Functional annotation of proteins in Catharanthus roseus shoot cultures under biogenic zinc nanotreatment.

Nano-interactions are well known for their positive as well as negative impacts on the morphological and physiological systems of plants. Keeping in mind, the conformational changes in plant proteins as one of the key mechanisms for stress adaptation responses, the current project was designed to explore the effect of glutathione-capped and uncapped zinc nano-entities on Catharanthus roseus shoot cultures. Zinc nanotreatment (0.05 µg/mL) significantly induced ester production in C. roseus shoots as detected by Gas Chromatography-Mass spectrometry. These nanotreated shoots were further subjected to peptide-centric nano-LC-MS/MS analysis. Mass spectrometry followed by a Heat map revealed a significant effect of zinc nanoparticles on 59 distinct classes of proteins as compared to control. Proteins involved in regulating stress scavenging, transport, and secondary metabolite biosynthesis were robustly altered under capped zinc nanotreatment. UniProt database identified majority of the localization of the abundantly altered protein in cell membranes and chloroplasts. STRING and Cytoscape analysis assessed inter and intra coordination of triosephosphate isomerase with other identified proteins and highlighted its role in the regulation of protein abundance under applied stress. This study highlights the understanding of complex underlying mechanisms and regulatory networks involved in proteomic alterations and interactions within the plant system to cope with the nano-effect.

Attention-based deep learning framework to recognize diabetes disease from cellular retinal images.

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary cause of DR in patients is high blood sugar, and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and their automation applications can recognize the presence of DR in retinal cells and vessel images. Authors have proposed an attention-based hybrid model to recognize diabetes in early stage to prevent harmful clauses. Proposed methodology uses DenseNet121 architecture for convolution learning and then, the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates binary and multiclass classification to recognize the infection and the spreading of disease. Binary classification recognizes DR images either positive or negative, while multiclass classification represents an infection on a scale of 0-4. Simulation of the proposed methodology has achieved 98.57% and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention-based deep learning model has achieved remarkable accuracy to detect diabetic infection from retinal cellular images.

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.

Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning.

With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient's data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.

Challenges perceived by nursing professionals in physician-centred organizations: An exploratory qualitative study.

AIM: To explore and analyse contextual challenges in nursing that have affected nurses' perceptions and role performance. BACKGROUND: Health system hierarchy and patient/family-centred care has led to a high demand for skilled nurses. However, patriarchal organizations create challenges for nursing clinicians in Pakistan and elsewhere. METHODS: A qualitative exploratory research (phenomenology) design was used. Twenty-five participants identified through purposive sampling contributed to the study. The data analysis was conducted using NVivo 12 Plus. We generated six major themes. Reporting was accomplished according to the consolidated criteria for reporting qualitative research checklist. RESULTS: Gendered division of labour places nurses in a submissive position in clinical practice. Decreases in nurse-to-patient ratio and increase in patient-focused care adversely affect evidence-based practice. The gap between theory and practice in delivering quality care is increasing due to existing communication barriers among health-related professionals and an inadequate work environment. Comparatively inactive nursing leadership and directorate roles are not improving the social image of nursing, and are promoting role conflict and poor nursing self-concepts among nurses. In fact, cultural shock experienced by young nurses has produced inherent disorientation in their professionalism and fostered displays of horizontal violence towards them by senior nurses. CONCLUSION: These challenges are influencing nurses' decisions to remain in or to join nursing as a profession that is confronted by severe recruitment and retention shortages due to the social and cultural stigmatization of this female dominated profession. IMPLICATIONS FOR NURSING, HEALTH AND SOCIAL POLICY: This study promotes the concept of evidence-based practice to deliver quality health services in public hospitals and to improve the social status of nursing in Pakistan. It provides influential evidence to policymakers who should urgently address nurses' workplace health and safety issues as a global right.

Frequency of low back pain in young adults and its relationship with different mattresses.

OBJECTIVE: To find out the frequency of low back pain in young adults and its relationship with the mattresses being used. METHODS: The cross-sectional study was conducted from September 2019 to February 2020 in Rawalpindi and Islamabad, Pakistan, and comprised young adults aged 18-35 years who were using the same kind of mattress for more than 3 months. Data was collected using a self-structured questionnaire along with the Modified Oswestry Scale and the Numeric Pain Rating Scale. Data was analysed using SPSS 24. RESULTS: Of the 366 subjects, 266(72.7%) were women and 100(27.3%) were men. The overall mean age was 22.06±3.74 years. Of the total, 208(56.4%) participants were feeling low back pain, and, of them, pain was most prevalent in 30(14.4%) who were using firm mattress, and by 128(61.5%) who were using foam mattress. The pain was more frequent in those not having changed their mattresses for more than three years 105(50.4%). CONCLUSIONS: Low back pain was found to be a frequent occurrence in young adults and it was more prevalent in those using firm or foam mattresses for more than three years.

Qualitative study of socio-cultural challenges in the nursing profession in Pakistan.

BACKGROUND: In a patriarchal social system, a women-dominated profession like nursing is mostly seen as a disempowered group due to its stereotypical image and negative connotations. The low social prestige of this profession is based on the roles typically assigned to men and women to maintain gender identity according to their performance and embodiment. The aim of this study was to explore the social and cultural challenges faced by nurses while creating their professional image within the regional context of Lahore (Punjab) in Pakistan. METHODS: A qualitative research design was chosen to conduct one-to-one, in-depth interviews with twelve nurses. Recruitment was based on purposive sampling from three large public hospitals in Lahore to learn about nurses' perceptions of social and cultural challenges in the nursing profession. A thematic analysis was conducted using the data analysis software package NVivo 12 Plus. RESULTS: Cultural values give preference for female nurses. We have identified four major themes related to the social and cultural challenges facing the nursing profession: 1) gender-segregated profession, 2) inappropriate portrayals by the media, 3) issues around marriage settlement, and 4) identity from a religious perspective. These conflicts are affecting the professional status and changing perceptions of nurses, who either do not choose to remain in the nursing profession or do not recommend nursing as a career option. These ongoing constraints are still perpetuating and increasing shortage of nurses within the Pakistani healthcare system. CONCLUSION: The present study solely highlights nurses' perspectives on redefining gender roles and gender integration within the nursing profession. It argues that there is a need for positive portrayals in the media for the removal of public misperceptions related to nursing. This would reduce the shortage of nurses along with increasing retention and improving the quality of healthcare delivered to the public.

Monitoring of chlorination disinfection by-products and their associated health risks in drinking water of Pakistan.

This study reports the baseline data of chlorination disinfection by-products such as trihalomethanes (THMs) and their associated health risks in the water distribution network of Islamabad and Rawalpindi, Pakistan. THM monitoring was carried out at 30 different sampling sites across the twin cities for 6 months. The average concentration of total trihalomethanes (TTHMs) and chloroform ranged between 575 and 595 µg/L which exceeded the permissible US (80 µg/L) and EU (100 µg/L) limits. Chloroform was one of the major contributors to the TTHMs concentration (>85%). The occurrence of THMs was found in the following order: chloroform, bromodichloromethane > dibromochloromethane > bromoform. Lifetime cancer risk assessment of THMs for both males and females was carried out using prediction models via different exposure routes (ingestion, inhalation, and dermal). Total lifetime cancer risk assessment for different exposure routes (ingestion, inhalation, and skin) was carried out. The highest cancer risk expected from THMs seems to be from the inhalation route followed by ingestion and dermal contacts. The average lifetime cancer risk for males and females was found to be 0.51 × 10⁻³ and 1.22 × 10⁻³, respectively. The expected number of cancer risks per year could reach two to three cases for each city.

A novel univariate interpolation and bivariate regression hybrid method application to biodegradation of bisphenol A diglycidyl ether using laccases from Geobacillus stearothermophilus and Geobacillus thermoparafinivorans strains.

Bisphenol A diglycidyl ether (BADGE), a derivative of the well-known endocrine disruptor Bisphenol A (BPA), is a potential threat to long-term environmental health due to its prevalence as a micropollutant. This study addresses the previously unexplored area of BADGE toxicity and removal. We investigated, for the first time, the biodegradation potential of laccase isolated from Geobacillus thermophilic bacteria against BADGE. The laccase-mediated degradation process was optimized using a combination of response surface methodology (RSM) and machine learning models. Degradation of BADGE was analyzed by various techniques, including UV-Vis spectrophotometry, high-performance liquid chromatography (HPLC), Fourier transform infrared (FTIR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS). Laccase from Geobacillus stearothermophilus strain MB600 achieved a degradation rate of 93.28% within 30 min, while laccase from Geobacillus thermoparafinivorans strain MB606 reached 94% degradation within 90 min. RSM analysis predicted the optimal degradation conditions to be 60 min reaction time, 80°C temperature, and pH 4.5. Furthermore, CB-Dock simulations revealed good binding interactions between laccase enzymes and BADGE, with an initial binding mode selected for a cavity size of 263 and a Vina score of -5.5, which confirmed the observed biodegradation potential of laccase. These findings highlight the biocatalytic potential of laccases derived from thermophilic Geobacillus strains, notably MB600, for enzymatic decontamination of BADGE-contaminated environments.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation.

The statewide consumer transportation demand model analyzes consumers' transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server's computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Efficient geospatial mapping of buildings, woodlands, water and roads from aerial imagery using deep learning.

As more aerial imagery becomes readily available, massive volumes of data are being gathered constantly. Several groups can benefit from the data provided by this geographical imagery. However, it is time-consuming to manually analyze each image to gain information on land cover. This research suggests using deep learning methods for precise and rapid pixel-by-pixel classification of aerial imagery for land cover analysis, which would be a significant step forward in resolving this issue. The suggested method has several steps, such as the augmentation and transformation of data, the selection of deep learning models, and the final prediction. The study uses the three most popular deep learning models (Vanilla-UNet, ResNet50 UNet, and DeepLabV3 ResNet50) for the experiments. According to the experimental results, the ResNet50 UNet model achieved an accuracy of 94.37%, the DeepLabV3 ResNet50 model achieved an accuracy of 94.77%, and the Vanilla-UNet model achieved an accuracy of 91.31%. The accuracy, precision, recall, and F1-score of DeepLabV3 and ResNet50 are higher than those of the other two models. The proposed approach is also compared to the existing UNet approach, and the proposed approaches have produced greater probability prediction scores than the conventional UNet model for all classes. Our approach outperforms model DeepLabV3 ResNet50 on aerial image datasets based on the performance.

Artificial intelligence framework for heart disease classification from audio signals.

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model's performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks.

The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access to data and amenities. Enterprises and academics are contemplating using machine learning (ML) and deep learning (DL) for cyberattack avoidance because ML and DL show immense potential in several domains. Several DL teachings are implemented to extract various patterns from many annotated datasets. DL can be a helpful tool for detecting cyberattacks. Early network data segregation and detection thus become more essential than ever for mitigating cyberattacks. Numerous deep-learning model variants, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are implemented in the study to detect cyberattacks on an assortment of network traffic streams. The Canadian Institute for Cybersecurity's CICDIoT2023 dataset is utilized to test the efficacy of the proposed approach. The proposed method includes data preprocessing, robust scalar and label encoding techniques for categorical variables, and model prediction using deep learning models. The experimental results demonstrate that the RNN model achieved the highest accuracy of 96.56%. The test results indicate that the proposed approach is efficient compared to other methods for identifying cyberattacks in a realistic IoT environment.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption. ML algorithms, however, require a large amount of data to be adequate. There may be privacy violations due to collecting this data. To tackle this problem, this study proposes a federated deep learning (FDL) architecture developed around a deep neural network (DNN) paradigm. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the general public. The data is normalized using the min-max normalization approach, and the Synthetic Minority Over-sampling Technique (SMOTE) is used to enhance the minority class's interpretation. The DNN model is trained separately on the dataset after obtaining modifications from two clients. Each client assesses the data greatly to reduce the over-fitting impact. The test result demonstrates the efficiency of the proposed FDL by reaching 82.40% accuracy while securing the data.

Automated Cognitive Health Assessment Based on Daily Life Functional Activities.

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96% accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.

Insights into the microbiological and virulence characteristics of bacteria in orthopaedic implant infections: A study from Pakistan.

The exponential increase in the prevalence of multidrug resistant bacteria has resulted in limiting surgical treatment options globally, potentially causing biofilm-related complications, implant failure, and severe consequences. This study aims to isolate and characterize bacteria from post-surgical orthopaedic implant infections and screening for multiple antibiotic resistance. A cross-sectional study was conducted, involving isolation of forty-four dominant pathogenic bacterial isolates from 16 infected implant samples from across Islamabad and Rawalpindi. Out of forty-four, 38% cocci and 61% bacilli were obtained. Approximately 90% of isolates showed multiple antibiotic resistance (MAR) index of more than 0.2. Eleven strains were identified via 16S rRNA gene sequencing as Pseudomonas aeruginosa, Bacillus spp., Planococcus chinensis, Staphylococcus, Escherichia coli and Enterobacter cloacae. The bacterial strain E. coli MB641 showed sensitivity to Polymyxin only, and was resistant to all other antibiotics used. Maximum biofilm forming ability 0.532 ± 0.06, 0.55 ± 0.01 and 0.557 ± 0.07 was observed in Pseudomonas aeruginosa MB663, Pseudomonas aeruginosa MB664 and Bacillus spp. MB647 respectively after 24 hours of incubation. EPS production of bacterial strains was assessed, the polysaccharides and protein content of EPS were found to be in the range of 11-32 µg/ml and 2-10 µg/ml, respectively. Fourier transform infrared spectroscopic analysis of EPS showed the presence of carbohydrates, proteins, alkyl halides, and nucleic acids. X-ray diffraction analysis revealed crystalline structure of EPS extracted from biofilm forming bacteria. These findings suggest a high prevalence of antibiotic-resistant bacteria in orthopaedic implant-associated surgeries, highlighting the urgent need for ongoing monitoring and microorganism testing in infected implants.

Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.

Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model's accuracy to get a faster and more accurate prediction.

Primary hyperhidrosis: From a genetics point of view.

Primary hyperhidrosis is a disorder of profuse sweating which negatively influences a patient's quality of life and is caused because of over-activation of the sympathetic nervous system. It was believed that hyperhidrosis is a condition limited to only anxious individuals; however, this hypothesis is discredited now. It has been found that people with a positive family history of primary hyperhidrosis are likely to suffer from this condition, suggesting a strong genetic basis. Genetic analysis has revealed a dominant autosomal pattern of inheritance with a variable degree of penetrance and is a sex-independent trait. It is a heterogeneous condition both genetically and clinically as different studies revealed variable genetics and clinical factors. There are no proper criteria for diagnosis as it is not treated as disease by most affected persons. Various studies revealed opposing results in localizing disease gene loci, so further genetic research is needed to pinpoint genes responsible for causing this debilitating condition. Gene expression profiling of human anxiety-causing genes in hyperhidrotic sufferers will also help to devise new treatment modalities. This review highlights the current genetic studies on hyperhidrosis, which may prove to be helpful in understanding the molecular mechanism governing hyperhidrosis.

Isolation and Characterization of Lactobacillus crispatus, Lactococcus lactis, and Carnobacterium divergens as Potential Probiotic Bacteria from Fermented Black and Green Olives (Olea europaea): An Exploratory Study.

Background: Table olives are becoming well recognized as a source of probiotic bacteria that might be used to create a health-promoting fermented food product by traditional procedures based on the activities of indigenous microbial consortia present in local environments. Methodology. In the present study, the characterization of probiotic bacteria isolated from mince, chunks, and brine of fermented green and black olives (Olea europaea) was done based on morphological, biochemical, and physiological characteristics. Results: Bacterial isolates demonstrated excellent survival abilities at 25, 37, and 45°C and at a variable range of pH. However, the optimum temperature is 37 and the optimum pH is 7 for all three isolates. An antimicrobial susceptibility pattern was found among these isolates through the disc diffusion method. Most of the isolates were susceptible to streptomycin, imipenem, and chloramphenicol, whereas, amoxicillin showed resistance to these isolates, and variable results were recorded for the rest of the antibiotics tested. The growth of the isolates was optimum with the supplementation of 3% NaCl and 0.3% bile salt. The isolated bacteria were able to ferment skimmed milk into yogurt, hence making it capable of producing organic acid. Conclusion: Isolates of Lactobacillus crispatus MB417, Lactococcus lactis MB418 from black olives, and Carnobacterium divergens MB421 from green olives were characterized as potential candidates for use as starter cultures to induce fermentation of other probiotic food products.

Ensemble deep learning for brain tumor detection.

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.