Machine learning based model for detecting depression during Covid-19 crisis.

Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression. Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them.

Minimum Latency-Secure Key Transmission for Cloud-Based Internet of Vehicles Using Reinforcement Learning.

The Internet of Vehicles (IoV) communication key management level controls the confidentiality and security of its data, which may withstand user identity-based attacks such as electronic spoofing. The IoV group's key is updated with a defined frequency under the current key management method, which lengthens the time between crucial changes and encryption. The cluster key distribution management is used as the study object in this paper, which is based on the communication security on the Internet of Vehicles cluster. When vehicles enter and exit the cluster, the Internet of Vehicles must update the group key in real-time to ensure its forward and backward security. A low-latency IoV group key distribution management technology based on reinforcement learning is proposed to optimize the group owner vehicle according to factors such as changes in the number of surrounding vehicles and essential update records and the update frequency and the key length of its group key. The technology does not require the group leader vehicle to predict the nearby traffic flow model. The access-driven cache attack model reduces the delay of encryption and decryption and is verified in the simulation of the IoV based on advanced encryption standards. The simulation results show that, compared with the benchmark group key management scheme, this technology reduces the transmission delay of key updates, the calculation delay of encryption and decryption of the IoV, and improves the group key confidentiality.

Lung Cancer Classification and Prediction Using Machine Learning and Image Processing.

Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K-means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.

Groundwater Suitability Evaluation Using Entropy Weightage Quality Index (EWQI) Model and Human Health Cancer Risk Assessment of Heavy Metal in Eastern India.

This study evaluated the groundwater using the Entropy Weightage Quality Index model (EWQI). Eighteen samples were taken from the different wellbores during premonsoon seasons in 2021. The present study is aimed at developing a comprehensive approach for groundwater quality assessment and associated health risk along with the cancer risk due to the presence of heavy metals. The water quality of Ranchi city was found to be better except in the western zone. Principal component analysis (PCA) revealed that arsenic (As) was the most influencing element that deteriorated the potability of water which supports our study. The study looked at cancer and noncancer health hazards connected with heavy metal music. The value of hazardous quotient (HQ) was observed to be relatively higher in As (HQ > 1) and Ni, followed by Mn > Fe > Zn > Cu. Also, the children were at higher risk than adults. The cancer risk associated with arsenic was investigated and found that the northern part and southeast-west (lapung block) of the study are at higher risk. Prolonged ingestion of As causes diseases like arsenicosis that leads to enhanced chances of cancer risk. This research provides an immense research database to assess the potability of drinking water in a similar city like Ranchi.

Real-Time Assessment of Mandarin Crop Water Stress Index.

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80−100% FC water treatment as full field capacity and three deficit irrigation treatments at 70−75% FC, 60−65% FC, and 50−55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC's color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.