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Zinc oxide (ZnO), iron oxide (Fe3O4) NPs, and ZnO/Fe3O4 nanocomposites (NCs) were synthesized using Nigella sativa (NS) seed extracts. These nanomaterials (NMs) were characterized by multiple analytical techniques like UV-visible spectrophotometers, FTIR spectroscopy, SEM, and XRD spectroscopy. The UV-visible spectrum showed 364 nm, 330 nm, and 331nm for ZnO NPs, Fe3O4 NPs, and ZnO/Fe3O4 NCs, respectively. Moreover, it was confirmed when the samples were run through FTIR techniques, and it was observed that the availability of bioactive functional groups was involved in the decline of bulk compounds to NPs. XRD showed their average nanoparticle size based on applying the formula of full width at half maximum (FWHM) for ZnO NPs, Fe3O4 NPs, and ZnO/Fe3O4 NCs at 29.52 nm, 22.08 nm, and 12.82 nm, respectively. In this study, the reduction of Methylene Blue (MB) dye was done by NPs and NCs, and their catalytic activities were investigated by a UV-visible spectrophotometer. MB dye showed the maximum absorption peak at 664 nm, and the reaction of MB was reduced using synthesized products with an accompanying color change reaction from blue to colorless within 60 min at 84%, 86%, and 99.5% degradation for ZnO NPs, Fe3O4 NPs, and ZnO/Fe3O4 NCs with rate constants of 0.00035, 0.00065, and 0.0015 Lmol-1S-1. The 2nd order rate constant was observed in this study. The agar disc diffusion method was used to investigate the potential antibacterial activity of synthesized NPs and NCs. This study is best suited for Gram-positive bacteria. NS seed extract-based synthesis of NCs could be an excellent catalyst and biological application for environmental pollution.

Purpose – The present research conducts a thorough review of published literature relevant to earnings management (EM) practices in family firms (FFs), utilizing the Scopus database, intending to identify potential directions for future research. Design/methodology/approach – Through a systematic review, this study focuses on identifying and summarizing trends in publications over the years, the journal outlets, geographical contexts, research methodologies, the temporal evolution of theories and the specific constructs under investigation. Findings – Earlier empirical studies suggest that corporate governance enhances integrity and transparency in FFs, thereby reducing EM practices. Contrarily, compliance with International Financial Reporting Standards (IFRS) seems to offer managers more opportunities for convenient EM rather than restricting such practices. Notably, corporate social responsibility (CSR) practices do not appear to mitigate EM practices consistently. The literature, however, reveals inclusive results and areas requiring deeper exploration for more definitive results. For instance, certain corporate governance mechanisms, such as family-specific social and cultural business characteristics, subjective measures of family businesses, behavioral approaches to family owners' decision-making and directors' personal, psychological and social factors, remain largely untested. Additionally, there is a notable research gap concerning the relationship between IFRS, capital structure and EM. Originality/value – This study’s contributions lie in its comprehensive literature review, identification of research trends and gaps, and its potential to guide future research endeavors in the domain of EM practices in FFs.

Abstract Herein we have reported a fluorescent probe (MB-M) based on MB derivative for Cu2+ ions detection. The probe was well characterized by 1H NMR, 13C NMR and HR-MS spectrum. Probe MB-M showed naked-eyes recognition to Cu2+ as color change from colorless to indigo. The probe exhibited promising features such as high fluorescence and UV–vis selectivity, fast response (5 mint), workable at pH 2–7, and low limit of detection (LOD = 0.33 µM). Probe MB-M was also used for Cu2+ ions imaging in HepG-2 cells and detection in daily life (Test Strip and lake water). Moreover, non-covalent interaction (NCI) and quantum theory of atoms in molecules (QTAIM) analysis were used to study the interaction between MB-M and Cu2+ ions. By examining the electronic characteristics of the complex using natural bond orbital (NBO), electron density difference (EDD), and frontier molecular orbital (FMO) analysis, the sensitivity of MB-M towards Cu2+ ions were investigated. The results illustrated that the interactions between MB-M and Cu2+ ions involved chemisorption.

Background: Functional gastrointestinal disorders (FGIDs) are common disorders characterized by chronic or recurrent gastrointestinal (GI) symptoms, not related to structural or biochemical abnormalities Objective: To determine the frequency of FGIDs in children with complaints of recurrent abdominal pain. Methods: This cross-sectional study was done at the outpatient department of pediatrics, National Institute of Child Health, Karachi, Pakistan from October 2022 to March 2023. Children of either gender aged 1-12 years with complaints of recurrent abdominal pain were analyzed. Socio-demographic characteristics along with presenting complaints and types of FGIDs were noted. Results: In a total of 188 children, the mean age was 7.22±1.96 years while 107 (56.9%) children were boys. The mean duration of symptoms was 2.7±1.8 months. The most frequent presenting complaints were pain during bowel movement, nausea, and epigastric pain reported by 88 (46.8%), 75 (39.9%), and 64 (34.0%) children respectively. Frequency distribution of functional GI disorders revealed that functional abdominal pain was the commonest noted in 72 (38.3%) children while irritable bowel syndrome (IBS) and functional dyspepsia were found among 38 (20.2%) and 37 (19.7%) children respectively. Stratification of functional GI disorders showed a statistically significant relationship (p

Background: The most prevalent and significant arboviral disease affecting humans is dengue. It is spread by the Aedes genus of mosquitoes, which are common in tropical and subtropical regions of the world. Thrombocytopenia occurs commonly among patients suffering from dengue fever, making them more prone to hemorrhagic complications. The predisposing factors leading to thrombocytopenia and bleeding manifestations after dengue infection are not well-known Objective: We attempted to determine the frequency of thrombocytopenia and its co-relation with serum vitamin B12 levels among dengue patients. Methods: This cross-sectional study after taking approval from the hospital ethical committee was carried out at the Department of Medicine PNS Rahat Hospital from June, 2022 to November, 2022. A total of 145 diagnosed patients suffering from dengue fever fulfilling the inclusion criteria were enrolled. Patients with concomitant sepsis, malignancy, autoimmune disorder, hematological disorder, and drugs causing thrombocytopenia were not included in the study. Precise history and biodata were entered in the Pro forma. Patients were investigated for CBC, VIT B12 LEVEL, and other routine investigations. SPSS version 27 was used for data analysis. P-value≤0.05 was considered as statistically significant. Results: Out of a total of 145 patients, 78 (53.8%) were males and 64 (46.2%) were females With a mean age of 38.22±15.93 years.139 patients (95%) had thrombocytopenia. we found a significant positive correlation of thrombocytopenia with Vitamin B12 deficiency (r=0.731, p

Even though methicillin-resistant Staphylococcus aureus (MRSA) is considered a source of nosocomial pneumonia, a few cases have likewise depicted instances of community-acquired pneumonia (CAP) among healthy individuals that were caused by MRSA. Mostly CA-MRSA causes skin and soft tissue infection but can be responsible for severe invasive infection with higher morbidity. Invasive CA-MRSA requires aggressive management to prevent complications. CA-MRSA strains are now recognized as distinct clonal entities that differ from traditional hospital-acquired MRSA (HA-MRSA) strains. The increasing prevalence of MRSA in the community is an important public health problem, which needs increased vigilance in the diagnosis and management of suspected and confirmed staphylococcal infections. We present an intense case of CAP brought about by MRSA with impending respiratory failure in a 19-year-old male, who presented with acute febrile illness with swelling over lips with no co-morbid and insignificant past medical history. Through this case, we intend to increase awareness among physicians about the growing role of CA-MRSA in the population; they should be meticulous in treating patients with CA-MRSA, and be aware of the complications of this disease and the appropriate empiric and target antibiotics regimen for such infections, whenever it is suspected, to enhance better clinical outcomes

Background:Infertility is a disease that affects 8%–12% of couples at their reproductive age. Tubal factors account for 20%–30% of infertility cases. Tubal infertility can be caused by infections, endometriosis, and complications from previous abdominopelvic surgery. Objectives:The objective of the study is to determine the impact of pelvic surgery on future fertility by examining the fallopian tubes for any potential damage using a hysterosalpingography (HSG) examination. Materials and Methods:A cross-sectional study was conducted at Nineveh Governorate, Iraq, in two clinics from March 2022 to September 2022 by examining 317 infertile females using HSG. The exam was done as part of an infertility workup. Results:The results revealed that the study included 317 infertile women patients with a mean age of 28.82 ± 6.9 years (range 17–49 years), of these, 184 patients (58.04%) with primary infertility, and 133 patients (41.95%) with secondary infertility. The years of infertility ranged between 1 and 20 years and a median of 4 years. The history of one or more previous pelvic interventions was recorded in 39 cases (12.3%). The prevalence of tubal abnormalities was noted in 23.1% of cases with previous pelvic intervention and 13.3% in other patients, with an odds ratio of 1.95 at a 95% confidence interval. Tubal abnormalities do not differ significantly according to the type of infertility, and the mean age of infertile women with tubal abnormalities is not statistically different from those with normal tubes. Conclusion:This study concludes that pelvic surgeries, particularly cesarean operations increase the risk of developing subsequent fallopian tube abnormalities and pelvic adhesions, which can lead to infertility.

Background: With the global increase in antibacterial resistance, the challenge faced by developing countries is to utilize the available antibiotics, alone or in combination, against resistant bacterial strains. We aimed to encapsulate the levofloxacin (LVX) into polymeric nanoparticles using biodegradable polymers i.e. Chitosan and PLGA, estimating their physicochemical characteristics followed by functional assessment as nanocarriers of levofloxacin against the different resistant strains of bacteria isolated from biological samples collected from tertiary care hospital in Lahore, Pakistan. Methods: LVX-NPs were synthesized using ion gelation and double emulsion solvent-evaporation method employing chitosan (CS) and poly-lactic-co-glycolic acid (PLGA), characterized via FTIR, XRD, SEM, and invitro drug release studies, while antibacterial activity was assessed using Kirby-Bauer disc-diffusion method. Results: Data revealed that the levofloxacin-loaded chitosan nanoparticles showed entrapment efficiency of 57.14% ± 0.03 (CS-I), 77.30% ± 0.08(CS-II) and 87.47% ± 0.08 (CS-III). The drug content, particle size, and polydispersity index of CS-I were 52.22% ± 0.2, 559 nm ± 31 nm, and 0.030, respectively, whereas it was 66.86% ± 0.17, 595 nm ± 52.3 nm and 0.057, respectively for CS-II and 82.65% ± 0.36, 758 nm ± 24 nm and 0.1, respectively for CS-III. The PLGA-levofloxacin nanoparticles showed an entrapment efficiency of 42.80% ± 0.4 (PLGA I) and 23.80% ± 0.4 (PLGA II). The drug content, particle size and polydispersity index of PLGA-I were 86% ± 0.21, 92 nm ± 10 nm, and 0.058, respectively, whereas it was 52.41% ± 0.45, 313 nm ± 32 nm and 0.076, respectively for PLGA-II. The XRD patterns of both polymeric nanoparticles showed an amorphous nature. SEM analysis reflects the circular-shaped agglomerated nanoparticles with PLGA polymer and dense spherical nanoparticles with chitosan polymer. The in-vitro release profile of PLGA-I nanoparticles showed a sustained release of 82% in 120 h and it was 58.40% for CS-III. Both types of polymeric nanoparticles were found to be stable for up to 6 months without losing any major drug content. Among the selected formulations, CS-III and PLGA-I, CS-III had better antibacterial potency against gram+ve and gram-ve bacteria, except for K. pneumonia, yet, PLGA-I demonstrated efficacy against K. pneumonia as per CSLI guidelines. All formulations did not exhibit any signs of hemotoxicity, nonetheless, the CS-NPs tend to bind on the surface of RBCs. Conclusion: These data suggested that available antibiotics can effectively be utilized as nano-antibiotics against resistant bacterial strains, causing severe infections, for improved antibiotic sensitivity without compromising patient safety.

Learners’ academic performance is one of the most imperative elements of the classroom and a core responsibility of teachers as well as schools. Thus, it is imperative to manage a classroom in a way that is conducive to teaching and learning that can promote high academic performance. Consequently, the study aimed to examine the role of classroom management in enhancing learners' academic performance. A generic qualitative research method approach was employed in the study. A purposive sampling technique was used to select a total of 18 teachers from six government secondary schools to study the phenomenon. Therefore, it was found that effective classroom management plays an important role in learners' performance. During the research, it was found that the no availability of physical resources and poor classroom management contribute to learners, performance. Based on the research findings it was identified that teacher effectiveness, teacher preparedness and teacher social and emotional proficiency, teacher-learner relationship, and learners' motivation, behavior, and discipline are the major factors that also have a strong role on learners' performance. The schools selected to conduct research are lacking resources, and teachers are not as effective and skilled as required. Due to the lack of resources and skills, teachers can't manage classrooms which can enhance learners' academic performance. It is thus mandatory to provide resources to schools and skills and training to teachers.

Abstract Background Water stress is a major danger to crop yield, hence new approaches to strengthen plant resilience must be developed. To lessen the negative effects of water stress on wheat plants, present study was arranged to investigate the role of synergistic effects of biochar, trans-zeatin riboside (t-ZR), and Azospirillum brasilense on soil improvement and enzymatic activity in water-stressed wheat. Results In a three-replication experiment comprising of four treatments (T0: Control, T1: Drought stress (DS), T2: DS + t-ZR with biochar, T3: DS + A. brasilense with biochar), we observed notable improvements in soil quality and enzymatic activities in water-stressed wheat plants with the application of t-ZR and A. brasilense with biochar. In drought stress, Treatment having the application of A. brasilense with biochar performs best as compared to the other and significant increased the enzymatic activities such as peroxidase (7.36%), catalase (8.53%), superoxide dismutase (6.01%), polyphenol oxidase (14.14%), and amylase (16.36%) in wheat plants. Different enzymatic activities showed different trends of results. Soil organic C, dissolved organic C, dissolved organic N also enhanced 29.46%, 8.59%, 22.70% respectively with the application of A. brasilense with biochar under drought stress condition. Conclusions The synergistic action of A. brasilense and biochar creates an effective microbiological environment that supports essential plant physiological processes during drought stress. This enhancement is attributed to improved soil fertility and increased organic matter content, highlighting the potential of these novel strategies in mitigating water stress effects and enhancing crop resilience.

Chronic disease is an irregular change inside or outside of the tissues and organs that results in accumulation, local damage, and inflammation or irritation. Suaeda aegyptiaca (S. aegyptiaca) is a plant that has been used for a long time for the treatment of human diseases. Therefore, the aim of this present research study is to prepare various plant extracts and screen their antioxidant activity spectroscopically, and later on to isolate antioxidant biomarkers from the significantly highest active crude extract of the aerial parts of S. aegyptiaca. To attain the present objectives, different crude extracts were prepared from the aerial parts of S. aegyptiaca by using a maceration method. The antioxidant and cytotoxic activities of the prepared aerial crude extracts of S. aegyptiaca were determined by 2,2-diphenyl-1-1-picrylhydrazyl (DPPH) and brine shrimp lethality (BSL) methods, respectively. All the prepared aerial crude extracts of the particular plant at six different concentrations showed significant antioxidant activity against the DPPH. The ethyl acetate crude extract showed the highest antioxidant activity, and the lowest activity was in the butanol extract. However, all the aerial crude extracts of S. aegyptiaca were prepared at different concentrations did not show any activity against the BSL method. Based on the antioxidant activity results, the ethyl acetate extract was selected for the isolation of antioxidant compounds. The extract was purified using column chromatography by using different solvent ratios. A series of test tubes were collected with a volume of 3 mL and depending on the similar retention mobility (Rf) behavior, a total of twelve fractions were prepared. Similarly, the antioxidant activity of the obtained twelve fractions from column chromatography was determined by the same DPPH method. All the fractions showed significant antioxidant activity. Among the fractions from the column, fraction 6 gave the highest antioxidant activity and the lowest was fraction 1. In conclusion, all the aerial extracts showed promising activities against DPPH and the fraction with the highest antioxidant activity could be used as natural antioxidant biomarkers to prevent cell damage.

The adoption of cloud computing has become increasingly widespread across various domains. However, the inherent security vulnerabilities of cloud computing pose significant risks to its overall safety. Consequently, intrusion detection systems (IDS) play a pivotal role in identifying malicious activities within a cloud system. The considerable volume of network traffic data may contain redundant and irrelevant features that can impact the classification performance of the classifier. In addition, the complexity and time consumption increase while processing such a substantial volume of data in the cloud intrusion detection process. To enhance the performance of the IDS, this study proposes a hybrid feature selection approach, combining two bio-inspired algorithms, namely the grasshopper optimization algorithm (GOA) and the genetic algorithm (GA). The combination of these two algorithms ensures a more efficient search for optimal solutions. A random forest (RF) classifier is trained using those optimal features. Moreover, the proposal addresses the challenge of imbalanced data by employing a hybrid approach: over-sampling the minority classes using an adaptive synthetic (ADASYN) algorithm, while implementing random under-sampling (RUS) for the majority class as needed. This integrated strategy significantly influences each category, enhancing the true positive rate (TPR) while minimizing the false positive rate (FPR), thus improving the overall system performance. The proposed approach was evaluated using three datasets: UNSW-NB15, CIC-DDoS2019, and CIC Bell DNS EXF 2021. The recorded accuracies for these datasets were 98%, 99%, and 92%, respectively. The hybrid feature selection-based IDS demonstrated superior performance in multi-class classification, along with exemplary results for individual classes within the datasets. The proposed strategy exhibited a marked superiority with the random forest classifier, especially when compared to other classifiers including SVM, LR, FLN, LSTM, AlexNet, DNN, DBN, DT, and XGBoost. Moreover, this performance remained consistent and commendable even when benchmarked against contemporary state-of-the-art methodologies across multiple evaluation metrics.

Objective: To assess the awareness level and need perception of soft skills among general dental practitioners (GDPs), dental residents and specialists. Study Design: Mixed method study Place and Duration of Study: Armed Forces Institute of Dentistry (AFID), Rawalpindi and 28 Military Dental Centre (28 MDC), Lahore Pakistan, from Mar to Sep 2020. Methodology: A mixed method study research design with descriptive and explorative strategies was used to formulate a survey questionnaire and in-depth interviews for participants aged 24-60, medically fit, with a minimum of one year of practical experience, and willing to participate. Results: 136(74.37%) knew the basic concept of soft skills being non-technical skills, as they classified their awareness of soft skills as ‘Partially Aware’. Only 9(4.91%) responded as being ‘Expert’ in this topic. 38(20.76%) were totally ‘Unaware’. Regarding the need for teaching soft skills to dental students, 171(93.44%) participants wanted soft skills teaching to begin in the undergraduate years. Conclusion: Participating dental professionals understood the basic concept of soft skills but needed a formal knowledge of what it entails. There is a dire need for the dental professional to have a satisfactory knowledge of soft skills to improve their overall job performance.

Recent years have witnessed security as a great concern in vehicular networks (VANET). Particularly, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can jeopardize the network by broadcasting a storm of packets. Correspondingly, the network resources are jammed with malicious traffic. In this connection, the existing research presented various techniques to cope with DoS and DDoS attacks. Different from those traditional approaches, this study proposes an Intelligent Intrusion Detection System (IDS) by leveraging Machine Learning (ML). The proposed IDS utilizes a publicly available dataset on the application layer for mitigating DDoS attacks. The designed ML-based IDS relies on combining both the Random Projection (RP) and Randomized Matrix Factorization (RMF) methods to achieve the best results for enhancing the detection capabilities of the IDS. This amalgamation enhances the system's detection capabilities by extracting and analyzing meaningful features from network traffic data. Experimental validation of our approach involves a comprehensive evaluation of various ML models, including Extra Tree Classifier (ETC), Logistic Regression (LR), and Random Forest (RF). Remarkably, the combined accuracy of these models yields an average system accuracy of 0.98, surpassing existing methods. Unlike conventional approaches, our proposed IDS excels in efficiency and exhibits notable performance in detecting DoS and DDoS attacks in VANET. This proficiency ensures the integrity and safety of vehicle communications. Thus, our research substantially contributes to the vehicular network security field. The presented findings establish a foundation for future advancements in securing connected vehicles.

Objective To develop and content validate a questionnaire to assess the financial and functional impact of major lower limb amputation in patients with diabetes-related foot disease.Design Prospective observational study.Setting This study was conducted at a tertiary care centre in Pakistan.Participants We conducted a thorough literature review and a group interview with 10 participants, resulting in domain identification and item generation. The group included seven patients with diabetes-related foot disease who underwent major lower limb amputation and three caregivers. Subsequently, a focused group discussion was held to assess overlap and duplication among the items, and two rounds of content validation were carried out by five content and five lay experts in both English and Urdu. Question items with a Content Validity Index (CVI) score of >0.79 were retained, items with a CVI score between 0.70 and 0.79 were revised and items with a CVI score of

Learners’ social skills enhancement is one of the key components of the classroom and the basic obligation of a teacher and school. Thus, it is imperative to manage the classroom in a way that can offer a comfortable teaching-learning environment to learners. Therefore, the study aimed to examine to role of classroom management in enhancing learners' social skills. The interpretive research design was utilized to describe the qualitative data, and a generic qualitative research approach was employed to undertake the study. A total of 18 teachers from six secondary schools were selected by using a purposive sampling technique. It was instituted that classroom management and the social environment of the classroom play a vital role in learners' social skills enhancement. Based on research findings, physical resources, teacher role, class rule, conduct and regulation, socioeconomic status of learners, family background, and poverty are the leading factors that have a strapping role on learners’ skills enhancement. The selected schools to accomplish research are lacking resources, however, teachers are considered as a core in teaching-learning. It is, therefore, obligatory to provide resources to classrooms, skills to teachers, and address learners' socioeconomic status to achieve educational goals and enhance social skills.

Euglycemic diabetic ketoacidosis (EuDKA), is an uncommon but potentially fatal medical emergency characterized by metabolic acidosis, ketosis, and either normal or near normal blood glucose level. The Diagnosis is delayed due to a lack of hyperglycemia leading to adverse consequences. It’s not a routine diagnosis but patients with both types of diabetes mellitus may suffer from it. Since august 2014, after the inclusion of sodium-glucose cotransporter-2 inhibitors (SGLT2i) for the management of diabetes mellitus, the incidence of euglycemia DKA has raised. The other conditions that may contribute to this metabolic disorder include fasting, chronic liver disease, pregnancy, gastroparesis, bariatric surgery, glycogen storage disease, cocaine intoxication, and insulin pump failure. Euglycemic DKA, a diagnosis of exclusion, should be considered in any unexplained metabolic acidosis among diabetic patients, who report to the hospital with nonspecific symptoms not suggesting DKA and have been taking sodium-glucose cotransporter-2 inhibitors. Here we are describing a case of ketoacidosis with normoglycemia caused by empagliflozin in a female suffering from type 2 diabetes mellitus to raise awareness of physicians to minimize the delays in the identification of this life-threatening metabolic disorder.

Abstract Transmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of ’scalograms’, derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.