Food adulteration poses significant health risks globally and is rigorously monitored by safety authorities. In developing nations, where milk is highly prone to contamination (with Brazil, India, China, and Pakistan producing half of the world’s milk), stringent detection and classification techniques are essential. This study employs both destructive and non-destructive methods for milk adulteration analysis. The destructive method uses Lactoscan for comprehensive qualitative measurements, including temperature, pH, conductivity, solids, protein, density, fat content, and SNF. The non-destructive method utilizes hyperspectral imaging (HSI) with the Specim Fx-10 (397–1003 nm) for image-based analysis, involving preprocessing steps like image scaling, ROI selection, radiometric correction, and spectral reflectance extraction using the empirical line method (ELM). Advanced deep learning models, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU), are employed to predict and classify pure and adulterated milk spectra. CNNs showed superior performance in identifying adulteration trends. The proposed pipeline, validated with a 97% accuracy, outperforms state-of-the-art techniques based on metrics such as Kappa, accuracy, precision, recall, F1-score, MCC, and Jaccard Index.

The detection of lungs nodule cancer by Computer Aided Diagnosis (CAD) system, provide a great support to the medical experts and can lower the death rate. In this research, CAD system is established for early detection of lungs nodule cancer. First, the contrast of an input image is enhanced by applying Contrast Limited Adaptive Histogram Equalization (CLAHE). The Otsu thresholding is applied for segmentation of lungs tumor followed by morphological filters to remove background and other geometrical objects. The resultant image is de-noised by applying Discrete Wavelet Transform (DWT). Gray Level Co-occurrence Matrix (GLCM) is used for features extraction such as correlation, energy, etc. Principle Component Analysis (PCA) is employed for feature selection. Finally, Support Vector Machine (SVM) is used to classify the image into benign (non-cancerous) or malignant (cancerous). The performance parameters such as accuracy, sensitivity, specificity, Peak Signal to Noise Ratio (PSNR), Root Mean Square Errors (RMSE) and Area under the Curve are used for the evaluation of the proposed method. The proposed system is verified on the LIDC dataset. The visual and parametric results of the proposed method are computed and compared with other existing methods.

The innovation of e-metering (Electronic Metering) has experienced fast mechanical progressions and there is expanded interest in a solid and effective Automatic Meter Reading (AMR) framework. GSM Based shrewd vitality meter perusing framework replaces conventional meter perusing techniques. It empowers remote access to the existing vitality meter by the vitality provider. A GSM-based remote correspondence module is incorporated with the electronic vitality meter of every element to have remote access to the utilization of power. A PC with a GSM recipient at the opposite end, which contains the database goes about as the charging point. Live meter perusing from the GSM-empowered vitality meter is sent back to this charging point intermittently and these subtle elements are refreshed in a focal database. The total month-to-month utilization and the due bill are informed back to the client after handling this information. So, GSM-based remote AMR framework is a more successful approach for a traditional charging framework. This framework additionally gives specialists to power organizations to take activities against tolerant clients who have a remarkable contribution; generally, the organization has the ideal to detach the power supply, and it can reconnect the control supply after the affidavit of duty. So, we thought about building such an automatic system. This research is GSM-Based on a smart energy meter reading system to eliminate the conventional way of the reading system. In this paper, the GSM module sends reading information through SMS to the related Authority. There are no chances of any unethical mistake by using this modern technique.

Food contamination remains a serious global concern due to its health risks, with milk being one of the most commonly adulterated foods in developing countries such as Pakistan, India, and Bangladesh. Accurate detection of milk contamination is essential for ensuring consumer safety and maintaining food industry standards. This study explores both invasive and noninvasive approaches for contamination analysis. The invasive method uses the Lactoscan system to assess parameters such as fat, conductivity, protein, density, solids, lactose, temperature, pH, and SNF across varying contamination levels. The noninvasive method employs hyperspectral imaging using the Specim FX-10 system (400-1,000 nm) to detect contamination through spectral and spatial analysis. Preprocessing involved image resizing and region of interest selection for feature extraction, as well as radiometric correction using the empirical line method. Postprocessing included noise reduction and spectral smoothing using the Savitzky-Golay filter. The resulting clean spectral data were classified using a hybrid ensemble learning (HEL) framework, which combines voting and stacking ensembles of gradient boosting, XGBoost, LightGBM, and multilayer perceptron models. Comparative results show the HEL approach significantly outperforms existing methods, achieving 100% training and 96% validation accuracy-demonstrating its potential for real-time, noninvasive milk quality assurance.