Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and practical applications across a broad range of industries,including biotechnology,automotive,aerospace,and telecom-munications.By examining critical advancements in AI algorithms,machine learning,deep learning models,simulations,and predictive analytics,the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies.The review integrates both qualitative and quantitative data derived from academic studies,industry reports,and real-world case studies to showcase the tangible impacts of AI on product innovation,process optimization,and strategic decision-making.Notably,it discusses the challenges of integrating AI within complex industrial systems,such as ethical concerns,technical limitations,and the need for regulatory oversight.The findings reveal a mixed landscape where AI has significantly accelerated R&D processes,reduced costs,and enabled more precise simulations and predictions,but also highlighted gaps in knowledge transfer,skills adaptation,and cross-industry standardization.By bridging the gap between AI theory and practice,the review offers insights into the effectiveness,successes,and obstacles faced by organizations as they implement AI-driven solutions.Concluding with a forward-looking perspective,the review identifies emerging trends,future challenges,and promising opportunities inAI-poweredR&D,such as the rise of autonomous systems,AI-driven drug discovery,and sustainable energy solutions.It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers,engineers,and policymakers involved in high-tech Research and Development(R&D).

Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.

Risk factors for hepatocellular carcinoma associated with hepatitis C genotype 3 infection:A systematic review

BACKGROUND Hepatitis C virus(HCV)is a blood-borne virus which globally affects around 79 million people and is associated with high morbidity and mortality.Chronic infection leads to cirrhosis in a large proportion of patients and often causes hepatocellular carcinoma(HCC)in people with cirrhosis.Of the 6 HCV genotypes(G1-G6),genotype-3 accounts for 17.9%of infections.HCV genotype-3 responds least well to directly-acting antivirals and patients with genotype-3 infection are at increased risk of HCC even if they do not have cirrhosis.AIM To systematically review and critically appraise all risk factors for HCC secondary to HCV-G3 in all settings.Consequently,we studied possible risk factors for HCC due to HCV-G3 in the literature from 1946 to 2023.METHODS This systematic review aimed to synthesise existing and published studies of risk factors for HCC secondary to HCV genotype-3 and evaluate their strengths and limitations.We searched Web of Science,Medline,EMBASE,and CENTRAL for publications reporting risk factors for HCC due to HCV genotype-3 in all settings,1946-2023.RESULTS Four thousand one hundred and forty-four records were identified from the four databases with 260 records removed as duplicates.Three thousand eight hundred and eighty-four records were screened with 3514 excluded.Three hundred and seventy-one full-texts were assessed for eligibility with seven studies included for analysis.Of the seven studies,three studies were retrospective case-control trials,two retrospective cohort studies,one a prospective cohort study and one a cross-sectional study design.All were based in hospital settings with four in Pakistan,two in South Korea and one in the United States.The total number of participants were 9621 of which 167 developed HCC(1.7%).All seven studies found cirrhosis to be a risk factor for HCC secondary to HCV genotype-3 followed by higher age(five-studies),with two studies each showing male sex,high alpha feto-protein,directly-acting antivirals treatment and achievement of sustained virologic response as risk factors for developing HCC.CONCLUSION Although,studies have shown that HCV genotype-3 infection is an independent risk factor for end-stage liver disease,HCC,and liver-related death,there is a lack of evidence for specific risk factors for HCC secondary to HCV genotype-3.Only cirrhosis and age have demonstrated an association;however,the number of studies is very small,and more research is required to investigate risk factors for HCC secondary to HCV genotype-3.

Corneal astigmatic outcomes after femtosecond laser-assisted cataract surgery combined with surface penetrating arcuate keratotomies

AIM:To evaluate corneal astigmatic outcomes of femtosecond laser-assisted arcuate keratotomies(FAKs)combined with femtosecond-laser assisted cataract surgery(FLACS)over 12mo follow-up.METHODS:Totally 145 patients with bilateral cataracts and no ocular co-morbidities were recruited to a singlecentre,single-masked,prospective randomized controlled trial(RCT)comparing two monofocal hydrophobic acrylic intraocular lenses.Eyes with corneal astigmatism(CA)of>0.8 dioptres(D)received unpaired,unopened,surface penetrating FAKs at the time of FLACS.Visual acuity,subjective refraction and Scheimpflug tomography were recorded at 1,6,and 12mo.Alpins vectoral analyses were performed.RESULTS:Fifty-one patients(61 eyes),mean age 68.2±9.6y[standard deviation(SD)],received FAKs.Sixty eyes were available for analysis,except at 12mo when 59 attended.There were no complications due to FAKs.Mean pre-operative CA was 1.13±0.20 D.There was a reduction of astigmatism at all post-operative visits(residual CA 1mo:0.85±0.42 D,P=0.0001;6mo:0.86±0.35 D,P=0001;and 12mo:0.90±0.39,P=0.0001).Alpins indices remained stable over 12mo.Overall,the cohort was under-corrected at all time points.At 12mo,61%of eyes were within±15 degrees of pre-operative astigmatic meridian.CONCLUSION:Unpaired unopened penetrating FAKs combined with on-axis phacoemulsification are safe but minimally effective.CA is largely under-corrected in this cohort using an existing unmodified nomogram.The effect of arcuate keratotomies on CA remained stable over 12mo.

Jonzac Thermal Spring Water Reinforces Skin Barrier Function of Human Skin and Presents a Soothing and Regenerating Effect

The skin is a formidable physical and biological barrier which communicates continuously with the outside of the body. And the stratum corneum, the outermost layer of human epidermis, plays a central role in the interaction between the cutaneous tissue and the external environment. The horny layer, and more generally the whole skin layers, avoid the penetration of harmful exogenous agents, produce molecules named anti-microbial peptides which impact the composition of the cutaneous microbiota, regulate the internal corporal temperature, avoid the water loss from the inside of the body and constitute an incredible efficient anti-oxidant network. Nevertheless, nowadays, the skin is more and more solicited by the different elements of the cutaneous exposome, including atmospheric pollution and solar radiations, which can cause a dramatic acceleration of the skin ageing process. As a consequence, due to the multifunctional protective role of the skin, during the recent decade the cosmetic industry invested massively in the development of new raw materials and end-products (dermo-cosmetics) able to preserve an optimal state of the skin regarding the external environment. Based on their physical-chemical properties thermal spring waters, which are extremely rich in inorganics ions, are interesting and powerful candidates to be part, as integral component, of new efficient dermo-cosmetic formulations dedicated to protect the skin from the external stimuli. The aim of the present work was to investigate and characterize the activity of Jonzac thermal spring water on the skin. Using different models, we proved for the first time that Jonzac thermal spring water reinforces the barrier function of the skin by modulating the expression of key markers including filaggrin and human beta defensin 2 on ex vivo human skin. The ex vivo and in vivo hydration activity, by Raman spectroscopy and corneometry respectively, has been also demonstrated. We have also shown that Jonzac thermal spring water ameliorates significantly the cutaneous microrelief in vivo. To conclude, we characterize the soothing effect of Jonzac thermal spring water by the analysis of histamine release in Substance P treated skin explants and by measuring the redness of the skin following UV exposure of the skin in vivo. We observed that both parameters decreased following a preventive treatment of the skin with Jonzac thermal spring water. Taken together our results indicate that Jonzac thermal spring water is a promising and powerful dermo-cosmetic which can be used to preserve an optimal state of the cutaneous tissue.

Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technicians.However,it also became a time-consuming process.Hence the need for automated diagnosis became mandatory.In order to identify the tumor accurately,this research pro-poses a novel Convolution Neural Network(CNN)based superior image classi-fication technique.The proposed deep learning classification strategy has a precision of 97.7%,allowing for more effective usage of the automatically exe-cuted feature extraction technique to diagnose cancer cells.Comparative analysis with CNN-Grey Wolf Optimization(GWO)is carried based on varied testing and training outcomes.The suggested study is carried out at a rate of 90%–10%,80%–20%,and 70%–30%,indicating the robustness of the proposed research work.Outcomes show that the suggested method is effective.GWO-CNN is reli-able and accurate relative to other detection methods available in the literatures.

LSTM Based Spectrum Prediction for Real-Time Spectrum Access for IoT Applications

In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of applications.Spectrum prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio spectrum.Long short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum access.The proposed scheme provides an average maximum waiting time gain of 2.88 ms.The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.

Comparative Methodical Assessment of Established MOSFET Threshold Voltage Extraction Methods at 10-nm Technology Node

Threshold voltage (VTH) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise VTH value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of VTH diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the device. Numerous prevalent VTH extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node. Aim of this research paper is to explore and present a comparative study of largely applied threshold extraction methods for bulk driven nano-MOSFETs especially at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.

Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.

Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.

Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.

Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.

Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.

Cognitive Radios： A Survey On Spectrum Sensing, Security and Spectrum Handoff

Pervasive wireless computing and communication have created an ever-increasing demand for more radio spectrum. Since, most of the spectrum is underutilized, it motivated the introduction of the concept of cognitive radios, a dynamic spectrum access enabling technology. The first stage of cognitive radio is to sense the environment and determine which parts of the spectrum are available. This is achieved through spectrum sensing. However, spectrum sensing poses the most fundamental challenge in cognitive radios. Moreover, cognitive radios suffer from many vulnerabilities and the security attacks can severely degrade the performance of cognitive radios. This paper surveys state-of-theart research on spectrum sensing and security threats in cognitive radios. Lastly, we also consider the analysis of issues related to spectrum handoffs in cognitive radios.

Balloon overtube-guided colorectal endoscopic submucosal dissection

AIM:To evaluate the usefulness of a balloon overtube to assist colorectal endoscopic submucosal dissection(ESD)using a gastroscope.METHODS:The results of 45 consecutive patients who underwent colorectal ESD were analyzed in a single tertiary endoscopy center.In preoperative evaluation of access to the lesion,difficulties were experienced in the positioning and stabilization of a gastroscope in 15 patients who were thus assigned to the balloonguided ESD group.A balloon overtube was placed with a gastroscope to provide an endoscopic channel to the lesion in cases with preoperatively identified difficulties related to accessibility.Colorectal ESD was performed following standard procedures.A submucosal fluid bleb was created with hyaluronic acid solution.A circumferential mucosal incision was made to marginate the lesion.The isolated lesion was finally excised from the deeper layers with repetitive electrosurgical dissections with needle knives.The success of colorectal ESD,procedural feasibility,and procedure-related complications were the main outcomes and measurements.RESULTS:The overall en bloc excision rate of colorectal ESD during this study at our institution was 95.6%.En bloc excision of the lesion was successfully achieved in 13 of the 15 patients(86.7%)in the balloon overtube-guided colorectal ESD group,which was comparable to the results of the standard ESD group with better accessibility to the lesion(30/30,100%,not statistically significant).CONCLUSION:Use of a balloon overtube can improve access to the lesion and facilitate scope manipulation for colorectal ESD.

Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmentation applica-tion.Detection of some abnormal structures in human body has become a difficult task to complete with some simple images.For expounding and distinguishing neural architecture of human brain in an effective manner,MRI(Magnetic Reso-nance Imaging)is one of the most suitable and significant technique.Here we work on detection of Cerebral Atherosclerosis from MRI images of patients.Cer-ebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain.It leads to Ischemic stroke if not diagnosed early.Stroke affects majorly old age people and percentage of affected women is more compared to men.Results:Preproces-sing is done by using alpha trimmed meanfilter which is used to remove noise and also it enhances the image.Segmentation of cerebral atherosclerosis is done by using K-means clustering,Contextual clustering,and proposed Hybrid algo-rithm.Various parameters like Correlation,Pixel density,energy is determined and from the analysis of parameters it is determined that proposed Hybrid algo-rithm is efficient.

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Pest detection in agricultural cropfields is the most challenging task,so an effective pest detection technique is required to detect insects automatically.Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection,improved crop man-agement and productivity.On the other hand,developing the automatic pest mon-itoring system dramatically reduces the workforce and errors.Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy.Therefore,a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitor-ing and detection.The four-step image processing technique begins with image pre-processing,removing the insect image’s noise and sunlight illumination by utilizing an adaptive medianfilter.The insects’size and shape are identified using the Expectation Maximization Algorithm(EMA)based clustering technique,which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an image.Speeded up robust feature(SURF)method is employed to select the best possible image features.Eventually,the image with best features is classified by introducing a hybrid FF-GWO-CNN algorithm,which combines the benefits of Firefly(FF),Grey Wolf Optimization(GWO)and Convolutional Neural Network(CNN)classification algorithm for enhancing the classification accuracy.The entire work is executed in MATLAB simulation software.The test result reveals that the suggested technique has deliv-ered optimal performance with high accuracy of 97.5%,precision of 94%,recall of 92%and F-score value of 92%.