Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

An Ingenious IoT Based Crop Prediction System Using ML and EL

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield.Python is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable crop.We implemented the proposed system in the field.The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

Human Pose Estimation and Object Interaction for Sports Behaviour

In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.

CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist guide.Incorporating visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene understanding.In this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an image.Initially,after acquisition and preprocessing,the image is segmented by using CNN.Then,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are computed.After the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion technique.Then,to select theminimal set of features,genetic algorithm-based feature selection is used.In order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is applied.Once objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation approach.Finally,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image.The experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

Automated Facial Expression Recognition and Age Estimation Using Deep Learning

With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is necessary.The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments.The proposed system first takes an input image pre-process it and then detects faces in the entire image.After that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group.The proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups dataset.The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time.The proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.

Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

Intelligent Sign Language Recognition System for E-Learning Context

In this research work,an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines.This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way.The hand gesture recognition based on these features has been implemented for usage in real-time.The feature set used hand landmarks,which were generated using media-pipe(MediaPipe)and open computer vision(openCV)on each frame of the incoming video.The overall algorithm has been tested on two well-known ASLalphabet(American Sign Language)and ISL-HS(Irish Sign Language)sign language datasets.Different machine learning classifiers including random forest,decision tree,and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been compared.Since the random forest classifier performed better,it has been selected as the base classifier for the proposed system.It showed 96.7%accuracy with ISL-HS and 93.7%accuracy with ASL-alphabet dataset using the extracted features.

Sensors-Based Ambient Assistant Living via E-Monitoring Technology

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted timely.Apart from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with robots.Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions.In this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human actions.These features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization method.Finally,the Masi entropy classifier is applied for action recognition and classification.Experiments have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and CMU-MMAC.During experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC dataset.Hence experimental results validated the proposed system.

Pedestrian Physical Education Training Over Visualization Tool

E-learning approaches are one of the most important learning platforms for the learner through electronic equipment.Such study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods.Many of them are currently working on this domain to fulfill the requirements of industry and the environment.In this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural network.For input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is applied.Deep flow features are extracted to find more accurate information.Furthermore,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior mining.The proposed method shows 84.50%of mean accuracy and a 15.50%of error rate.Therefore,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).

Improving the Ambient Intelligence Living Using Deep Learning Classifier

Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live independently.With a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s society.AAL can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for longer.In this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)classifier.We first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode decomposition.The convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal features.We assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,respectively.Experiments reveal that hybrid method gives more accuracy than single classifier in AAL model.The suggested system can assists persons with impairments,assisting carers and medical personnel.

Self-Care Assessment for Daily Living Using Machine Learning Mechanism

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders.Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity recognition.In this paper,an RGB-D-based ADL recognition system has been presented.Initially,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.Based on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)algorithm.Finally,random forest classifier has been used to classify activities into different categories.The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or offices.Hence,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of patients.We propose a robust system comprising both RGB(red,green,blue)and depth information.In particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map.To track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector quantization.Then,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for classification.There are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity dataset.Furthermore,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI datasets.The results proved the validity of the proposed system.

Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the game.Further,the sensor data is sent to a machine learning model,which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational sector.Extensive experimentation proved the validity of the proposed system.

A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)etc.Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way.Instead of the classical random walk,this paper proposes the quantum walk to achieve high image security.Classical random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave functions.The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity etc.All experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally efficient.In the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively.

An Intelligent Framework for Recognizing Social Human-Object Interactions

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model

Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.

A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

Automatic Anomaly Monitoring in Public Surveillance Areas

With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classification also increases.Although it has multiple applications,the problem is very challenging.In this paper,a novel approach for detecting nor-mal/abnormal activity has been proposed.We used the Gaussian Mixture Model(GMM)and Kalmanfilter to detect and track the objects,respectively.After that,we performed shadow removal to segment an object and its shadow.After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded humans.Fuzzy c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalflow are extracted for each identified silhouettes.Gray Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classification that is performed using the XG-Boost classifier.This system is applicable in any surveillance appli-cation used for event detection or anomaly detection.Performance of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of anomaly.The mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%respec-tively.These results are more accurate as compared to other existing methods.