gupta Brijendra
Published: September 2023

Network attacks are discovered using intrusion detection systems (IDS), one of the most crucial security solutions. Machine learning techniques-based intrusion detection approaches have been rapidly created as a result of the widespread use of standard machine learning algorithms in the security field. Unfortunately, as technology has advanced and there have been faults in the machine learning-based intrusion detection system, the system has consistently failed to fulfill the standards for cyber security. Generative adversarial networks (GANs) have drawn a lot of interest recently and have been utilized widely in anomaly detection due to their enormous capacity for learning difficult high-dimensional real time data distribution. Traditional machine learning algorithms for intrusion detection have a number of drawbacks that deep learning techniques can significantly mitigate. With the help of a real time dataset, this work suggests employing GANs and its variants to detect network intrusions in SDN. The feasibility and comparison results are also presented. For different kinds of datasets, the BiGAN outcomes outperform the GAN.
An Improved Firefly Optimization Algorithm for Analysis of Arrhythmia Types
Authors: Sinnoor Mala , Janardhan Shanthi Kaliyil
Published: September 2023

Irregular heartbeats rhythm is the result of arrhythmia condition which can be a threat to life if not treated at the early stage. If it is necessary to know the type of arrhythmia to treat the patient appropriately. The traditional method is complex and an efficient algorithm is required to diagnose. An improved firefly optimization algorithm is proposed to analyze arrhythmia types. Four performance measures confirm the model's effectiveness and experimental evaluation shows that it achieves a sensitivity of 86.27%, accuracy of 86.14%, precision of 87.52%, and specificity of 87.37% in arrhythmia-type classification. The algorithm can effectively classify the arrhythmia types with high accuracy and specificity.
Performance Analysis of Reputation based Proof of Credibility Consensus Mechanism for Blockchain based Applications
Authors: Khamar Jalpa , Patel Hiren
Published: September 2023

Blockchain is a decentralized transaction and data management technology first developed for the Bitcoin cryptocurrency. Blockchain technology is gaining popularity due to its core attributes which provides security, anonymity and data integrity without any involvement of third party. Consensus mechanism is a procedure by which all peers in the blockchain network agrees to a common agreement on the current state of the distributed ledger. It plays vital role in increasing efficiency of any blockchain environment. Though we have many consensus mechanisms working currently in different areas but they still lack in parameters like status of validators, latency, node failure etc. In Our proposed algorithm Proof of credibility, we have tried to incorporate all above factors in it. We have also implemented two or more factors of proposed algorithm and have evaluated and compared with existing consensus algorithm. In future research we aim to implement RPoC in any blockchain network and then we will evaluate it in terms of different evaluation parameters such as performance, security, scalability.
MBFRDH: Design of a Multimodal Bioinspired Feature Representation Deep Learning Model for Identification of Heart-Diseases
Authors: Jaisinghani Komal S. , Malik Sandeep
Published: September 2023

Electrocardiograms (ECGs) are generated by checking different beating patterns of heart, and are widely used for identification of multiple heart-related issues. Existing deep learning models that are proposed for ECG analysis are either highly complex, or showcase lower scalability when applied to clinical scans. To overcome these issues, this text proposes design of a novel multimodal bioinspired feature representation deep learning model for identification of heart-diseases. The proposed model initially collects large-scale ECG datasets, and extracts Fourier, Cosine, iVector, Gabor, and Wavelet components. These components are given to a Grey Wolf Optimization (GWO) based feature selection model, which assists in identification of high-inter-class variance feature sets. This is done via modelling a variance-based fitness function and fusing it with an Iterative Learning Model (ILM) that use feedback-accuracy levels for optimization of selected feature sets. The extracted features are used to incrementally train a custom 1D Binary-Augmented Convolutional Neural Network (1D BACNN) that can be trained for multiclass scenarios. The BACNN Model is trained individually for each of the heart diseases. Each BACNN categorizes input ECG samples between ‘Normal’, and ‘Heart-Disease’ categories. Due to use of this binary-type classification, the proposed model is able to achieve a consistent 99.9% accuracy for multiple heart disease sets, which is found to be higher than most of the existing multiclass techniques. The model was tested for Angina, Arrhythmia, Valve disease, and Congenital heart conditions, and was observed to achieve 3.5% higher precision, 4.9% higher accuracy, with 1.2% increase is computational delay, which makes it highly suitable for real-time clinical use cases.
Peer-to-Peer File Sharing WebApp
Authors: Bhisikar Swati , Taneja Simran , Yadav Omkar , Srivastava Swarnika
Published: September 2023

Peer-to-peer (P2P) networking has emerged as a promising technology that enables distributed systems to operate in a decentralized manner. P2P networks are based on a model where each node in the network can act as both a client and a server, thereby enabling data and resource sharing without relying on centralized servers. The P2P model has gained considerable attention in recent years due to its potential to provide a scalable, fault-tolerant, and resilient architecture for various applications such as file sharing, content distribution, and social networks.In recent years, researchers have also proposed hybrid architectures that combine the benefits of both structured and unstructured P2P networks. For example, the Distributed Hash Table (DHT) is a popular hybrid architecture that provides efficient lookup and search algorithms while maintaining the flexibility and adaptability of the unstructured network.To demonstrate the feasibility of P2P systems, several prototypes have been developed, such as the BitTorrent file-sharing protocol and the Skype voice-over-IP (VoIP) service. These prototypes have demonstrated the potential of P2P systems for large-scale applications and have paved the way for the development of new P2P-based systems.
Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review
Authors: Dinesh P , Lakshmanan Ramanathan
Published: September 2023

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.
Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model
Authors: Rajalakshmi T. , Senthilkumar C.
Published: September 2023

The use of deep learning techniques for White Blood Cell (WBC) classification has garnered significant attention on medical image analysis due to its potential to automate and enhance the accuracy of WBC classification, which is critical for disease diagnosis and infection detection. Convolutional neural networks (CNNs) have revolutionized image analysis tasks, including WBC classification effectively capturing intricate spatial patterns and distinguishing between different cell types. A key advantage of deep learning-based WBC classification is its capability to handle large datasets, enabling models to learn the diverse variations and characteristics of different cell types. This facilitates robust generalization and accurate classification of previously unseen samples. In this paper, a novel approach called Red Deer Optimization with Deep Learning for Robust White Blood Cell Detection and Classification was presented. The proposed model incorporates various components to improve performance and robustness. Image pre-processing involves the utilization of median filtering, while U-Net++ is employed for segmentation, facilitating accurate delineation of WBCs. Feature extraction is performed using the Xception model, which effectively captures informative representations of the WBCs. For classification, BiGRU model is employed, leveraging its ability to model temporal dependencies in the WBC sequences. To optimize the performance of the BiGRU model, the RDO is utilized for parameter tuning, resulting in enhanced accuracy and faster convergence of the deep learning models. The integration of RDO contributes to more reliable detection and classification of WBCs, further improving the overall performance and robustness of the approach. Experimental results demonstrate the superiority of our Red Deer Optimization with deep learning-based approach over traditional methods and standalone deep learning models in achieving robust WBC detection and classification. This research highlights the possibility of combining deep learning techniques with optimization algorithms for improving WBC analysis, offering valuable insights for medical professionals and medical image analysis.
EEG based Stress Analysis through Feature Extraction
Authors: Pathak Kishor R. , Haneef Faraha
Published: September 2023

The diagnosis of Stress relies virtually solely on doctor-patient conversation and scale analysis, which includes problems such as patient denial, insensitivity, subjective biases, and inaccuracy. Improving the accuracy of Stress diagnosis and therapy necessitates the development of an objective, computerized system for predicting clinical outcomes. Using the modification of EEG data and machine learning techniques, this study attempts to improve the recognition of Stress. The EEG data of 10 volunteers were acquired using a Narosky device during an experiment, including emotive facial stimuli. Psychiatrists used the EEG signal as the criterion for diagnosis of Stress in patients. The different approaches processed the features: machine learning and deep learning. Significant outcomes are achieved using PCA, ICA & EMD for BCI applications. SVM empowers a developer with several advantages: PCA exhibits excellent generalization properties, with stress & pressure detection using EEG Signals. If the signals are negative, the impact of overtraining is sensitive to the curse-of- dimensionality. These advantages were achieved by using EEG signals to detect Stress. The experimental analysis gives some overview of all different approaches, which depend on frequency domain analysis with 14 fourteen-channel EEG signals with reasonable accuracy.
Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)
Authors: Vijayakumar D. Sudaroli , Ganapathy Sannasi
Published: September 2023

One of today's most promising developments is wireless networking, as it enables people across the globe to stay connected. As the wireless networks' transmission medium is open, there are potential issues in safeguarding the privacy of the information. Though several security protocols exist in the literature for the preservation of information, most cases fail with a simple spoof attack. So, intrusion detection systems are vital in wireless networks as they help in the identification of harmful traffic. One of the challenges that exist in wireless intrusion detection systems (WIDS) is finding a balance between accuracy and false alarm rate. The purpose of this study is to provide a practical classification scheme for newer forms of attack. The AWID dataset is used in the experiment, which proposes a feature selection strategy using a combination of Elastic Net and recursive feature elimination. The best feature subset is obtained with 22 features, and a deep deterministic policy gradient learning algorithm is then used to classify attacks based on those features. Samples are generated using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) to evaluate classification outcomes using adversarial samples. The meta-analysis reveals improved results in terms of feature production (22 features), classification accuracy (98.75% for testing samples and 85.24% for adversarial samples), and false alarm rates (0.35%).
Edge-Based Health Care Monitoring System: Ensemble of Classifier Based Model
Authors: A Raja , P M Prathibhavani , K R Venugopal
Published: September 2023

Health Monitoring System (HMS) is an excellent tool that actually saves lives. It makes use of transmitters to gather information and transmits it wirelessly to a receiver. Essentially, it is much more practical than the large equipment that the majority of hospitals now employ and continuously checks a patient's health data 24/7. The primary goal of this research is to develop a three-layered Ensemble of Classifier model on Edge based Healthcare Monitoring System (ECEHMS) and Gauss Iterated Pelican Optimization Algorithm (GIPOA) including data collection layer, data analytics layer, and presentation layer. As per our ECEHMS-GIPOA, the healthcare dataset is collected from the UCI repository. The data analytics layer performs preprocessing, feature extraction, dimensionality reduction and classification. Data normalization will be done in preprocessing step. Statistical features (Min/Max, SD, Mean, Median), improved higher order statistical features (Skewness, Kurtosis, Entropy), and Technical indicator based features were extracted during Feature Extraction step. Improved Fuzzy C-means clustering (FCM) will be used for handling the Dimensionality reduction issue by clustering the appropriate feature set from the extracted features. Ensemble model is introduced to predict the disease stage that including the models like Deep Maxout Network (DMN), Improved Deep Belief Network (IDBN), and Recurrent Neural Network (RNN). Also, the enhancement in prediction/classification accuracy is assured via optimal training. For which, a GIPOA is introduced. Finally, ECEHMS-GIPOA performance is compared with other conventional approaches like ASO, BWO, SLO, SSO, FPA, and POA.
A Novel Cyber Resilience Framework – Strategies and Best Practices for Today's Organizations
Authors: Kanthimathinathan A. , Saravanan S. , Anbalagan P.
Published: September 2023

Cyber resilience refers to an organization's ability to maintain its essential functions, services despite cyber-attacks and swiftly recover from any disruptions. It involves proactive measures like gathering threat intelligence and managing risks, as well as reactive measures such as incident response planning, data backup and recovery. To achieve cyber resilience, organizations must implement robust cyber security measures, regularly update their incident response plans, and educate employees on safe online practices. Furthermore, having a comprehensive backup and recovery strategy in place is crucial to swiftly restore critical systems and data in the event of an attack. Overall, the proposed framework emphasizes cyber resilience as a continuous and proactive approach for managing cyber security risks and safeguarding against the growing threat of cyber-attacks.
Classification of Mild Cognitive Impairment with Deep Transfer Learning Approach using CWT based Scalogram Images
Authors: Saroja P. , Nalini N. J. , Mahesh G.
Published: September 2023

Mild Cognitive Impairment (MCI) is a condition that can occur as a person gets older, and faces problems like recognition, memory, and language skills. Early detection of MCI is crucial, as it can progress to more severe conditions like Alzheimer's disease. This study proposes a method to use Scalogram images, obtained by applying Continuous Wavelet Transform (CWT) to EEG signals and pre-trained models like ResNet50, VGG16, InceptionV3, Inception_ResNetV2 through transfer learning to classify MCI and Healthy Control (HC). Fine-tuning of the models is also used to improve the results, and various performance metrics are employed for classification. The study concludes that Inception_ResNetV2 transfer learning yielded good results, while ResNet50 and InceptionV3 transfer learning with fine-tuning resulted in higher accuracy using a low learning rate.
Survey on IoT based Cyber Security Issues and Autonomous Solutions for Implantable Medical Devices
Authors: C Thyagarajan. , S Vijay Bhanu. , S Suthir.
Published: September 2023

In today’s world the technology has got boomed up to the peak. So as a measure of this technology peak we could see that the enhancement of this has raised very large. This technology booming has also impacted health care sector. In our paper we are going to discuss much on implantable medical devices and its uses which plays a major role in patient’s life. This IMD’s are going to be the life changing aspect of each and every patient. These devices are highly controlled IoT devices (i.e.) those devices are connected through internet which will help doctors to track the details of the patients remotely. On the other hand since all these devices are connected to internet, these are easily hacked by the hackers. The factors of how those devices are much vulnerable and what are all the threats that will make these devices to malfunction and lead a problem to the patients is discussed. And also this will lead the health sector to fall in their reputation. IMD’s are of many types which are in existing in the Medical industry. But we are going to consider some IMD’s as example and we have planned to make a detailed study on the problems on those devices. All these devices are vulnerable since it is connected to internet. So our aim is to completely or partially reduce the risks on those devices via communication network. We have also showcased the possible threats and vulnerabilities chances on those devices. The main scenarios of device control issues and possible solutions have been discussed in this article.
Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model
Authors: Jawale Shila S. , Sawarkar S. D.
Published: September 2023

Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written,  polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like  XLNet) to effectively represent the features needed for processing.  To improve the performace towards emotion or  sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced  dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as  MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well.
Harnessing Artificial Intelligence to Empower HR Processes and Drive Enhanced Efficiency in the Workplace to Boost Productivity
Authors: Yadav Meenakshi , Kakkar Manav , Kaushik Priyanka
Published: September 2023

The current study aims to advance knowledge about how to relate to AI-based systems for HR operations to increase productivity and develop applications. The system uses AI technologies including machine learning, natural language processing, and classification analysis to automate and streamline HR operations like job applications, recruitment, learning enhancement, mentoring for applicants, and enrichment of start-ups. The solution is anticipated to improve process accuracy and efficiency while reducing the time and cost associated with HR functions. The project entails creating a system for applications as well as an efficient system. The project's outcomes are anticipated to show the potential advantages of AI in HR-related tasks.
Utilization of Augmented Reality for Human Organ Analysis
Authors: Gupta Neha , Bansal Ashish , Khan Ihtiram Raza , Vani Nilesh Shubhash
Published: September 2023

This research paper investigates the utilization of augmented reality (AR) technology for human organ analysis in medical education. The study aims to develop and evaluate an AR application that provides an immersive and interactive learning experience for medical students. The research follows a quantitative methodology, to develop and test the effectiveness of the AR application in improving learning outcomes. The research examines the impact of the AR application on student engagement, retention of information, and performance on assessments. The results show that the AR application has a significant positive impact on learning outcomes. The use of AR technology improves student engagement, retention of information, and performance on assessments. The application's design and functionality were found to be intuitive and user-friendly, making it accessible for both students and educators. The research highlights the potential of AR technology in medical education and provides insights into its effectiveness in improving learning outcomes. The findings suggest that AR technology can be a valuable tool in medical education, enhancing the way students learn about human anatomy. This research can contribute to the existing literature on the use of AR technology in education, paving the way for future research and innovation in the field. Ultimately, the study shows that the integration of AR technology in medical education can significantly enhance the learning experience for students, providing them with an immersive and interactive approach to learning about human anatomy.
Secure Face and Liveness Detection with Criminal Identification for Security Systems
Authors: Shinde Pratibha , Raundale Ajay
Published: September 2023

The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security.

The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.
Video Deraining Mechanism by Preserving the Temporal Consistency and Intrinsic Properties of Rain Streaks
Authors: Gogulamudi Subbarao , Mahalakshmi V. , Sreeram Indraneel
Published: September 2023

The problem of video deraining is one of the most focused research areas where several techniques are introduced to achieve higher visual quality in the results. The process of video deraining tries to eliminate the presence of rain streaks in the videos so that the overall quality of the video can be enhanced. The existing frameworks tried to accurately eliminate the rain streaks in videos, but there is still room for improvement to preserve the temporal consistencies and intrinsic properties of rain streaks. This work does the job with the combination of handcrafted and deep priors that deeply extract and identify the nature of rain streaks in different dimensions. The proposed work included three main steps: Prior extraction, Derain modelling and optimization. Four major priors are extracted from the frames where the gradient prior (GP) and sparse prior (SP) are extracted from the rain streaks, and the smooth temporal prior (STP) and deep prior (DP) are extracted from the clean video. The unidirectional total variation (UTV) is applied to extract the GP, and the L1 normalization method is followed to extract the SP and STP. The DP is then extracted from the clean frames using the residual gated recurrent deraining network (Res-GRRN) model based on deep learning. Derain modelling is carried out based on the extracted priors, and the stochastic alternating direction multiplier method (SADMM) algorithm is utilized to solve the optimization problem. The proposed approach is then implemented in python and evaluated using the real-world dataset. The overall PSNR achieved by the proposed approach is 39.193dB, which is more optimal than the existing methods.
Cross-Layer Fragment Indexing based File Deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for Optimized Cloud Storage
Authors: Geetha K. , Vijaya A.
Published: September 2023

Cloud computing and storage processing is a big service for maintaining a large number of data in a centralized server to store and retrieve data depending on the use to pay as a service model. Due to increasing storage depending on duplicate copy presence during different sceneries, the increased size leads to increased cost. To resolve this problem, we propose a Cross-Layer Fragment Indexing (CLFI) based file deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for optimized cloud storage. Initially, the file storage indexing easy carried out with Lexical Syntactic Parser (LSP) to split the files into blocks. Then comparativesector was created based on Chunk staking. Based on the file frequency weight, the relative Indexing was verified through Cross-Layer Fragment Indexing (CLFI). Then the fragmented index gets grouped by maximum relative threshold margin usingIntra Subset Near-Duplicate Clusters (ISNDC). The hashing is applied to get comparative index points based on hyper correlation comparer using Hyper Spectral Hash Duplicate Filter (HSHDF). This filter the near duplicate contentdepending on file content difference to identify the duplicates. This proposed system produces high performance compared to the other system. This optimizes cloudstorage and has a higher precision rate than other methods.