Object Recognition Research Articles

Low-Resolution Object Recognition With Cross-Resolution Relational Contrastive Distillation

Low-Resolution Object Recognition With Cross-Resolution Relational Contrastive Distillation

BNVGLENET: Hypercomplex Bangla handwriting character recognition with hierarchical class expansion using Convolutional Neural Networks

Object recognition technology has made significant strides where recognizing handwritten Bangla characters including symbols, compounds form, etc. remains a challenging problem due to the prevalence of cursive writing and many ambiguous characters. The complexity and variability of the Bangla script, and individual’s unique handwriting styles make it difficult to achieve satisfactory performance for practical applications, and the best existing recognizers are far less effective than those developed for English alpha-numeric characters. In comparison to other major languages, there are limited options for recognizing handwritten Bangla characters. This study has the potential to improve the accuracy and effectiveness of handwriting recognition systems for the Bengali language, which is spoken by over 200 million people worldwide. This paper aims to investigate the application of Convolutional Neural Networks (CNNs) for recognizing Bangla handwritten characters, with a particular focus on enlarging the recognized character classes. To achieve this, a novel challenging dataset for handwriting recognition is introduced, which is collected from numerous students’ handwriting from two institutions. A novel convolutional neural network-based approach called BNVGLENET is proposed in this paper to recognize Bangla handwritten characters by modifying the LeNet-5 and combining it with the VGG architecture, which has the advantage of significantly identifying the characters from Bengali handwriting. This study systematically evaluated the performance of models not only on custom novel dataset but also on the publicly available Bangla handwritten character dataset called the Grapheme dataset. This research achieved a state-of-the-art recognition accuracy of 98.2% on our custom testing vowel-consonant class and 97.5% on the custom individual class. The improvements achieved in this study bridge a notable disparity between the practical needs and the actual performance of Bangla handwritten character recognition systems.

The role of gender differences in the outcome of juvenile social isolation: Emphasis on changes in behavioral, biochemical and expression of nitric oxide synthase genes alteration

Social isolation can cause serious problem in performance of individuals in community. As gender differences may cause variation results in the severity of depressive behavior and response of patients to therapy, the impact of gender and the interaction of the level of endocrine secretion in depression were investigated in this study. Wistar rats of both sexes were subjected to post-weaning social isolation (PWSI) conditions and, together with the control group, experienced several behavioral tests including open-field Test (OFT), elevated plus maze (EPM), force swimming test (FST), splash test and novel object recognition test (NOR). Hippocampal tissue was isolated to measure biochemical factors such as nitric oxide level, FRAP amount, MDA level. In addition, real-time-PCR test was used to quantify the genes expression level of inducible nitric oxide synthase (iNOS) and neuronal nitric oxide synthase (nNOS). On the other hand, sexual hormone levels in blood were measured. Both cognitive and behavioral f unctions were declined as the result of PWSI induction in male and diestrus female rats. The consequent surge of estradiol during estrous phase seems to suppress the accumulation of reactive oxygen species (ROS), and modulate iNOS and nNOS expression. In conclusion, while the pattern of PWSI in surge cellular antioxidants, raising cellular ROS level is gender-specific, this alleviation was in relation with the drop of estradiol and unrelated with testosterone level.

Novel α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator LT-102: A promising therapeutic agent for treating cognitive impairment associated with schizophrenia.

We aimed to evaluate the potential of a novel selective α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator, LT-102, in treating cognitive impairments associated with schizophrenia (CIAS) and elucidating its mechanism of action. The activity of LT-102 was examined by Ca2+ influx assays and patch-clamp in rat primary hippocampal neurons. The structure of the complex was determined by X-ray crystallography. The selectivity of LT-102 was evaluated by hERG tail current recording and kinase-inhibition assays. The electrophysiological characterization of LT-102 was characterized by patch-clamp recording in mouse hippocampal slices. The expression and phosphorylation levels of proteins were examined by Western blotting. Cognitive function was assessed using the Morris water maze and novel object recognition tests. LT-102 is a novel and selective AMPAR potentiator with little agonistic effect, which binds to the allosteric site formed by the intradimer interface of AMPAR's GluA2 subunit. Treatment with LT-102 facilitated long-term potentiation in mouse hippocampal slices and reversed cognitive deficits in a phencyclidine-induced mouse model. Additionally, LT-102 treatment increased the protein level of brain-derived neurotrophic factor and the phosphorylation of GluA1 in primary neurons and hippocampal tissues. We conclude that LT-102 ameliorates cognitive impairments in a phencyclidine-induced model of schizophrenia by enhancing synaptic function, which could make it a potential therapeutic candidate for CIAS.

Brain areas interconnected to ventral pathway circuits are independently able to induce enhancement in object recognition memory and cause reversal in object recognition memory deficit.

Ventral pathway circuits are constituted by the interconnected brain areas that are distributed throughout the brain. These brain circuits are primarily involved in processing of object related information in brain. However, their role in object recognition memory (ORM) enhancement remains unknown. Here, we have studied on the implication of these circuits in ORM enhancement and in reversal of ORM deficit in aging. The brain areas interconnected to ventral pathway circuits in rat brain were activated by an expression of a protein called regulator of G-protein signaling 14 of 414 amino acids (RGS14414). RGS14414 is an ORM enhancer and therefore used here as a gain-in-function tool. ORM test and immunohistochemistry, lesions, neuronal arborization, and knockdown studies were performed to uncover the novel function of ventral pathway circuits. An activation of each of the brain areas interconnected to ventral pathway circuits individually induced enhancement in ORM; however, same treatment in brain areas not interconnected to ventral pathway circuits produced no effect. Further study in perirhinal cortex (PRh), area V2 of visual cortex and frontal cortex (FrC), which are brain areas that have been shown to be involved in ORM and are interconnected to ventral pathway circuits, revealed that ORM enhancement seen after the activation of any one of the three brain areas was unaffected by the lesions in other two brain areas either individually in each area or even concurrently in both areas. This ORM enhancement in all three brain areas was associated to increase in structural plasticity of pyramidal neurons where more than 2-fold higher dendritic spines were observed. Additionally, we found that an activation of either PRh, area V2, or FrC not only was adequate but also was sufficient for the reversal of ORM deficit in aging rats, and the blockade of RGS14414 activity led to loss in increase in dendritic spine density and failure in reversal of ORM deficit. These results suggest that brain areas interconnected to ventral pathway circuits facilitate ORM enhancement by an increase in synaptic connectivity between the local brain area circuits and the passing by ventral pathway circuits and an upregulation in activity of ventral pathway circuits. In addition, the finding of the reversal of ORM deficit through activation of an interconnected brain area might serve as a platform for developing not only therapy against memory deficits but also strategies for other brain diseases in which neuronal circuits are compromised.

A Spatial Memory Deficit in Male But Not Female Rats After Neonatal Diazepam Exposure: A New Model for Developmental Sedative Neurotoxicity.

Developmental anesthetic neurotoxicity is well described in animal models for GABAergic, sedating drugs. Here we investigate the role of the benzodiazepine, diazepam on spatial and recognition memory of young adult rats after neonatal exposure. On postnatal day 7, male (n = 30) and female (n = 30) rats were exposed to diazepam (30 mg/kg intraperitoneally) or vehicle. On postnatal day 42, animals started a series of behavioral tests including Barnes maze (spatial memory), object recognition battery (recognition memory), and open field and elevated plus maze (anxiety). In a separate cohort, blood gases were obtained from diazepam-exposed animals and compared to isoflurane-exposed animals (1 MAC for 4 hours). Male animals exposed to diazepam had impaired performance in the Barnes maze and were unable to differentiate the goal quadrant from chance (1-sample t test; tdiazepam/male (14) = 1.49, P = .158). Female rats exposed to diazepam performed the same as the vehicle controls ( tdiazepam/female (12) = 3.4, P = .005, tvehicle/female (14) = 3.62, P = .003, tvehicle/male (13) = 4.76, P < .001). There were no statistical differences in either males or females in measures of recognition memory, anxiety, or locomotor activity in other behavioral tests. Physiologic measurements of arterial blood gases taken from animals under sedation with diazepam were much less aberrant than those exposed to the volatile anesthetic isoflurane by t test (pH diazepam [M = 7.56, standard deviation {SD} = 0.11] versus pH Isoflurane [M = 7.15, SD = 0.02], t (10) = 8.93, P < .001; Paco 2diazepam [M = 32.8 mm Hg, SD = 10.1] versus Paco 2Isoflurane [M = 91.8 mm Hg, SD = 5.8], t (10) = 8.93, P < .001). The spatial memory results are consistent with volatile anesthetic suggesting a model in which development of the GABA system plays a critical role in determining susceptibility to behavioral deficits.

Nigella Sativa Supplementations Attenuate Recognition Memory and Cellular Morphometric Impairments Induced by Toluene Administration in Mice

Toluene exposure has been associated with detrimental effects on the central nervous system. Discovering natural products that can offer neuroprotection following toluene exposure is an essential alternative. Nigella sativa (NS), a popular natural supplement, is a good candidate due to its antioxidant and neuroprotective properties. The study aimed to investigate the protective potentials of NS against toluene, on recognition memory performance in the Novel Object Recognition (NOR) test and cellular morphometric measurements of hippocampal CA1 pyramidal neurons. Adult male ICR mice (n=30) were randomly divided into five groups: Group 1 (G1) - corn oil (CO), G2 – Toluene (TOL), G3 - Toluene and NS seed suspension (TOL-NSS), G4 - Toluene and NS oil (TOL-NSO), G5 - Toluene and thymoquinone (TOL-TQ). NS supplementations were administered orally once daily for 14 consecutive days while 500 mg/kg b.w. toluene was administered intraperitoneally from day 8 until day 14. Behavioural NOR test was conducted. Subsequently, mice were intracardially perfused and harvested brains were histologically processed. The somatic size and shape of hippocampal CA1 pyramidal neurons were quantified according to specific morphometric parameters. Toluene reduced recognition memory performance of mice and somatic size of hippocampal CA1 pyramidal neurons. Contradictorily, TQ, NSO, and NSS improved mice recognition memory and somatic size of hippocampal CA1 pyramidal neurons. Somatic shape of hippocampal CA1 pyramidal was unaffected by the various treatments. Although non-significant differences were observed, the results indicate the tendency for toluene to cause impairment, while NS supplementations to improve mice recognition memory performance and brain hippocampal neuronal structure.

A Novel Tetrapeptide Ala-Phe-Phe-Pro (AFFP) Derived from Antarctic Krill Prevents Scopolamine-Induced Memory Disorder by Balancing Lipid Metabolism of Mice Hippocampus.

Memory impairment is a serious problem with organismal aging and increased social pressure. The tetrapeptide Ala-Phe-Phe-Pro (AFFP) is a synthetic analogue of Antarctic krill derived from the memory-improving Antarctic krill peptide Ser-Ser-Asp-Ala-Phe-Phe-Pro-Phe-Arg (SSDAFFPFR) after digestion and absorption. The objective of this research was to assess the neuroprotective effects of AFFP by reducing oxidative stress and controlling lipid metabolism in the brains of mice with memory impairment caused by scopolamine. The 1H Nuclear magnetic resonance spectroscopy results showed that AFFP had three active hydrogen sites that could contribute to its antioxidant properties. The findings from in vivo tests demonstrated that AFFP greatly enhanced the mice's behavioral performance in the passive avoidance, novel object recognition, and eight-arm maze experiments. AFFP reduced oxidative stress by enhancing superoxide dismutase activity and malondialdehyde levels in mice serum, thereby decreasing reactive oxygen species level in the mice hippocampus. In addition, AFFP increased the unsaturated lipid content to balance the unsaturated lipid level against the neurotoxicity of the mice hippocampus. Our findings suggest that AFFP emerges as a potential dietary intervention for the prevention of memory impairment disorders.

Object Detection and Speech Recognition Using Machine Learning

Abstract: This paper presents a comprehensive project that integrates computer vision, natural language processing, and deep learning techniques to enhance object and speech recognition capabilities. The proposed system leverages the OpenCV library to identify objects within an input image, subsequently generating a new image annotated with the names of the recognized objects. Furthermore, the project incorporates the pyttsx3 module to convert the identified object names into speech, providing an additional layer of accessibility. The system extends its functionality by incorporating a contextual summarization component. Upon user input of contextual information related to the recognized objects, the system utilizes a language model, Large Language Model (LLM), to summarize the provided context. This summarization process contributes a Retrieval Augmented Generation (RAG) element, offering a quick and efficient overview of the given information. The seamless integration of object identification, speech synthesis, and contextual summarization enhances the user experience, making the system versatile and accessible. The proposed solution finds application in various domains such as assistive technology, image recognition, and natural language processing. The experimental results demonstrate the effectiveness and accuracy of the system, showcasing its potential contributions to the field of machine learning and deep learning application

TENSOR AND VECTOR APPROACHES TO OBJECTS RECOGNITION BY INVERSE FEATURE FILTERS

The investigation of the extraction of image objects features by filters based on tensor and vector data presentation is considered. The tensor data is obtained as a sum of rank-one tensors, given by the tensor product of the vector of lexicographic representation of image fragments pixels with itself. The accumulated tensor is approximated by one rank tensor obtained using singular values decomposition. It has been shown that the main vector of the decomposition can be considered as the object feature vector. The vector data is obtained by accumulating analogous vectors of image fragments pixels. The accumulated vector is also considered as an object feature. The filter banks of a set of objects are obtained by regularized inversion of the matrices compiled by object features vectors. Optimized regularization of the inversion is used to expand the regions of object features capture with minimal error. The object fragments and corresponding feature vectors are selected through a training iterative process. The tensor and vector approaches create two channels for recognition. High efficiency of object recognition can be achieved by choosing the filter capture band and creating filter branches according to the given bands. The filters create a convolutional network to recognize a set of objects. It has been shown that the obtained filters have an advantage over known correlation filters when recognizing objects with small fragments.

Differentiate the Varieties of Plants Using Leaf Images with YOLOv4

There are many types of plant, not all recognized by people. Indonesia has many varieties of plants across the country, from Sabang to Merauke. Often, we find some fruits or plants that grow in different areas called by different names. The goal of this research is to create an application that can recognize plant types from their characteristics. These characteristics can be leaves, fruit, flowers or roots. Current research focuses on distinguishing several types of plants including their varieties using leaf images. It is hoped that apart from being able to determine the mango plant, we can also determine the type of mango, such as manalagi, harum manis, as well as guava and orange. To be able to recognize these types of plants, an object recognition algorithm based on CNN (Convolutional Neural Network) will be used. The algorithm that will be used is YOLO (You Only Look Once) version 4. Method in this research includes data collection, all data was taken using webcam or mobile phone camera. The next step is data preparation and creating a bounding box. Next step is creating model using training data and the last step was compare model with testing data, The result we got is really encouraging with almost 100 percent accuracy. The lowest accuracy is 98 percent for jambu air. This research show us that YOLOv4 can be used to differentiate varieties of plants.

Retina‐Inspired Nanofluidic Membranes for Underwater Visual Imaging Based on Active Ion Transport

AbstractThrough evolution, biological organisms have developed ways to sense light using ion channels, which holds several advantages, such as energy efficiency and water resistance, over humanmade optoelectronic devices. Herein, a retina‐inspired nanofluidic system is presented with a Janus heterogeneous membrane (J‐HM), which can achieve underwater visual imaging through light‐driven active ion transport. The J‐HMs are obtained through sequentially assembled WS2, and a kind of metal–organic framework nanosheets via the reaction between 2,3,6,7,10,11‐hexahydroxytriphenylene hydrate (HHTP) and Cu2+ (Cu‐HHTP). Due to the formed intramembrane electric field caused by the efficient charge separation under illumination, a photovoltaic driving force is generated for active ion transport from Cu‐HHTP to WS2. Furthermore, the unidirectionally active ion transport can be enhanced by self‐diffusion under a concentration gradient. The J‐HM with a single‐pixel design shows a nearly linear response with light intensity and has enough resolution for basic object recognition as well as a long‐term memory after data processing using a defined pixelated matrix, which can pave an avenue for designing more intelligent sensing systems.

Analysis the improvements of YOLOv5 algorithms: NRT-YOLO, MR-YOLO and YPH-YOLOv5

Computer Vision (CV) is a fundamental aspect of artificial intelligence, with applications spanning multiple domains. The YOLO (You Only Look Once) algorithm has significantly contributed to real-time object recognition in CV. This paper explores the evolution of the YOLO algorithm, focusing on the improvements brought by three specialized variants: NRT-YOLO, MR-YOLO, and TPH-YOLOv5. NRT-YOLO addresses the challenge by introducing the C3NRT module, enhancing precision while maintaining low complexity. MR-YOLO optimizes YOLOv5 for industrial quality control, improving speed and accuracy. TPH-YOLOv5 enhances object detection in drone-captured images by introducing additional prediction heads and transformer modules. Despite these advancements, YOLO algorithms have limitations, including difficulty in detecting small objects and issues in complex scenes. Nevertheless, the research sheds light on the continuous evolution of YOLO algorithms, offering insights into real-time object detection, with applications in manufacturing, healthcare, transportation, and more. The significance of this research lies in showcasing the adaptability and potential of the YOLO framework. As YOLO continues to evolve, it promises to revolutionize various industries and applications, providing robust, real-time object detection solutions.

Short Working Memory Impairment Associated with Hippocampal Microglia Activation in Chronic Hepatic Encephalopathy.

Hepatic encephalopathy (HE) is a major neuropsychological condition that occursas a result of impaired liver function. It is frequently observed in patients with advanced liver disease or cirrhosis. Memory impairment is among the symptoms of HE; the pathophysiologic mechanism for this enervating condition remains unclear. However, it is possible that neuroinflammation may be involved, as recent studies have emphasized such phenomena. Therefore, the aim of the present study is to assess short working memory (SWM) and examine the involvement of microglia in a chronic model of HE. The study was carried out with male Wistar rats that were induced by repeated thioacetamide (TAA) administration (100 mg/kg i.p injection for 10 days). SWM function was assessed through Y-maze, T-Maze, and novel object recognition (NOR) tests, together with an immunofluorescence study of microglia activation within the hippocampal areas. Our data showed impaired SWM in TAA-treated rats that was associated with microglial activation in the three hippocampal regions, and which contributed to cognitive impairment.

Coupling thermogalvanic and piezoresistive effects in a robust hydrogel for Deep-Learning-Assisted Self-Powered sign language and object recognition

The human hand provides rich sensory information for proper interaction with the environment. However, designing passive hand-sensing arrays with robust mechanical properties, high conductivity, ease of integration and reliability remains a long-term challenge. Here, we develop a robust dynamic crystalline ion-injected thermoelectric gel (DCI-TEG) via a simple solvent-exchange salt-out strategy. The resultant DCI-TEG exhibits high tensile strength (3.8 MPa), toughness (7.5 MJ m−3), and ionic conductivity of 1.84 S m−1. Benefiting from non-covalent crosslinking, the gel electrolyte and electrodes can be fully integrated into modules with tight physical and electrical contact, facilitating a stable and reliable signal output. By coupling thermogalvanic and piezoresistive effects, the DCI-TEG permits self-powered timely sensing of pressure using current signals. By implanting ionic thermoelectric module arrays on a glove, a wearable pressure sensing route is demonstrated with the merits of self-power, low cost and simple preparation. With the help of deep learning algorithms, a self-powered recognition for sign language and objects is realized in wearable/portable scenarios by active high-accuracy pressure detection. This work gives people a new direction toward barrier-free passive communication and human–machine interaction in the next-generation artificial intelligence.

Saliency information and mosaic based data augmentation method for densely occluded object recognition

Saliency information and mosaic based data augmentation method for densely occluded object recognition

Enhancing Convolutional Neural Networks via separately trained kernels for digit recognition

This paper introduces a novel methodology for pre-training neural networks. Instead of the traditional approach of running a single classifier for all objects, the method used in the research constructs individual classifiers for each object to extract unique features. These classifiers, in essence, act as a series of binary classifiers, each pre-training a distinct set of convolutional kernels. These individually trained kernels are then combined and processed by a comprehensive classifier. The methodology leverages the power of individual feature extraction and collaborative processing to enhance the overall performance of the classifier. The results demonstrate that this innovative pre-training approach leads to a significant improvement in classification performance, yielding higher accuracy compared to conventional methods. It underscores the benefits of incorporating individual object feature extraction and combined processing in the pre-training phase of neural network-based classification tasks. The study opens new paths for improving pre-training methods in neural networks, with potential applications in various fields that require high-accuracy object recognition. Future work will delve deeper into the potential challenges associated with model complexity and overfitting, and will also include a more comprehensive evaluation using extensive training, cross-validation, and independent test sets. This will further validate the effectiveness of our proposed pre-training methodology.

Preference aggregation method in determining brightness threshold values for object recognition on optical images

Relevance. The need to automate the processing large volumes of geophysical information extracted from images obtained through aerial photography or space remote sensing. The main stage of image processing for object recognition is segmentation. It consists in dividing the image into two homogeneous, clearly distinguishable non-overlapping areas with known boundaries. Threshold segmentation methods are simple but effective and therefore popular. Aim. To describe the experimental studies results of application of the robust method of interval fusion with preference aggregation previously developed by the authors for selecting threshold values when segmenting objects in optical images. Objects. Coastline images taken by Earth remote sensing satellites. Methods. The analyzed image is divided into equal bands. Based on the brightness histogram of each band, its characteristic brightness interval is determined. For the obtained brightness intervals, the fusion result is calculated using the interval fusion with preference aggregation method. The latter is used as a threshold brightness value when forming a segmented image. Results. The results of experimental studies of 100 satellite images of the coastline showed that the proposed method provided correct separation of land and sea regions in 84 images. And the traditional methods of maximum entropy, arithmetic averaging and Otsu showed correct results only in 27, 76 and 73 cases, respectively. Evaluations of such the metrics as Precision, Recall and deviation of the calculated threshold from a known correct value showed the dominance of the proposed method over other tested ones in terms of segmentation quality.

Odors Detection and Recognition Based on Intelligent E-Nose

Electronic noses have become more common as a result of developments in sensor technology, machine learning (ML), and Artificial Intelligence (AI). At the moment, the majority of e-nose research is conducted in laboratories; access from other locations is not possible with e-nose. Very few real-time smell detection applications, including tiny drones equipped with commercial gas sensors or biosensors made of insect antennas, have been created. The scope of this work is to design an intelligent E-Nose for odors detection. Smell Inspector developer kit has been used to get measurements. The Smell Inspector consists of sensors for temperature and humidity in addition to four smell iX16 chips. The Smell Inspector creates digital fragrance fingerprints using a variety of separate gas detectors. The approach for object recognition and classification using artificial intelligence techniques is presented in this paper. These techniques include Machine Learning (ML), clustering, and regression algorithms. Using the K-Nearest Neighbor (KNN) algorithm, the experimental results' array sensors were able to recognize; clean air, onion, garlic, coffee, spices, lemon, vinegar, gasoline, petrol, diesel, and perfumes with 78% accuracy rate.

Liraglutide versus pramlintide in protecting against cognitive function impairment through affecting PI3K/AKT/GSK-3β/TTBK1 pathway and decreasing Tau hyperphosphorylation in high-fat diet- streptozocin rat model.

The American Diabetes Association guidelines (2021) confirmed the importance of raising public awareness of diabetes-induced cognitive impairment, highlighting the links between poor glycemic control and cognitive impairment. The characteristic brain lesions of cognitive dysfunction are neurofibrillary tangles (NFT) and senile plaques formed of amyloid-β deposition, glycogen synthase kinase 3 beta (GSK3β), and highly homologous kinase tau tubulin kinase 1 (TTBK1) can phosphorylate Tau proteins at different sites, overexpression of these enzymes produces extensive phosphorylation of Tau proteins making them insoluble and enhance NFT formation, which impairs cognitive functions. The current study aimed to investigate the potential contribution of liraglutide and pramlintide in the prevention of diabetes-induced cognitive dysfunction and their effect on the PI3K/AKT/GSK-3β/TTBK1 pathway in type 2 diabetic (T2D) rat model. T2D was induced by administration of a high-fat diet for 10weeks, then injection of a single dose of streptozotocin (STZ); treatment was started with either pramlintide (200μg/kg/day sc) or liraglutide (0.6mg/kg/day sc) for 6weeks in addition to the HFD. At the end of the study, cognitive functions were assessed by novel object recognition and T-maze tests. Then, rats were sacrificed for biochemical and histological assessment of the hippocampal tissue. Both pramlintide and liraglutide treatment revealed equally adequate control of diabetes, prevented the decline in memory function, and increased PI3K/AKT expression while decreasing GSK-3β/TTBK1 expression; however, liraglutide significantly decreased the number of Tau positive cells better than pramlintide did. This study confirmed that pramlintide and liraglutide are promising antidiabetic medications that could prevent associated cognitive disorders in different mechanisms.