Wireless sensor networks (WSNs) due to their deployment in open and unprotected environments become suspected to attacks. Most of the resource exhaustion occurs as a result of attacking the data flow control thus creating challenges for the security of WSNs. An Anomaly Detection System (ADS) framework inspired from the Human Immune System is implemented in this paper for detecting Sybil attacks in WSNs. This paper implemented an improved, decentralized, and customized version of the Negative Selection Algorithm (NSA) for data flow anomaly detection with learning capability. The use of -contiguous bit matching, which is a light-weighted bit matching technique, has reduced holes in the detection coverage. This paper compares the Sybil attack detection performance with three algorithms in terms of false negative, false positive, and detection rates. The higher detection, and lower false positive and false negative rates of the implemented technique due to the -contiguous bit matching technique used in NSA improve the performance of the proposed framework. The work has been tested in Omnet++ against Sybil attacks for WSNs.

<b><i>Introduction:</i></b> Globally, 300 million adults have clinical obesity. Heightened adiposity and inadequate musculature secondary to obesity alter bipedal stance and gait, diminish musculoskeletal tissue quality, and compromise neuromuscular feedback; these physiological changes alter stability and increase injury risk from falls. Studies in the field focus on obese patients across a broad range of body mass indices (BMI &#x3e;30 kg/m²) but without isolating the most morbidly obese subset (BMI ≥40 kg/m²). We investigated the impact of obesity in perturbing postural stability in morbidly obese subjects elected for bariatric intervention, harboring a higher-spectrum BMI. <b><i>Subjects and Methods:</i></b> Traditional force plate measurements and stabilograms are gold standards employed when measuring center of pressure (COP) and postural sway. To quantify the extent of postural instability in subjects with obesity before bariatric surgery, we assessed 17 obese subjects with an average BMI of 40 kg/m² in contrast to 13 nonobese subjects with an average BMI of 30 kg/m². COP and postural sway were measured from static and dynamic tasks. Involuntary movements were measured when patients performed static stances, with eyes either opened or closed. Two additional voluntary movements were measured when subjects performed dynamic, upper torso tasks with eyes opened. <b><i>Results:</i></b> Mean body weight was 85% (<i>p</i> &#x3c; 0.001) greater in obese than nonobese subjects. Following static balance assessments, we observed greater sway displacement in the anteroposterior (AP) direction in obese subjects with eyes open (87%, <i>p</i> &#x3c; 0.002) and eyes closed (76%, <i>p</i> = 0.04) versus nonobese subjects. Obese subjects also exhibited a higher COP velocity in static tests when subjects’ eyes were open (47%, <i>p</i> = 0.04). Dynamic tests demonstrated no differences between groups in sway displacement in either direction; however, COP velocity in the mediolateral (ML) direction was reduced (31%, <i>p</i> &#x3c; 0.02) in obese subjects while voluntarily swaying in the AP direction, but increased in the same cohort when swaying in the ML direction (40%, <i>p</i> &#x3c; 0.04). <b><i>Discussion and Conclusion:</i></b> Importantly, these data highlight obesity’s contribution towards increased postural instability. Obese subjects exhibited greater COP displacement at higher AP velocities versus nonobese subjects, suggesting that clinically obese individuals show greater instability than nonobese subjects. Identifying factors contributory to instability could encourage patient-specific physical therapies and presurgical measures to mitigate instability and monitor postsurgical balance improvements.

In recent years, the demand for alternative medical diagnostics of the human kidney or renal is growing, and some of the reasons behind this relate to its non-invasive, early, real-time, and pain-free mechanism. The chronic kidney problem is one of the major kidney problems, which require an early-stage diagnosis. Therefore, in this work, we have proposed and developed an Intelligent Iris-based Chronic Kidney Identification System (ICKIS). The ICKIS takes an image of human iris as input and on the basis of iridology a deep neural network model on a GPU-based supercomputing machine is applied. The deep neural network models are trained while using 2000 subjects that have healthy and chronic kidney problems. While testing the proposed ICKIS on 2000 separate subjects (1000 healthy and 1000 chronic kidney problems), the system achieves iris-based chronic kidney assessment with an accuracy of 96.8%. In the future, we will work to improve our AI algorithm and try data-set cleaning, so that accuracy can be increased by more efficiently learning the features.

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Human lungs are essential respiratory organs. Different Obstructive Lung Diseases (OLD) such as bronchitis, asthma, lungs cancer etc. affects the respiration. Diagnosing OLD in the initial stage is better than diagnosing and curing them later. The delay in diagnosing OLD is due to expensive diagnosing tool and experts requirement. Therefore, a non-invasive diagnosing tool for OLD is required that identifies dysfunctional lungs without the support of expert, complex and expensive diagnosing types of equipment. In this work, we design an Iris based Lungs Prediagnostic System (ILPS). The ILPS takes iris images as input and identifies dysfunctional Lungs based on iridology map. While testing with 50 lungs patients, the results confirm that the ILPS identifies dysfunctional lungs patients with an accuracy of 88%.