Introduction: Obesity has emerged as a critical public health issue in India, with a notable rise in prevalence, particularly among women. The relationship between obesity and cognitive function remains underexplored, especially in non-Western populations. This study aims to investigate the association between obesity and cognitive function in middle-aged women in Chennai, South India. Methods:A cross-sectional study was conducted among 140 healthy female subjects aged 40-60 years, categorized into four groups based on BMI: Normal (18.5-22.9 kg/m²), At Risk (23-24.9 kg/m²), Obese 1 (25.0-29.9 kg/m²), and Obese 2 (>30 kg/m²). Anthropometric measurements were taken, and cognitive function was assessed using the Addenbrooke’s Cognitive Examination (ACE) questionnaire, covering domains such as attention/orientation, memory, verbal fluency, language, and visuo-spatial abilities. Data analysis was performed using R statistical software, with ANOVA and post hoc tests to compare groups, and Pearson correlation to examine associations between anthropometric values and cognitive scores. Results:Significant differences were found in height, weight, BMI, waist circumference, hip circumference, and neck circumference across BMI groups (p < 0.001). Cognitive scores significantly decreased with increasing BMI across all domains (p = 0.0001). Orientation/Attention, Memory, Verbal Fluency, Language, and Visuo-Spatial Ability scores were lowest in the Obese 2 group. Strong negative correlations were observed between BMI (r = -0.84, p = 0.0001), waist circumference (r = -0.49, p = 0.0001), hip circumference (r = -0.54, p = 0.0001), neck circumference (r = -0.41, p = 0.0001), and cognitive scores. The waist-hip ratio did not show a significant correlation with cognitive scores. Conclusion:This study demonstrates a significant association between obesity and cognitive impairment in middle-aged women. Reducing excess weight through interventions such as diet and physical activity may improve cognitive function and reduce morbidity and mortality. Early intervention is crucial for optimal benefits
Introduction: Obesity has emerged as a critical public health issue in India, with a notable rise in prevalence, particularly among women. The relationship between obesity and cognitive function remains underexplored, especially in non-Western populations. This study aims to investigate the association between obesity and cognitive function in middle-aged women in Chennai, South India. Methods:A cross-sectional study was conducted among 140 healthy female subjects aged 40-60 years, categorized into four groups based on BMI: Normal (18.5-22.9 kg/m²), At Risk (23-24.9 kg/m²), Obese 1 (25.0-29.9 kg/m²), and Obese 2 (>30 kg/m²). Anthropometric measurements were taken, and cognitive function was assessed using the Addenbrooke’s Cognitive Examination (ACE) questionnaire, covering domains such as attention/orientation, memory, verbal fluency, language, and visuo-spatial abilities. Data analysis was performed using R statistical software, with ANOVA and post hoc tests to compare groups, and Pearson correlation to examine associations between anthropometric values and cognitive scores. Results:Significant differences were found in height, weight, BMI, waist circumference, hip circumference, and neck circumference across BMI groups (p < 0.001). Cognitive scores significantly decreased with increasing BMI across all domains (p = 0.0001). Orientation/Attention, Memory, Verbal Fluency, Language, and Visuo-Spatial Ability scores were lowest in the Obese 2 group. Strong negative correlations were observed between BMI (r = -0.84, p = 0.0001), waist circumference (r = -0.49, p = 0.0001), hip circumference (r = -0.54, p = 0.0001), neck circumference (r = -0.41, p = 0.0001), and cognitive scores. The waist-hip ratio did not show a significant correlation with cognitive scores. Conclusion:This study demonstrates a significant association between obesity and cognitive impairment in middle-aged women. Reducing excess weight through interventions such as diet and physical activity may improve cognitive function and reduce morbidity and mortality. Early intervention is crucial for optimal benefits
Image compression is to reduce redundancy of the image data in order to store or transmit data in an efficient form.Compression is carried out for the following reasons about reduce, the storage requirement, processing time and transmission duration.The most powerful and quantization technique used for the image compression is vector quantization (VQ).The Existing methods Linde-Buzo-Gray (LBG) and Fast Back Propagation (FBP) algorithm are presented.In existing methods, the compression ratio is decreased.The proposed method adaptive vector quantization is used to analyze for image vector quantization (VQ).The performance of proposed work is analyzed using the factors SNR, MSE, PSNR and CR.The experimental work using MatLab shows that the proposed scheme is efficient and produced expected result.
Face recognition is used to identity a person effectively and most effective physiological biometric trait. In this paper, we propose sorting pixels-based face recognition using Discrete Wavelet Transform (DWT) and statistical features. The novel concept of sorting pixel values in ascending order is introduced and segmented into two parts viz., Low Pixel Values (LPV) and High Pixel Values (HPV). The DWT is applied on LPV matrix to generate low and high frequency bands such as LL, LH, HL and HH. The low frequency LL band is considered for features as the coefficient values are enhanced compared to original image pixel values and also reduction in dimensionality. The statistical measure is applied on HPV to compute mean, median, mode, maximum and standard deviation features. The features of LL band and statistical features are concatenated to obtain final features. The Artificial Neural Network (ANN) is used as classifier to recognize human beings. It is perceived that the performance of the proposed method is enhanced compared with the existing methods.
Today Micro-blogging has become a popular Internet-user communication tool. Millions of users exchange views on different aspects of their lives. Thus micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Due to the recent emergence of micro blogging, there are a few research works devoted to this subject. We concentrate in our paper on Twitter, one of the prominent micro blogging sites to analyze sentiment of the public. We'll demonstrate, how to gather real-time twitter data for sentiment analysis or opinion mining purposes, and employed algorithms like Term Frequency - Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes ( MNB). We are able to determine positive and negative sentiments for the real-time twitter data using the above chosen algorithms. Experimental evaluations below shows that the algorithms used are efficient and it can be used as a application in detection of the depression of the people. We worked with English in this article, but for any other language it can be used.
Face recognition (FR) is getting a lot of attention for a good reason in the field of research and making a big impact in areas such as computer vision and human-computer interaction. This paper proposes a FR model based on the windowing technique using discrete cosine transform (DCT), average covariance and artificial neural network (ANN). The windowing technique is used to divide the whole image into 4 × 4, 8 × 8 and 16 × 16 size of windows. The DCT is applied to each window to acquire DCT coefficients. The average covariance is calculated for the obtained DCT coefficient matrix. The calculation of an average covariance decreases the original size of the image by around 97%. The network is created, trained and tested to assess the performance of the network using nine standard face databases. Experimental results indicate that the proposed model achieves a higher recognition rate with a reduced number of features and computational intricacy compared with conventional methods.
The field of Face Recognition (FR) is still a thought-provoking problem, while in recent advances of Artificial Neural Networks (ANN) has shown improved performance in FR rate. In this paper, we propose face recognition based on windowing technique using Discrete Cosine Transform (DCT), average covariance and ANN. The novel concept of windowing technique is used to divide each image to 4x4,8X8 and 16X16 size of windows. The DCT is applied on each window to obtain DCT co-efficients. The covariance matrix is computed on each DCT coefficient matrix and average value of each block is also computed to obtain final feature value. The computation of an average covariance reduces the original size of face image by around 97% i.e., the number of co-efficients in the final feature set is only around 3% of the original size of an image. The proposed method is very efficient in identifying with very less number of features. Network is created and trained the input dataset and target dataset to reach the desired output. The trained net is then tested to compute performance parameters of the network. The experiments are conducted on some popularly used face databases to illuminate the performance and the efficiency of the proposed algorithm. The experimental results are tabulated and are compared with the existing methods. It is observed that, the proposed model achieves better recognition accuracy for 16X16 windowing and also with existing algorithms.
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